A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: Multi-objective optimization , Particle swarm optimization , Submerged arc furnace , Power optimization , Supply voltage unbalances
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/390077 , http://hdl.handle.net/10210/20559 , uj:16112 , Citation: Sun, Y. & Wang, Z. 2015. A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace. Proceedings of the Sixth International Conference on Swarm Intelligence (ICSI 2015), Beijing, China, 25-29, June 2015.
- Description: Abstract: To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; minimize the supply voltage unbalances to reduce the side effect of the power grids. In this paper, maximizing the power input and minimum the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are focused on. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is pro-posed, which can be used to optimize the NN model of the three-phase SAF. The optimization results showed the efficiency of the proposed method.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: Multi-objective optimization , Particle swarm optimization , Submerged arc furnace , Power optimization , Supply voltage unbalances
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/390077 , http://hdl.handle.net/10210/20559 , uj:16112 , Citation: Sun, Y. & Wang, Z. 2015. A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace. Proceedings of the Sixth International Conference on Swarm Intelligence (ICSI 2015), Beijing, China, 25-29, June 2015.
- Description: Abstract: To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; minimize the supply voltage unbalances to reduce the side effect of the power grids. In this paper, maximizing the power input and minimum the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are focused on. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is pro-posed, which can be used to optimize the NN model of the three-phase SAF. The optimization results showed the efficiency of the proposed method.
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Fuzzy logic system for intermixed biogas and photovoltaics measurement and control
- Matindife, Liston, Wang, Zenghui, Sun, Yanxia
- Authors: Matindife, Liston , Wang, Zenghui , Sun, Yanxia
- Date: 2018
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/274909 , uj:29351 , Citation: Matindife, L., Wang, Z. & Sun, Y. 2018. Fuzzy logic system for intermixed biogas and photovoltaics measurement and control. Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 5412062, 18 pages https://doi.org/10.1155/2018/5412062.
- Description: Abstract: This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods.The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design.The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation.
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- Authors: Matindife, Liston , Wang, Zenghui , Sun, Yanxia
- Date: 2018
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/274909 , uj:29351 , Citation: Matindife, L., Wang, Z. & Sun, Y. 2018. Fuzzy logic system for intermixed biogas and photovoltaics measurement and control. Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 5412062, 18 pages https://doi.org/10.1155/2018/5412062.
- Description: Abstract: This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods.The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design.The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation.
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Fully connected multi-objective particle swarm optimizer based on neural network
- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Neural network , Pareto front , Non domination
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22087 , uj:16157 , Citation: Wang, Z. & Sun, Y. 2011. Fully connected multi-objective particle swarm optimizer based on neural network. Lecture notes in computer science 6838:170-177.
- Description: Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance. , Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011.
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- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Neural network , Pareto front , Non domination
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22087 , uj:16157 , Citation: Wang, Z. & Sun, Y. 2011. Fully connected multi-objective particle swarm optimizer based on neural network. Lecture notes in computer science 6838:170-177.
- Description: Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance. , Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011.
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Chaotic particle swarm optimization
- Sun, Yanxia, Qi, Guoyuan, Wang, Zenghui, Van Wyk, Barend Jacobus, Hamam, Yskandar
- Authors: Sun, Yanxia , Qi, Guoyuan , Wang, Zenghui , Van Wyk, Barend Jacobus , Hamam, Yskandar
- Date: 2009
- Subjects: Chaos , Particle swarm optimization , Neural network , Convergence
- Language: English
- Identifier: http://hdl.handle.net/10210/22298 , uj:16185 , Citation: Sun, Y. et al. 2009. Chaotic particle swarm optimization. 2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit), Shanghai, China, June 12-14, 2009. p. 505-510. ISBN:16-05-58326-X.
- Description: Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hop¯eld neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence.
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- Authors: Sun, Yanxia , Qi, Guoyuan , Wang, Zenghui , Van Wyk, Barend Jacobus , Hamam, Yskandar
- Date: 2009
- Subjects: Chaos , Particle swarm optimization , Neural network , Convergence
- Language: English
- Identifier: http://hdl.handle.net/10210/22298 , uj:16185 , Citation: Sun, Y. et al. 2009. Chaotic particle swarm optimization. 2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit), Shanghai, China, June 12-14, 2009. p. 505-510. ISBN:16-05-58326-X.
