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Alternative techniques to solve hard multi-objective optimization problems

Published: 07 July 2007 Publication History

Abstract

In this paper, we propose the combination of different optimization techniques in order to solve "hard" two- and three-objective optimization problems at a relatively low computational cost. First, we use the ε-constraint method in order to obtain a few points over (or very near of) the true Pareto front, and then we use an approach based on rough sets to spread these solutions, so that the entire Pareto front can be covered. The constrained single-objective optimizer required by the ε-constraint method, is the cultured differential evolution, which is an efficient approach for approximating the global optimum of a problem with a low number of fitness function evaluations. The proposed approach is validated using several difficult multi-objective test problems, and our results are compared with respect to a multi-objective evolutionary algorithm representative of the state-of-the-art in the area: the NSGA-II.

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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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Published: 07 July 2007

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Author Tags

  1. cultural algorithms
  2. differential evolution
  3. epsilon-constraint
  4. multi-objective optimization

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2024)Evolutionary constrained multi-objective optimization: a reviewVicinagearth10.1007/s44336-024-00006-51:1Online publication date: 4-Jul-2024
  • (2023)A Survey on Evolutionary Constrained Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.315553327:2(201-221)Online publication date: Apr-2023
  • (2022)A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimizationArchives of Computational Methods in Engineering10.1007/s11831-022-09859-930:3(2181-2209)Online publication date: 9-Dec-2022
  • (2019)Use of a goal-constraint-based approach for finding the region of interest in multi-objective problemsJournal of Heuristics10.1007/s10732-018-9387-825:1(107-139)Online publication date: 1-Feb-2019
  • (2016)Multiobjective genetic algorithm-based adaptive wavelet designInternational Journal of Computational Vision and Robotics10.1504/IJCVR.2016.0737636:1/2(127-145)Online publication date: 1-Dec-2016
  • (2015)Comparison of Various Approaches in Multi-objective Particle Swarm Optimization (MOPSO): Empirical StudyMulti-objective Swarm Intelligence10.1007/978-3-662-46309-3_3(75-103)Online publication date: 11-Mar-2015
  • (2013)Goal-constraint: Incorporating preferences through an evolutionary ε-constraint based method2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557642(741-747)Online publication date: Jun-2013
  • (2009)Dynamic multiple swarms in multiobjective particle swarm optimizationIEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans10.1109/TSMCA.2009.201391539:4(890-911)Online publication date: 1-Jul-2009
  • (2008)PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local ArchivesIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics10.1109/TSMCB.2008.92575738:5(1270-1293)Online publication date: 1-Oct-2008

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