The Culture-Based Particle Swarm Optimization Algorithm
Yun Huang, Yufa Xu, Guochu Chen
Abstract
Yun Huang, Yufa Xu, Guochu Chen
Abstract
The particle swarm optimization algorithm based on the intelligent optimization algorithm. But the algorithm easily plunging into the local optimization. For this problem, a new culture-based particle swarm optimization algorithm is proposed in this paper. It constitute with the population space and the belief space. Each space has their own algorithm. Meanwhile, the two spaces communicate with each other by any communication agreement. Both CSPSO and PSO are used to resolve the optimization problems of several widely used test functions, and the results show that CBPSO enhances the global searching ability and has better optimization performance than PSO.
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The particle swarm optimization algorithm based on the intelligent optimization algorithm. But the algorithm easily plunging into the local optimization. For this problem, a new culture-based particle swarm optimization algorithm is proposed in this paper. It constitute with the population space and the belief space. Each space has their own algorithm. Meanwhile, the two spaces communicate with each other by any communication agreement. Both CSPSO and PSO are used to resolve the optimization problems of several widely used test functions, and the results show that CBPSO enhances the global searching ability and has better optimization performance than PSO.
Key concepts: Multi-swarm optimization, Particle swarm optimization, Meta-optimization, Metaheuristic, Imperialist competitive algorithm, Mathematical optimization, Derivative-free optimization, Test functions for optimization