- Description: Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hop¯eld neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence.
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Adaptive optimal digital PID controller
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: PID controller , Adaptive control , Parameter tuning , Particle swarm optimization
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21202 , uj:16123 , Citation: Sun, Y. & Wang, Z. 2015. Adaptive optimal digital PID controller. Applied mechanics and materials 789-790:1021-1026.
- Description: Abstract: It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller. , Originally presented at 2014 International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2014), Beijing, October 24-26, 2014.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: PID controller , Adaptive control , Parameter tuning , Particle swarm optimization
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21202 , uj:16123 , Citation: Sun, Y. & Wang, Z. 2015. Adaptive optimal digital PID controller. Applied mechanics and materials 789-790:1021-1026.
- Description: Abstract: It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller. , Originally presented at 2014 International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2014), Beijing, October 24-26, 2014.
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Improved genetic algorithm based on PSO-inspired reference point placement
- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Non-dominated sorting genetic algorithm , Reference points , Optimization
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/395582 , uj:32807 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2019. Improved genetic algorithm based on PSO-inspired reference point placement.
- Description: Abstract: This paper investigates the performance of the Non-dominated Sorting Genetic Algorithm (NSGA) based on the placement of reference points in the objective function space. An improved version of NSGA is proposed and its performance is analysed for five and eight reference points respectively in the multi-objective function space. The reference points are arranged as two effective swarm topologies: wheel and Von Neumann topology, which have been widely used in Particle Swarm Optimization (PSO). Through the simulations, the wheel topology (called wheel reference point genetic algorithm (wRPGA)) based method achieves better performance than the one which is based on the Von Neumann topology. The wheel topology also achieves better performance with respect to IGD compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 CEC 2017 benchmark problems. Moreover, wRPGA gives a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid.
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- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Non-dominated sorting genetic algorithm , Reference points , Optimization
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/395582 , uj:32807 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2019. Improved genetic algorithm based on PSO-inspired reference point placement.
- Description: Abstract: This paper investigates the performance of the Non-dominated Sorting Genetic Algorithm (NSGA) based on the placement of reference points in the objective function space. An improved version of NSGA is proposed and its performance is analysed for five and eight reference points respectively in the multi-objective function space. The reference points are arranged as two effective swarm topologies: wheel and Von Neumann topology, which have been widely used in Particle Swarm Optimization (PSO). Through the simulations, the wheel topology (called wheel reference point genetic algorithm (wRPGA)) based method achieves better performance than the one which is based on the Von Neumann topology. The wheel topology also achieves better performance with respect to IGD compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 CEC 2017 benchmark problems. Moreover, wRPGA gives a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid.
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Prediction performance of improved decision tree based algorithms : a review
- Mienyea, Domor Ibomoiye, Sun, Yanxia, Wang, Zenghui
- Authors: Mienyea, Domor Ibomoiye , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Machine learning, Data mining, Algorithm
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/397330 , uj:33022 , Mienyea, D.I. et al. 2019. Prediction performance of improved decision tree based algorithms : a review
- Description: Abstract : Applications of machine learning can be found in retail, banking, education, health sectors etc. To process the large data emanating from the various sectors, researchers are developing different algorithms using expertise from several fields and knowledge of existing algorithms. Machine learning decision tree algorithms which includes ID3, C4.5, C5.0, and CART (Classification and Regression Trees) are quite powerful. ID3 and C4.5 are mostly used in classification problems, and they are the focus of this research. C4.5 is an improved version of ID3 developed by Ross Quinlan. The prediction performance of these algorithms is very important. In this paper, the prediction performance of decision tree algorithms will be studied, an in-depth review will be conducted on relevant researches that attempted to improve the performance of the algorithms and the various methods used. Comparison will also be done between the various tree based algorithms. The major contribution of this review is to provide researchers with the progress made so far, as there is no available literature that has put together relevant improvements of decision tree based algorithms, and lastly lay the foundation for future research and improvements.
