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2007•Journal of Zhengzhou UniversityRequires access

An Improved Particle Swarm Optimization Algorithm

Bei Yang

Open publisher page 19 citations

Abstract

Particle swarm optimization(PSO) is a new evolutionary computation method,which has been successfully applied to many fields.But the standard particle swarm optimization is used resulting in premature convergence.An improved particle swarm optimization is presented.Using differential evolution strategy,it can make the solution jump out of the local minimum point.The experimental results of classic functions show that the improved PSO is efficient and feasible.

About this research paper

What this paper is about

Particle swarm optimization(PSO) is a new evolutionary computation method,which has been successfully applied to many fields.But the standard particle swarm optimization is used resulting in premature convergence.An improved particle swarm optimization is presented.Using differential evolution strategy,it can make the solution jump out of the local minimum point.The experimental results of classic functions show that the improved PSO is efficient and feasible.

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OpenAlex reports 19 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Particle swarm optimization(PSO) is a new evolutionary computation method,which has been successfully applied to many fields.But the standard particle swarm optimization is used resulting in premature convergence.An improved particle swarm optimization is presented.Using differential evolution strategy,it can make the solution jump out of the local minimum point.The experimental results of classic functions show that the improved PSO is efficient and feasible.

Key concepts: Particle swarm optimization, Multi-swarm optimization, Metaheuristic, Mathematical optimization, Jump, Premature convergence, Convergence (economics), Swarm behaviour

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