2007•Computer Engineering and Applications JournalRequires access

Improving Particle Swarm Optimization by keeping particles activity

Shuai Xiaoying

Open publisher page 4 citations

Abstract

To overcome the problem of premature convergence on Particle Swarm Optimization(PSO),this paper proposes an Improved Particle Swarm Optimization(IPSO) called keeping particles active PSO,which is guaranteed to keep the diversity of the particle swarm.When particles lose activity,this paper uses a special mutation or perturbation to activate particles and to make particles explore the search space more efficiently.Four Benchmark functions are selected as the test functions.The experimental results show that the IPSO can not only significantly speed up the convergence,but also effectively solve the premature convergence problem.

About this research paper

What this paper is about

To overcome the problem of premature convergence on Particle Swarm Optimization(PSO),this paper proposes an Improved Particle Swarm Optimization(IPSO) called keeping particles active PSO,which is guaranteed to keep the diversity of the particle swarm.When particles lose activity,this paper uses a special mutation or perturbation to activate particles and to make particles explore the search space more efficiently.Four Benchmark functions are selected as the test functions.The experimental results show that the IPSO can not only significantly speed up the convergence,but also effectively solve the premature convergence problem.

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

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Available abstract

To overcome the problem of premature convergence on Particle Swarm Optimization(PSO),this paper proposes an Improved Particle Swarm Optimization(IPSO) called keeping particles active PSO,which is guaranteed to keep the diversity of the particle swarm.When particles lose activity,this paper uses a special mutation or perturbation to activate particles and to make particles explore the search space more efficiently.Four Benchmark functions are selected as the test functions.The experimental results show that the IPSO can not only significantly speed up the convergence,but also effectively solve the premature convergence problem.

Key concepts: Particle swarm optimization, Premature convergence, Multi-swarm optimization, Benchmark (surveying), Convergence (economics), Mathematical optimization, Swarm behaviour, Computer science

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