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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- 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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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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- 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.
- Full Text:
- 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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