research-article

Indicator-based differential evolution using exclusive hypervolume approximation and parallelization for multi-core processors

Authors:

Kiyoharu Tagawa,

Hidehito Shimizu,

Hiroyuki NakamuraAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 657 - 664

https://doi.org/10.1145/2001576.2001667

Published: 12 July 2011 Publication History

Abstract

A new Multi-Objective Evolutionary Algorithm (MOEA) based on Differential Evolution (DE), i.e., Indicator-Based DE (IBDE) is proposed. IBDE employs a strategy of DE for generating a series of offspring. In order to evaluate the quality of each individual in the population, IBDE uses the exclusive hypervolume as an indicator function. A fast algorithm called Incremental Hypervolume by Slicing Objectives (IHSO) has been reported for calculating the exclusive hypervolume. However, the computational time spent by IHSO increases exponentially with the number of objectives and considered individuals. Therefore, an exclusive hypervolume approximation, in which IHSO can be also used effectively, is proposed. Furthermore, it is proven that the proposed exclusive hypervolume approximation gives an upper bound of the accurate exclusive hypervolume. The procedure of IHSO is parallelized by using the multiple threads of the Java language. By using the parallelized IHSO, not only the exclusive hypervolume but also the exclusive hypervolume approximation can be calculated concurrently on a multi-core processor. By the results of numerical experiments and statistical tests conducted on test problems, the usefulness of the proposed approach is demonstrated.

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Cited By

Deng JZhang Q(2019)Approximating Hypervolume and Hypervolume Contributions Using Polar CoordinateIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.289510823:5(913-918)Online publication date: Oct-2019
https://doi.org/10.1109/TEVC.2019.2895108
Wang WYang SLin QZhang QWong KCoello Coello CChen J(2019)An Effective Ensemble Framework for Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2018.287907823:4(645-659)Online publication date: Aug-2019
https://doi.org/10.1109/TEVC.2018.2879078
Menchaca-Mendez AMontero EZapotecas-Martinez S(2018)An Improved S-Metric Selection Evolutionary Multi-Objective Algorithm With Adaptive Resource AllocationIEEE Access10.1109/ACCESS.2018.28774026(63382-63401)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2877402
Show More Cited By

Index Terms

Indicator-based differential evolution using exclusive hypervolume approximation and parallelization for multi-core processors
1. Computing methodologies

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Deng JZhang Q(2019)Approximating Hypervolume and Hypervolume Contributions Using Polar CoordinateIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.289510823:5(913-918)Online publication date: Oct-2019
https://doi.org/10.1109/TEVC.2019.2895108
Wang WYang SLin QZhang QWong KCoello Coello CChen J(2019)An Effective Ensemble Framework for Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2018.287907823:4(645-659)Online publication date: Aug-2019
https://doi.org/10.1109/TEVC.2018.2879078
Menchaca-Mendez AMontero EZapotecas-Martinez S(2018)An Improved S-Metric Selection Evolutionary Multi-Objective Algorithm With Adaptive Resource AllocationIEEE Access10.1109/ACCESS.2018.28774026(63382-63401)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2877402
Bezerra LLópez-Ibáñez MStützle T(2017)A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary AlgorithmsEvolutionary Computation10.1162/evco_a_00217(1-36)Online publication date: 20-Nov-2017
https://doi.org/10.1162/evco_a_00217
Drozdik MAkimoto YAguirre HTanaka K(2015)Computational Cost Reduction of Nondominated Sorting Using the M-FrontIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.236649819:5(659-678)Online publication date: Oct-2015
https://doi.org/10.1109/TEVC.2014.2366498
Coello Coello C(2014)Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current ChallengesAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences10.1007/978-3-319-11541-2_1(3-18)Online publication date: 15-Nov-2014
https://doi.org/10.1007/978-3-319-11541-2_1
Tagawa KImamura AAlba E(2013)Many-hard-objective optimization using differential evolution based on two-stage constraint-handlingProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463446(671-678)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463446

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