Cooperative Asynchronous Parallel Particle Swarm Optimization for Large Dimensional Problems

Farid Bourennani
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Farid Bourennani: University of Jeddah, Jeddah, Saudi Arabia; University of Ontario Institute of Technology Oshawa, Canada

International Journal of Applied Metaheuristic Computing (IJAMC), 2019, vol. 10, issue 3, 19-38

Abstract: Metaheuristics have been very successful to solve NP-hard optimization problems. However, some problems such as big optimization problems are too expensive to be solved using classical computing. Naturally, the increasing availability of high performance computing (HPC) is an appropriate alternative to solve such complex problems. In addition, the use of HPC can lead to more accurate metaheuristics if their internal mechanisms are enhanced. Particle swarm optimization (PSO) is one of the most know metaheuristics and yet does not have many parallel versions of PSO which take advantage of HPC via algorithmic modifications. Therefore, in this article, the authors propose a cooperative asynchronous parallel PSO algorithm (CAPPSO) with a new velocity calculation that utilizes a cooperative model of sub-swarms. The asynchronous communication among the sub-swarms makes CAPPSO faster than a parallel and more accurate than the master-slave PSO (MS-PSO) when the tested big problems.

Date: 2019
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