2008Unpublished venueRequires access

The Culture-Based Particle Swarm Optimization Algorithm

Yun Huang, Yufa Xu, Guochu Chen

Open publisher page 4 citations

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.

About this research paper

What this paper is about

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.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available 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.

Key concepts: Multi-swarm optimization, Particle swarm optimization, Meta-optimization, Metaheuristic, Imperialist competitive algorithm, Mathematical optimization, Derivative-free optimization, Test functions for optimization

Related papers

Back to paper searchBrowse research topicsOriginal source
The Culture-Based Particle Swarm Optimization Algorithm — Research Paper | ScholarLens