2005Journal of Changchun Post and Telecommunication InstituteRequires access

Application of Particle Swarm Optimization for Solving Optimization Problems

Ming Ma

Open publisher page 2 citations

Abstract

PSO (Particle Swarm Optimization)is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. To avoids the local minimum problems and to improve convergent speed, a new probability of PSO algorithm was proposed. Different solving methods and test functions have been designed for unconstrained and constrained optimization problems, and to do research for solving multi objective optimization problems with PSO. Numerical experiments have shown the feasibility and effectiveness of the proposed algorithm.

About this research paper

What this paper is about

PSO (Particle Swarm Optimization)is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. To avoids the local minimum problems and to improve convergent speed, a new probability of PSO algorithm was proposed. Different solving methods and test functions have been designed for unconstrained and constrained optimization problems, and to do research for solving multi objective optimization problems with PSO. Numerical experiments have shown the feasibility and effectiveness of the proposed algorithm.

Why it matters

OpenAlex reports 2 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

PSO (Particle Swarm Optimization)is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. To avoids the local minimum problems and to improve convergent speed, a new probability of PSO algorithm was proposed. Different solving methods and test functions have been designed for unconstrained and constrained optimization problems, and to do research for solving multi objective optimization problems with PSO. Numerical experiments have shown the feasibility and effectiveness of the proposed algorithm.

Key concepts: Multi-swarm optimization, Particle swarm optimization, Metaheuristic, Imperialist competitive algorithm, Mathematical optimization, Derivative-free optimization, Meta-optimization, Computer science

Related papers

Back to paper searchBrowse research topicsOriginal source
Application of Particle Swarm Optimization for Solving Optimization Problems — Research Paper | ScholarLens