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An improved MOEA/D utilizing variation angles for multi-objective optimization

Published: 15 July 2017 Publication History

Abstract

This work proposes a decomposition-based multi-objective evolutionary algorithm utilizing variation angles among objective and weight vectors. The proposed algorithm introduces an angle-based proportional selection and dominance- and angle-based solution comparison criterion. Experimental results using WFG4 and WFG5 problems show that the proposed algorithm achieves better search performance than the conventional MOEA/D and MOEA/D-CRU.

References

[1]
Q. Zhang, H. Li, "MOEA/D: A Multi-objective Evolutionary Algorithm Based on Decomposition," IEEE Trans. on EC, Vol. 11, No. 6, pp. 712--731, 2007.
[2]
H. Sato, "Chain-Reaction Solution Update in MOEA/D and Its Effects on Multi and Many-Objective Optimization," Soft Computing, Springer, Vol. 20, Issue 10, pp. 3803--3820, 2016.

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  1. An improved MOEA/D utilizing variation angles for multi-objective optimization

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    cover image ACM Conferences
    GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2017
    1934 pages
    ISBN:9781450349390
    DOI:10.1145/3067695
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 15 July 2017

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    1. MOEA/D
    2. multi-objective optimization

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