A General Framework of Particle Swarm Optimization

Document Type

Book Chapter

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

10-13-2022

Abstract

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, we propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.

ISSN

2367-3389

Publisher

Springer International Publishing

Volume

559

First Page

307

Last Page

316

Disciplines

Computer Sciences

Keywords

Global optimization, Particle Swarm Optimization (PSO), Exploration, Exploitation

Indexed in Scopus

no

Open Access

no

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