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- Authors: Mienyea, Domor Ibomoiye , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Machine learning, Data mining, Algorithm
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/397330 , uj:33022 , Mienyea, D.I. et al. 2019. Prediction performance of improved decision tree based algorithms : a review
- Description: Abstract : Applications of machine learning can be found in retail, banking, education, health sectors etc. To process the large data emanating from the various sectors, researchers are developing different algorithms using expertise from several fields and knowledge of existing algorithms. Machine learning decision tree algorithms which includes ID3, C4.5, C5.0, and CART (Classification and Regression Trees) are quite powerful. ID3 and C4.5 are mostly used in classification problems, and they are the focus of this research. C4.5 is an improved version of ID3 developed by Ross Quinlan. The prediction performance of these algorithms is very important. In this paper, the prediction performance of decision tree algorithms will be studied, an in-depth review will be conducted on relevant researches that attempted to improve the performance of the algorithms and the various methods used. Comparison will also be done between the various tree based algorithms. The major contribution of this review is to provide researchers with the progress made so far, as there is no available literature that has put together relevant improvements of decision tree based algorithms, and lastly lay the foundation for future research and improvements.
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A new golden ratio local search based particle swarm optimization
- Sun, Yanxia, Van Wyk, Barend Jacobus, Wang, Zenghui
- Authors: Sun, Yanxia , Van Wyk, Barend Jacobus , Wang, Zenghui
- Date: 2012
- Subjects: Particle swarm optimization , Golden ratio , Local search
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21329 , uj:16141 , Citation: Sun, Y., Van Wyk, B.J. & Wang, Z. 2012. A new golden ratio local search based particle swarm optimization. 2012 International Conference on Systems and Informatics (ICSAI 2012), Yantai, China 19-20 May, 2012. p. 754-757. ISBN: 978-1-4673-0198-5
- Description: Abstract: At beginning of the search process of particle swarm optimization, one of the disadvantages is that PSO focuses on the global search while the local search is weaken. However, at the end of the search procedure, the PSO focuses on the local search as all most all the particles converge to small areas which may make the particle swarm trapped in the local minima if no particle find position near the minima at the beginning of search procedure. To improve the optimization performance, the local search is necessary for particle swarm optimization. In this paper, golden ratio is used to determine the size of the search area. Only two positions need to be checked to find whether there are local positions with lower fitness value around a certain particle position. This method is easy to use. It is also tested using several famous benchmarks with high dimensions and big search space to the efficiency of the proposed method.
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- Authors: Sun, Yanxia , Van Wyk, Barend Jacobus , Wang, Zenghui
- Date: 2012
- Subjects: Particle swarm optimization , Golden ratio , Local search
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21329 , uj:16141 , Citation: Sun, Y., Van Wyk, B.J. & Wang, Z. 2012. A new golden ratio local search based particle swarm optimization. 2012 International Conference on Systems and Informatics (ICSAI 2012), Yantai, China 19-20 May, 2012. p. 754-757. ISBN: 978-1-4673-0198-5
- Description: Abstract: At beginning of the search process of particle swarm optimization, one of the disadvantages is that PSO focuses on the global search while the local search is weaken. However, at the end of the search procedure, the PSO focuses on the local search as all most all the particles converge to small areas which may make the particle swarm trapped in the local minima if no particle find position near the minima at the beginning of search procedure. To improve the optimization performance, the local search is necessary for particle swarm optimization. In this paper, golden ratio is used to determine the size of the search area. Only two positions need to be checked to find whether there are local positions with lower fitness value around a certain particle position. This method is easy to use. It is also tested using several famous benchmarks with high dimensions and big search space to the efficiency of the proposed method.
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Analysis of a fractional order nonlinear system based on the frequency domain approximation
- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2014
- Subjects: Nonlinear system , Fractional order system , Chaos , Hyperchaos
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17800 , uj:15926 , Doi: 10.14355/ijepr.2014.0304.02 , Citation: Wang, Z., Sun, Y. 2014. Analysis of a fractional order nonlinear system based on the frequency domain approximation. International Journal of Engineering Practical Research (IJEPR), 3(4): 74-77.
