Genetic algorithm assisted HIDMS-PSO: a new hybrid algorithm for global optimisation

Varna, Fevzi Tugrul and Husbands, Phil (2021) Genetic algorithm assisted HIDMS-PSO: a new hybrid algorithm for global optimisation. IEEE CEC 2021, Kraków, Poland, 28th June - 1st July 2021. Published in: Proceedings of IEEE Congress on Evolutionary Computation (CEC). 1304-1311. IEEE ISBN 9781728183947

[img] PDF (© 2021 IEEE) - Accepted Version
Download (365kB)

Abstract

In this paper, a new hybrid algorithm, GA-HIDMS-PSO, is introduced by hybridising the state-of-the-art particle swarm optimisation (PSO) variant, the heterogeneous improved dynamic multi-swarm PSO (HIDMS-PSO) with a genetic algorithm (GA). The new hybrid model exploits the heterogeneous features of HIDMS-PSO and the evolutionary characteristics of the GA. In the GA-HIDMS-PSO architecture, HIDMS-PSO acts as the primary search engine, and the GA is employed as the secondary method to assist and slow down the loss of diversity for selected proportions of homogeneous and heterogeneous subpopulations of the HIDMS-PSO algorithm. Both methods run consecutively. As the primary search method, HIDMS-PSO runs for longer periods compared with the GA. The HIDMS-PSO pro-vides the initial solutions for the GA from both homogeneous and heterogeneous subpopulations and final solutions returned from the GA replace prior solutions in the HIDMS-PSO which resumes the search process with potentially more diverse particles to guide the swarm. The GA-HIDMS-PSO algorithm’s performance was tested on the 30 and 50 dimensional CEC’05 and CEC’17 test suites. The results were compared with 24 algorithms, with 12 state-of-the-art PSO variants and 12 other metaheuristics. GA-HIDMS-PSO outperformed all 24 comparison algorithms on both test suites for both 30 and 50 dimensions.

Item Type: Conference Proceedings
Additional Information: © 2021 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
Keywords: particle swarm optimisation, AI, metaheuristics
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 19 Aug 2021 07:35
Last Modified: 04 Mar 2022 17:22
URI: http://sro.sussex.ac.uk/id/eprint/101183

View download statistics for this item

📧 Request an update