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Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

Authors:

Asral Bahari Jambek,

Siew Chin NeohAuthors Info & Claims

Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 19, Issue 12

Pages 3571 - 3580

https://doi.org/10.1007/s00500-015-1767-5

Published: 01 December 2015 Publication History

Abstract

A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The effectiveness of HMGA in Pareto optimal convergence was investigated with two performance indicators (i.e. generational distance and spacing). To measure HMGA's performance, a comparison study was conducted between HMGA and MGA. In this work, HMGA is outperformed MGA in the search for Pareto optimal front and capable of solving different difficulty of MOPs.

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Cited By

Van Thieu NMirjalili S(2023)MEALPYJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2023.102871139:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.sysarc.2023.102871
Kim SKim IYou D(2022)Multi-condition multi-objective optimization using deep reinforcement learningJournal of Computational Physics10.1016/j.jcp.2022.111263462:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.jcp.2022.111263
Tan WMohamad-Saleh J(2022)MO-NFSA for solving unconstrained multi-objective optimization problemsEngineering with Computers10.1007/s00366-020-01223-438:3(2527-2548)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00366-020-01223-4

Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

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Published In

cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 19, Issue 12

December 2015

315 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

Copyright © Copyright © 2015 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2015

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Cited By

Van Thieu NMirjalili S(2023)MEALPYJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2023.102871139:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.sysarc.2023.102871
Kim SKim IYou D(2022)Multi-condition multi-objective optimization using deep reinforcement learningJournal of Computational Physics10.1016/j.jcp.2022.111263462:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.jcp.2022.111263
Tan WMohamad-Saleh J(2022)MO-NFSA for solving unconstrained multi-objective optimization problemsEngineering with Computers10.1007/s00366-020-01223-438:3(2527-2548)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00366-020-01223-4

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