- Description: Abstract: The dynamics of nonlinear system is very complicated especially the fractional nonlinear system since they can be found in many areas of engineering and science. The dynamics of the Lorenz system with fractional derivatives is analysed based on the frequency approximation. For a given range of parameters where the non‐fractional Lorenz system has periodic orbits, it is found that the fractional Lorenz system exhibits chaos and hyperchaos. A striking finding is that the fractional Lorenz system exhibits hyperchaos, although the total system order is less than 3, which is contrary to the well known conclusion that hyperchaos cannot occur in the integer‐order continuous‐time autonomous system of order less than 4. Finally, a reasonable explanation is offered for this complicated dynamical phenomenon.
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- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2014
- Subjects: Nonlinear system , Fractional order system , Chaos , Hyperchaos
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17800 , uj:15926 , Doi: 10.14355/ijepr.2014.0304.02 , Citation: Wang, Z., Sun, Y. 2014. Analysis of a fractional order nonlinear system based on the frequency domain approximation. International Journal of Engineering Practical Research (IJEPR), 3(4): 74-77.
- Description: Abstract: The dynamics of nonlinear system is very complicated especially the fractional nonlinear system since they can be found in many areas of engineering and science. The dynamics of the Lorenz system with fractional derivatives is analysed based on the frequency approximation. For a given range of parameters where the non‐fractional Lorenz system has periodic orbits, it is found that the fractional Lorenz system exhibits chaos and hyperchaos. A striking finding is that the fractional Lorenz system exhibits hyperchaos, although the total system order is less than 3, which is contrary to the well known conclusion that hyperchaos cannot occur in the integer‐order continuous‐time autonomous system of order less than 4. Finally, a reasonable explanation is offered for this complicated dynamical phenomenon.
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Asynchronous and stochastic dimension updating PSO and its application to parameter estimation for frequency modulated (FM) sound waves
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2016
- Subjects: Swarm optimization , Asynchronous updating , Stochastic dimension updating
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/92362 , uj:20221 , Citation: Sun, Y. & Wang, Z. 2016. Asynchronous and stochastic dimension updating PSO and its application to parameter estimation for frequency modulated (FM) sound waves.
- Description: Abstract: The particle velocity and position updating plays very important role for achieving good optimization performance for Particle Swarm Optimization (PSO). This paper analyzed the performance of asynchronously updating PSO and synchronously updating PSO by simulation and found that the asynchronously updating way can achieve better optimization performance than the synchronously updating way. Moreover, the convergence of asynchronously PSO is faster than the synchronously PSO, which means there is spare time to achieve better optimization performance based on some techniques. Here we proposed stochastic dimension updating technique which means only some dimensions of position will be updated. Several benchmark functions have been used to validate the proposed method and the proposed method is also applied to the parameter estimation for frequency modulated Sound Waves.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2016
- Subjects: Swarm optimization , Asynchronous updating , Stochastic dimension updating
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/92362 , uj:20221 , Citation: Sun, Y. & Wang, Z. 2016. Asynchronous and stochastic dimension updating PSO and its application to parameter estimation for frequency modulated (FM) sound waves.
- Description: Abstract: The particle velocity and position updating plays very important role for achieving good optimization performance for Particle Swarm Optimization (PSO). This paper analyzed the performance of asynchronously updating PSO and synchronously updating PSO by simulation and found that the asynchronously updating way can achieve better optimization performance than the synchronously updating way. Moreover, the convergence of asynchronously PSO is faster than the synchronously PSO, which means there is spare time to achieve better optimization performance based on some techniques. Here we proposed stochastic dimension updating technique which means only some dimensions of position will be updated. Several benchmark functions have been used to validate the proposed method and the proposed method is also applied to the parameter estimation for frequency modulated Sound Waves.
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Local and global search based PSO algorithm
- Sun, Yanxia, Wang, Zenghui, Van Wyk, Barend Jacobus
- Authors: Sun, Yanxia , Wang, Zenghui , Van Wyk, Barend Jacobus
- Date: 2013
- Subjects: Local search , Global search , Particle swarm optimisation
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21240 , uj:16129 , Citation: Sun,Y., Wang, Z. & Van Wyk, B. 2013. Local and global search based PSO algorithm. Lecture Notes in Computer Sciences (LNCS) 7928: 129-136. ISSN: 0302-9743
- Description: Abstract: In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching. , Originally presented at Fourth International Conference on Swarm Intelligence (ICSI 2013), Harbin, China, 12-15, June, 2013.
