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Small world network based dynamic topology for particle swarm optimization

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

Authors: Liu, Qingxue , Van Wyk, Barend Jacobus , Sun, Yanxia
Date: 2015
Subjects: Particle swarm , Small world network , Neighborhood topology , Global model , Local model
Language: English
Type: Conference proceedings
Identifier: http://hdl.handle.net/10210/22381 , uj:16196 , Citation: Liu, Q, Van Wyk, B.J. & Sun, Y. 2015. Small world network based dynamic topology for particle swarm optimization. 11th International Conference on Natural Computation (ICNC 2015). p. 289-294. ISBN: 978-1-4673-7678-5. DOI: 10.1109/ICNC.2015.7378005
Description: Abstract: A new particle optimization algorithm with dynamic topology is proposed based on ‘small world’ network. The technique imitates the dissemination of information in a ‘small world network’ by dynamically updating the neighborhood topology of particle swarm optimization. The proposed dynamic neighborhood strategy can effectively coordinate the exploration and exploitation ability of particle swarm optimization. Simulations demonstrated that convergence of the swarms is guaranteed. Experiments demonstrated that the proposed method maintained the population diversity and enhanced the global search ability.
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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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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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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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Hypothesis testing-based adaptive PSO

- Sun, Yanxia., Djouani, Karim, Van Wyk, Barend Jacobus, Wang, Zenghui, Siarry, Patrick

Authors: Sun, Yanxia. , Djouani, Karim , Van Wyk, Barend Jacobus , Wang, Zenghui , Siarry, Patrick
Date: 2014.
Subjects: Artificial intelligence. , Optimization algorithms. , Optimization and system - Network identification
Language: English
Type: Citation: Sun, Y. et al. 2014. Hypothesis testing-based adaptive PSO. Journal of Engineering, Design and Technology 12(1):89-101.
Identifier: http://hdl.handle.net/10210/16220 , uj:15752
Description: Abstract: In this paper, a new method to improve the performance of particle swarm optimization is proposed. The proposed method applies hypothesis testing to determine whether the particles trap into the local minimum or not. When the difference of means of two samples is not significant using hypothesis testing, the particles can be regarded as trapped into the local minima, the particles will be re-initialized and the global best experience is reserved. Several famous benchmarks are used to show the efficiency of the proposed technique. Moreover, optimisation results for three engineering optimisation problems with linear and nonlinear constraints demonstrate that the proposed method can effectively enhance the searching quality and stability.
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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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Seksueel-etiese aspekte in die Simsonverhaal (Rigters 13-16) as vertrekpunt vir Christelike berading binne 'n multi-godsdienstige beradingsituasie

- Van Wyk, Barend Jacobus

Authors: Van Wyk, Barend Jacobus
Date: 2008-07-07T09:11:40Z
Subjects: Samson (Biblical judge) , Ethics in the Bible , Sex religious aspects , Counseling of AIDS (Disease) patients , Judges 13-16 (Bible)
Type: Thesis
Identifier: uj:10205 , http://hdl.handle.net/10210/757
Description: This research was encouraged by the need for Christian religious-ethical principles for counselling of HIV/Aids patients and their families within a multi-religious environment. During his lifetime the researcher was a member of Professional Family Care, a multi-religious and multi-disciplinary organization assisting HIV/Aids patients in Middelburg, Mpumalanga. The aim of the study is to highlight the sexual ethos of people from a Christian ethical perspective by means of the example of the character Samson in the book of Judges (Jd 13-16), in order to derive sexual-ethical principles for counselling. The hypothesis is that an ethical relationship exists between the rebelliousness in Samson’s life, and his sexual conduct. A similar relationship can be identified in our current society as a result of the negligence of healthy religious-ethical norms. A socio-rhetorical approach has been applied to explore various textures found in the Samson saga. After a discussion of Old Testament ethics as a subject, emphasis was laid on analysing the intra-textual, ideological, social and cultural, and holiness structures of the Samson saga. HIV/Aids as a social problem is discussed, primarily by means of relevant statistics. Professional Family Care implements an eco-systemic model, viz. an integrated approach involving medical professions, social workers, and religious leaders from all the religions involved. The principles of this approach are explained. After the religious-ethical perspectives of various religions have been highlighted, final conclusions are drawn. The ethical conduct of individuals normally mirrors the dominant ideological framework of the society in which they live. The sexual-ethical conduct of Samson, within its context, and the ethical principles, which can be deduced from that for the current context of Middelburg, Mpumalanga, clearly indicate that a relationship exists between the violation of sexual-ethical norms of the society as well as the consequences thereof for individuals and the broader community. In the light thereof both the positive and negative conduct of Samson have been implemented to formulate basic principles for counselling. , Prof. Johan Coetzee
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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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A double-layer-clustering differential evolution by speciation and self-adaptive strategies 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: 2021
Subjects: Differential evolution , Multimodal optimization , Niching algorithm
Language: English
Type: Article
Identifier: http://hdl.handle.net/10210/477008 , uj:43084 , Citation: Liu, Q., Du, S., Van Wyk, B.J. & Sun, Y. 2021. A double-layer-clustering differential evolution by speciation and self-adaptive strategies for multimodal optimization. DOI: https://doi.org/10.1016/j.ins.2020.09.008
Description: Abstract: Multimodal optimization is to find and maintain as many global and local optima of a function as possible. Niching techniques based on multi-population and clustering have been proved to be effective and efficacious for tackling the multimodal optimization problems. How to enhance the diversity of the population and improve the global search ability of the algorithm to locate more optima is a big challenge. To address this objective, a Double-Layer-Clustering Differential Evolution (DE) based on Speciation (SDLCDE) and integrated it with Self-adaptive strategy (SSDLCDE) is proposed to solve the multimodal optimization problems. The first layer clustering based on speciation is used to divide the entire population into multiple subpopulations to locate the global and local optima. Then all the species seeds from each species form a subpopulation for the global search. To test the performance of our proposed algorithms, both SSDLCDE and SDLCDE are compared with 17 state-of-art niching algorithms on 29 multimodal problems with different dimensions. The Experiment results demonstrate that both the proposed algorithms outperform or perform comparably to the 17 niching algorithms on all the test functions.
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