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A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace

- Sun, Yanxia, Wang, Zenghui

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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Fully connected multi-objective particle swarm optimizer based on neural network

- Wang, Zenghui, Sun, Yanxia

Chaotic particle swarm optimization

- Sun, Yanxia, Qi, Guoyuan, Wang, Zenghui, Van Wyk, Barend Jacobus, Hamam, Yskandar

Adaptive optimal digital PID controller

- Sun, Yanxia, Wang, Zenghui

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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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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A new golden ratio local search based particle swarm optimization

- Sun, Yanxia, Van Wyk, Barend Jacobus, Wang, Zenghui

Analysis of a fractional order nonlinear system based on the frequency domain approximation

- Wang, Zenghui, Sun, Yanxia

Asynchronous and stochastic dimension updating PSO and its application to parameter estimation for frequency modulated (FM) sound waves

- Sun, Yanxia, Wang, Zenghui

Local and global search based PSO algorithm

- Sun, Yanxia, Wang, Zenghui, Van Wyk, Barend Jacobus

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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Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints

- Wang, Zenghui, Sun, Yanxia

A novel algorithm for optimizing the Pareto set in dynamic problem spaces

- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui

E-education in an open distance university

- Wang, Zenghui, Sun, Yanxia

Elitism set based particle swarm optimization and its application

- Sun, Yanxia, Wang, Zenghui

  • 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

- Sun, Yanxia, Wang, Zenghui

  • 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

Enhanced NSGA based on adaptive crossover rate and reference points

- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui

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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