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- Authors: Sun, Yanxia , Wang, Zenghui , Van Wyk, Barend Jacobus
- Date: 2013
- Subjects: Local search , Global search , Particle swarm optimisation
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21240 , uj:16129 , Citation: Sun,Y., Wang, Z. & Van Wyk, B. 2013. Local and global search based PSO algorithm. Lecture Notes in Computer Sciences (LNCS) 7928: 129-136. ISSN: 0302-9743
- Description: Abstract: In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching. , Originally presented at Fourth International Conference on Swarm Intelligence (ICSI 2013), Harbin, China, 12-15, June, 2013.
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Optimized energy consumption model for smart home using improved differential evolution algorithm
- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: HEMS , Evolutionary algorithms , RES
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/383615 , http://hdl.handle.net/10210/291789 , uj:31698 , Citation: Essiet, I.O, Sun, Y. & Wang, Z. 2019. Optimized energy consumption model for smart home using improved differential evolution algorithm.
- Description: Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.
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- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: HEMS , Evolutionary algorithms , RES
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/383615 , http://hdl.handle.net/10210/291789 , uj:31698 , Citation: Essiet, I.O, Sun, Y. & Wang, Z. 2019. Optimized energy consumption model for smart home using improved differential evolution algorithm.
- Description: Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.
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Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints
- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2010
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22284 , uj:16184 , Citation: Wang, Z. & Sun, Y. 2010. Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints. 2010 Second International Conference on Computational Intelligence and Natural Computing (CINC 2010) Wuhan, China, September 13-14, 2010. p. 303-306. DOI: 10.1109/CINC.2010.5643834
- Description: Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance.
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- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2010
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22284 , uj:16184 , Citation: Wang, Z. & Sun, Y. 2010. Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints. 2010 Second International Conference on Computational Intelligence and Natural Computing (CINC 2010) Wuhan, China, September 13-14, 2010. p. 303-306. DOI: 10.1109/CINC.2010.5643834
- Description: Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance.
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A novel algorithm for optimizing the Pareto set in dynamic problem spaces
- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2018
- Subjects: Component , Formatting , Style
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274916 , uj:29352 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2018. A novel algorithm for optimizing the Pareto set in dynamic problem spaces.
- Description: Abstract: This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs a single randomly mutating time-variant archive to balance convergence and diversity in order to efficiently select the final, non-dominated Pareto set. The algorithm is tested on selected dynamic optimization benchmark functions and improvement in the performance of the single archive approach is demonstrated by improved performance metrics and overall computational time.
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- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2018
- Subjects: Component , Formatting , Style
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274916 , uj:29352 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2018. A novel algorithm for optimizing the Pareto set in dynamic problem spaces.
- Description: Abstract: This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs a single randomly mutating time-variant archive to balance convergence and diversity in order to efficiently select the final, non-dominated Pareto set. The algorithm is tested on selected dynamic optimization benchmark functions and improvement in the performance of the single archive approach is demonstrated by improved performance metrics and overall computational time.
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E-education in an open distance university
- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2014
- Subjects: E-education , Open distance university , University of South Africa , e-system
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21137 , uj:16113 , Citation: Wang, Z. & Sun, Y. 2014. E-education in an open distance university. Proceedings of International Conference on Advanced Education and Management [ICAEM2014] Beijing, China, Jan.04~Jan.05, 2014. p. 141-145. ISBN: 978-1-60595-153-9
- Description: Abstract: With the development of internet, software and the relevant techniques, the e-education becomes more and more attractive. As one of most famous open distance universities, the University of South Africa (UNISA) has partly realized e-education. This paper describes and investigates the existing e-education system of UNISA. There are many advantages, such as realizing paperless office, lower cost, high efficiency, and so on, using this e-education system for teaching and learning. Moreover, the challenges and solving methods are also studied and discussed.
