List of Titles

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

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

- Wang, Zenghui, Sun, Yanxia

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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Adaptive optimal digital PID controller

- Sun, Yanxia, Wang, Zenghui

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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Home healthcare staff scheduling: a clustering particle swarm optimization approach

- Mutingi, Michael, Mbohwa, Charles

Authors: Mutingi, Michael , Mbohwa, Charles
Date: 2014
Subjects: Healthcare service providers , Particle swarm optimization , Healthcare staff scheduling
Type: Article
Identifier: uj:4973 , http://hdl.handle.net/10210/13074
Description: The home healthcare staff scheduling problem is concerned with the allocation of care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by the patients to improve their service quality. This paper proposes a clustering particle swam optimization methodology (CPSO) for addressing the scheduling problem. The approach utilizes the strengths of unique grouping techniques to efficiently exploit the group structure of the scheduling problem, enabling the algorithm to provide good solutions within reasonable computation times. Computational results obtained in this study demonstrate the efficiency and effectiveness of CPSO approach.
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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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A relaxed constraint method for engineering optimization problems

- Sun, Yanxia

Authors: Sun, Yanxia
Date: 2010
Subjects: Constraint nonlinear optimization , Particle swarm optimization , Relaxed constraint
Language: English
Type: Article
Identifier: http://hdl.handle.net/10210/17861 , uj:15933 , Citation: Sun, Y. et al. 2010. A relaxed constraint method for engineering optimization problems. International journal of computational science, 4(5):426-440.
Description: Abstract: Real-life problems are often subject to various constraints that limit the search space to a constrained area. Due to the complexity of constraints, a general deterministic solution is often hard to find. A relaxed constraint particle swarm optimization algorithm is proposed in this paper.
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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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A fuzzy-based particle swarm optimization algorithm for nurse scheduling

- Mutingi, Michael, Mbohwa, Charles

Authors: Mutingi, Michael , Mbohwa, Charles
Date: 2014
Subjects: Nurse scheduling , Nurse rostering , Personnel scheduling , Particle swarm optimization
Type: Article
Identifier: uj:4926 , ISSN 2078-0966 , http://hdl.handle.net/10210/13026
Description: The nurse scheduling problem (NSP) has a great impact on the quality and efficiency of health care operations. Healthcare Operations Analysts have to assign daily shifts to nurses over the planning horizon, so that operations costs are minimized, health care quality is improved, and the nursing staff is satisfied. Due to conflicting objectives and a myriad of restrictions imposed by labor laws, company requirements, and other legislative laws, the NSP is a hard problem. In this paper we present a particle swarm optimization-based algorithm that relies on a heuristic mechanism that incorporates hard constraints to improve the computational efficiency of the algorithm. Further, we incorporate soft constraints into objective function evaluation to guide the algorithm. Results from illustrative examples show that the algorithm is effective and efficient, even over large scale problems.
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A new multi-swarm multi-objective particle swarm optimization based on pareto front set

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

Authors: Sun, Yanxia , Van Wyk, Barend Jacobus , Wang, Zenghui
Date: 2011
Subjects: Multi-objective optimization , Particle swarm optimization , Multiple swarms , Pareto front
Language: English
Type: Conference proceedings
Identifier: http://hdl.handle.net/10210/22263 , uj:16181 , Citation: Sun, Y., Van Wyk, B.J. & Wang, Z. 2011. A new multi-swarm multi-objective particle swarm optimization based on pareto front set. Lecture Notes in Artificial Intelligence (LNAI) 6839:203-210. ISBN:978-3-642-25944-9
Description: Abstract: In this paper, a new multi-swarm method is proposed for multiobjective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality. , Originally presented at 2011 International Conference on Intelligent Computing, Zhengzhou, China 11-14 August, 2011.
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Dynamic small world network topology for particle swarm optimization

- Liu, Qingxue, Van Wyk, Barend Jacobus, Du, Shengzhi, Sun, Yanxia

Authors: Liu, Qingxue , Van Wyk, Barend Jacobus , Du, Shengzhi , Sun, Yanxia
Date: 2016
Subjects: Particle swarm optimization , Small world network , Dynamic neighbourhood topology
Language: English
Type: Article
Identifier: http://hdl.handle.net/10210/124064 , uj:20869 , Citation: Liu, Q. et al. 2016. Dynamic small world network topology for particle swarm optimization.
Description: Abstract: A new particle optimization algorithm with dynamic topology is proposed based on a small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the particle swarm optimization(PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more eÆective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.
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Application of improved particle swarm optimization in economic dispatch of power system

- Gninkeu Tchapda, G.Y., Wang, Z., Sun, Y.

