A modified indicator-based evolutionary algorithm (mIBEA)

Li, Wenwen, Özcan, Ender, John, Robert, Drake, John H., Neumann, Aneta and Wagner, Markus (2017) A modified indicator-based evolutionary algorithm (mIBEA). In: IEEE Congress on Evolutionary Computation 2017, 5-9 June 2017, Donostia-San Sebastian, Spain.

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Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off solutions. One commonly used approach of this nature is the indicator-based evolutionary algorithm (IBEA). In this study, we highlight the solution distribution issues within IBEA and propose a modification of the original approach by embedding an additional Pareto-dominance based component for selection. The improved performance of the proposed modified IBEA (mIBEA) is empirically demonstrated on the well-known DTLZ set of benchmark functions. Our results show that mIBEA achieves comparable or better hypervolume indicator values and epsilon approximation values in the vast majority of our cases (13 out of 14 under the same default settings) on DTLZ1-7. The modification also results in an over 8-fold speed-up for larger populations.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/864368
Additional Information: Published in: 2017 IEEE Congress on Evolutionary Computation (CEC). Piscataway, N.J. : IEEE, c2017. Electronic ISBN: 978-1-5090-4601-0. pp. 1047-1054, doi:10.1109/CEC.2017.7969423 © 2017 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: Sociology, Statistics, Evolutionary computation, Electronic mail, Optimization, Benchmark testing, Computer science
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Eprints, Support
Date Deposited: 21 Mar 2017 11:42
Last Modified: 04 May 2020 18:48
URI: https://eprints.nottingham.ac.uk/id/eprint/41420

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