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- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2014
- Subjects: E-education , Open distance university , University of South Africa , e-system
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21137 , uj:16113 , Citation: Wang, Z. & Sun, Y. 2014. E-education in an open distance university. Proceedings of International Conference on Advanced Education and Management [ICAEM2014] Beijing, China, Jan.04~Jan.05, 2014. p. 141-145. ISBN: 978-1-60595-153-9
- Description: Abstract: With the development of internet, software and the relevant techniques, the e-education becomes more and more attractive. As one of most famous open distance universities, the University of South Africa (UNISA) has partly realized e-education. This paper describes and investigates the existing e-education system of UNISA. There are many advantages, such as realizing paperless office, lower cost, high efficiency, and so on, using this e-education system for teaching and learning. Moreover, the challenges and solving methods are also studied and discussed.
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Elitism set based particle swarm optimization and its application
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2017
- Subjects: Particle swarm optimization , Statistical method , Topology structure
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/242178 , uj:24972 , Citation: Sun, Y. & Wang, Z. 2017. Elitism set based particle swarm optimization and its application.
- Description: Abstract: Topology plays an important role for Particle Swarm Optimization (PSO) to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2017
- Subjects: Particle swarm optimization , Statistical method , Topology structure
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/242178 , uj:24972 , Citation: Sun, Y. & Wang, Z. 2017. Elitism set based particle swarm optimization and its application.
- Description: Abstract: Topology plays an important role for Particle Swarm Optimization (PSO) to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.
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Adaptive sharing scheme based sub-swarm multi-objective PSO
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2017
- Subjects: Multi-objective PSO , Adaptive sharing scheme , Sub-swarm
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/247883 , uj:25750 , Citation: Sun, Y. & Wang, Z. 2017. Adaptive sharing scheme based sub-swarm multi-objective PSO.
- Description: Abstract: To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms particles dynamically search their corresponding areas which are around some points of the Pareto front set in the objective space, and the chosen points of the Pareto front set are determined based on the adaptive sharing scheme. The second sub-swarm particles search the rest objective space, and they are away from the Pareto front set, which can promote the global search ability of the method. Moreover, the core points of the first sub-swarms are dynamically determined by this new adaptive sharing scheme. Some Simulations are used to test the proposed method, and the results show that the proposed method can achieve better optimization performance comparing with some existing methods.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2017
- Subjects: Multi-objective PSO , Adaptive sharing scheme , Sub-swarm
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/247883 , uj:25750 , Citation: Sun, Y. & Wang, Z. 2017. Adaptive sharing scheme based sub-swarm multi-objective PSO.
- Description: Abstract: To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms particles dynamically search their corresponding areas which are around some points of the Pareto front set in the objective space, and the chosen points of the Pareto front set are determined based on the adaptive sharing scheme. The second sub-swarm particles search the rest objective space, and they are away from the Pareto front set, which can promote the global search ability of the method. Moreover, the core points of the first sub-swarms are dynamically determined by this new adaptive sharing scheme. Some Simulations are used to test the proposed method, and the results show that the proposed method can achieve better optimization performance comparing with some existing methods.
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A hyperchaotic system without equilibrium
- Wang, Zenghui, Cang, Shijian, Ochola, Elisha Oketch, Sun, Yanxia
- Authors: Wang, Zenghui , Cang, Shijian , Ochola, Elisha Oketch , Sun, Yanxia
- Date: 2012
- Subjects: Hyperchaos , Chaos , Lyapunov exponents , Poincare map , Equilibrium
- Type: Journal article
- Identifier: http://hdl.handle.net/10210/20529 , uj:16109 , Citation: Wang, Z. et al. 2012. A hyperchaotic system without equilibrium. Nonlinear Dynamics 69(2012):531–537. DOI: 10.1007/s11071-011-0284-z
- Description: Abstract: This article introduces a new chaotic system of 4-D autonomous ordinary differential equations, which has no equilibrium. This system shows a hyper-chaotic attractor. There is no sink in this system as there is no equilibrium. The proposed system is investigated through numerical simulations and analyses including time phase portraits, Lyapunov exponents, and Poincaré maps. There is little difference between this chaotic system and other chaotic systems with one or several equilibria shown by phase portraits, Lyapunov exponents and time series methods, but the Poincaré maps show this system is a chaotic system with more complicated dynamics. Moreover, the circuit realization is also presented.