Authors: Gninkeu Tchapda, G.Y. , Wang, Z. , Sun, Y.
Date: 2017
Subjects: Economic dispatch , Particle swarm optimization , Hybrid technique
Language: English
Type: Conference proceedings
Identifier: http://hdl.handle.net/10210/256941 , uj:26982 , Citation: Gninkeu Tchapda, G.Y., Wang, Z. & Sun, Y. 2017. Application of improved particle swarm optimization in economic dispatch of power system.
Description: Abstract: This paper introduces an improved particle swarm optimization to solve economic dispatch problems involving numerous constraints. Depending on the type of generating units, there are optimization constraints and practical operating constraints of generators such as prohibited operating zones and ramp rate limits. The algorithm is a hybrid technique made up of particle swarm optimization and bat algorithm. Particle swarm optimization as the main algorithm integrates bat algorithm in order to boost its velocity and adjust the improved solution. The new technique is firstly tested on five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. The simulation results show that it performs better than both particle swarm and bat technique.
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Niching particle swarm optimization based euclidean distance and hierarchical clustering for multimodal optimization

- Liu, Qingxue, Du, Shengzhi, Van Wyk, Barend Jacobus, Sun, Yanxia

Authors: Liu, Qingxue , Du, Shengzhi , Van Wyk, Barend Jacobus , Sun, Yanxia
Date: 2019
Subjects: Particle swarm optimization , Multimodal optimization , Niching algorithm
Language: English
Type: Article
Identifier: http://hdl.handle.net/10210/405017 , uj:33991 , Citation: Liu, Q., 2019 : Niching particle swarm optimization based euclidean distance and hierarchical clustering for multimodal optimization.
Description: Abstract : Multimodal optimization is still one of the most challenging tasks in the evolutionary computation field, when multiple global and local optima need to be effectively and efficiently located. In this paper, a niching Particle Swarm Optimization (PSO) based Euclidean Distance and Hierarchical Clustering (EDHC) for multimodal optimization is proposed. This technique first uses the Euclidean distance based PSO algorithm to perform preliminarily search. In this phase, the particles are rapidly clustered around peaks. Secondly, hierarchical clustering is applied to identify and concentrate the particles distributed around each peak to finely search as a whole. Finally, a small world network topology is adopted in each niche to improve the exploitation ability of the algorithm. At the end of this paper, the proposed EDHC-PSO algorithm is applied to the Traveling Salesman Problems (TSP) after being discretized. The experiments demonstrate that the proposed method outperforms existing niching techniques on benchmark problems, and is effective for TSP.
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Niching particle swarm optimization based euclidean distance and hierarchical clustering for multimodal optimization

- Sun, Yanxia, Liu, Qingxue, Du, Shengzhi, Van Wyk, Barend Jacobus

Authors: Sun, Yanxia , Liu, Qingxue , Du, Shengzhi , Van Wyk, Barend Jacobus
Date: 2019
Subjects: Particle swarm optimization , Multimodal optimization , Niching algorithm
Language: English
Type: Article
Identifier: http://hdl.handle.net/10210/407593 , uj:34315 , Citation: Liu, Q. et al. 2019: Niching particle swarm optimization based euclidean distance and hierarchical clustering for multimodal optimization.
Description: Abstract: Multimodal optimization is still one of the most challenging tasks in the evolutionary computation field, when multiple global and local optima need to be effectively and efficiently located. In this paper, a niching Particle Swarm Optimization (PSO) based Euclidean Distance and Hierarchical Clustering (EDHC) for multimodal optimization is proposed. This technique first uses the Euclidean distance based PSO algorithm to perform preliminarily search. In this phase, the particles are rapidly clustered around peaks. Secondly, hierarchical clustering is applied to identify and concentrate the particles distributed around each peak to finely search as a whole. Finally, a small world network topology is adopted in each niche to improve the exploitation ability of the algorithm. At the end of this paper, the proposed EDHC-PSO algorithm is applied to the Traveling Salesman Problems (TSP) after being discretized. The experiments demonstrate that the proposed method outperforms existing niching techniques on benchmark problems, and is effective for TSP.
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