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An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization

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journal contribution
posted on 30.03.2021, 00:18 by Jianhua Liu, Yi MeiYi Mei, Xiaodong Li
In particle swarm optimization (PSO), the inertia weight is an important parameter for controlling its search capability. There have been intensive studies of the inertia weight in continuous optimization, but little attention has been paid to the binary case. This paper comprehensively investigates the effect of the inertia weight on the performance of binary PSO (BPSO), from both theoretical and empirical perspectives. A mathematical model is proposed to analyze the behavior of BPSO, based on which several lemmas and theorems on the effect of the inertia weight are derived. Our research findings suggest that in the binary case, a smaller inertia weight enhances the exploration capability while a larger inertia weight encourages exploitation. Consequently, this paper proposes a new adaptive inertia weight scheme for BPSO. This scheme allows the search process to start first with exploration and gradually move toward exploitation by linearly increasing the inertia weight. The experimental results on 0/1 knapsack problems show that the BPSO with the new increasing inertia weight scheme performs significantly better than that with the conventional decreasing and constant inertia weight schemes. This paper verifies the efficacy of increasing inertia weight in BPSO. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

History

Preferred citation

Liu, J., Mei, Y. & Li, X. (2016). An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation, 20(5), 666-681. https://doi.org/10.1109/TEVC.2015.2503422

Journal title

IEEE Transactions on Evolutionary Computation

Volume

20

Issue

5

Publication date

01/01/2016

Pagination

666-681

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Contribution type

Article

ISSN

1089-778X

eISSN

1941-0026

Article number

5

Language

en