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- Authors: Wang, Zenghui , Cang, Shijian , Ochola, Elisha Oketch , Sun, Yanxia
- Date: 2012
- Subjects: Hyperchaos , Chaos , Lyapunov exponents , Poincare map , Equilibrium
- Type: Journal article
- Identifier: http://hdl.handle.net/10210/20529 , uj:16109 , Citation: Wang, Z. et al. 2012. A hyperchaotic system without equilibrium. Nonlinear Dynamics 69(2012):531–537. DOI: 10.1007/s11071-011-0284-z
- Description: Abstract: This article introduces a new chaotic system of 4-D autonomous ordinary differential equations, which has no equilibrium. This system shows a hyper-chaotic attractor. There is no sink in this system as there is no equilibrium. The proposed system is investigated through numerical simulations and analyses including time phase portraits, Lyapunov exponents, and Poincaré maps. There is little difference between this chaotic system and other chaotic systems with one or several equilibria shown by phase portraits, Lyapunov exponents and time series methods, but the Poincaré maps show this system is a chaotic system with more complicated dynamics. Moreover, the circuit realization is also presented.
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Enhanced NSGA based on adaptive crossover rate and reference points
- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2017
- Subjects: Optimization , Inverted generational distance , Reference points
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/256932 , uj:26981 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2017. Enhanced NSGA based on adaptive crossover rate and reference points.
- Description: Abstract: The challenges of many-objective optimization are investigated; and one new algorithm, which is based on the NSGA-II, is proposed for multi-objective optimization in this paper. The reference points and an adaptable crossover rate are combined in the algorithm to improve the performance of NSGA-II. The performance of NSGA for optimizing the many objective search space is examined with and without the proposed algorithm through a constrained two-objective problem with up to 40 dimensions. Simulation results show that the proposed algorithm improves the performance of NSGA for the selected test problem in generations where a non-dominated set is not obtained by 39%.
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- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2017
- Subjects: Optimization , Inverted generational distance , Reference points
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/256932 , uj:26981 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2017. Enhanced NSGA based on adaptive crossover rate and reference points.
- Description: Abstract: The challenges of many-objective optimization are investigated; and one new algorithm, which is based on the NSGA-II, is proposed for multi-objective optimization in this paper. The reference points and an adaptable crossover rate are combined in the algorithm to improve the performance of NSGA-II. The performance of NSGA for optimizing the many objective search space is examined with and without the proposed algorithm through a constrained two-objective problem with up to 40 dimensions. Simulation results show that the proposed algorithm improves the performance of NSGA for the selected test problem in generations where a non-dominated set is not obtained by 39%.
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Analysis of the effect of parameter variation on a dynamic cost function for distributed energy resources : a DER-CAM case study
- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2018
- Subjects: Evolutionary algorithm , Pareto front , Distributed energy resource
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/274901 , uj:29350 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2018. Analysis of the effect of parameter variation on a dynamic cost function for distributed energy resources : a DER-CAM case study.
- Description: Abstract: This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function, which optimizes the allocation of distributed energy resources for a mid-rise apartment building. This is achieved by comparison of parameter optimization results for both a high- and low-level optimizer respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for 4 selected cases of DER utilization previously optimized in DER-CAM. (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the 4 selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained demonstrate the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the 4 cases considered show that a DER combination of PV panel, battery storage, heat pump and load shifting outperforms the other strategies in 70% of the total simulation runs.
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- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2018
- Subjects: Evolutionary algorithm , Pareto front , Distributed energy resource
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/274901 , uj:29350 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2018. Analysis of the effect of parameter variation on a dynamic cost function for distributed energy resources : a DER-CAM case study.
- Description: Abstract: This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function, which optimizes the allocation of distributed energy resources for a mid-rise apartment building. This is achieved by comparison of parameter optimization results for both a high- and low-level optimizer respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for 4 selected cases of DER utilization previously optimized in DER-CAM. (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the 4 selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained demonstrate the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the 4 cases considered show that a DER combination of PV panel, battery storage, heat pump and load shifting outperforms the other strategies in 70% of the total simulation runs.
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