Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review-Reference-Cited by-同舟云学术

Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review

Published:2017-03 Issue:1 Volume:25 Page:1-54
ISSN:1063-6560
Container-title:Evolutionary Computation
language:en
Short-container-title:Evolutionary Computation

Author:

Bonyadi Mohammad Reza¹,Michalewicz Zbigniew²

Affiliation:

1. Department of Computer Science, The University of Adelaide, Adelaide, SA 5005, Australia. Also with the Centre for Advanced Imaging (CAI), the University of Queensland, Brisbane, QLD 4067, Australia, and Complexica Pty Ltd, Adelaide, SA 5021, Australia. ,

2. Department of Computer Science, The University of Adelaide, Adelaide, SA 5005, Australia. Also with Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Polish-Japanese Institute of Information Technology, Warsaw, Poland, and Complexica Pty Ltd, Adelaide, SA 5021, Australia.

Abstract

This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

Publisher

MIT Press - Journals

Subject

Computational Mathematics

Link

https://www.mitpressjournals.org/doi/pdf/10.1162/EVCO_r_00180

Reference161 articles.

1. Feature Selection for Structure−Activity Correlation Using Binary Particle Swarms

2. Chaos embedded particle swarm optimization algorithms

3. Statistical mechanics of complex networks

4. Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences

5. GAVaPS-a genetic algorithm with varying population size

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