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Hybrid particle swarm optimization with simulated annealing

Xihuai Wang, Junjun Li

Open publisher page 96 citations

Abstract

Particle swarm optimization is a recently invented intelligent optimizer with several highly desirable attributes. A hybrid particle swarm optimization is proposed. This method integrates the particle swarm optimization with simulated annealing. The method is applied to six test functions' optimization and the simulation shows that the performance of this algorithm is better than that of the adaptive particle swarm optimization and the genetic chaos optimization.

About this research paper

What this paper is about

Particle swarm optimization is a recently invented intelligent optimizer with several highly desirable attributes. A hybrid particle swarm optimization is proposed. This method integrates the particle swarm optimization with simulated annealing. The method is applied to six test functions' optimization and the simulation shows that the performance of this algorithm is better than that of the adaptive particle swarm optimization and the genetic chaos optimization.

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OpenAlex reports 96 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Particle swarm optimization is a recently invented intelligent optimizer with several highly desirable attributes. A hybrid particle swarm optimization is proposed. This method integrates the particle swarm optimization with simulated annealing. The method is applied to six test functions' optimization and the simulation shows that the performance of this algorithm is better than that of the adaptive particle swarm optimization and the genetic chaos optimization.

Key concepts: Multi-swarm optimization, Particle swarm optimization, Metaheuristic, Simulated annealing, Meta-optimization, Derivative-free optimization, Computer science, Mathematical optimization

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