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2018 | OriginalPaper | Buchkapitel

Use of Reference Point Sets in a Decomposition-Based Multi-Objective Evolutionary Algorithm

verfasst von : Edgar Manoatl Lopez, Carlos A. Coello Coello

Erschienen in: Parallel Problem Solving from Nature – PPSN XV

Verlag: Springer International Publishing

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Abstract

In recent years, decomposition-based multi-objective evolutionary algorithms (MOEAs) have gained increasing popularity. However, these MOEAs depend on the consistency between the Pareto front shape and the distribution of the reference weight vectors. In this paper, we propose a decomposition-based MOEA, which uses the modified Euclidean distance (\(d^+\)) as a scalar aggregation function. The proposed approach adopts a novel method for approximating the reference set, based on an hypercube-based method, in order to adapt the reference set for leading the evolutionary process. Our preliminary results indicate that our proposed approach is able to obtain solutions of a similar quality to those obtained by state-of-the-art MOEAs such as MOMBI-II, NSGA-III, RVEA and MOEA/DD in several MOPs, and is able to outperform them in problems with complicated Pareto fronts.

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Vorheriges Kapitel Tree-Structured Decomposition and Adaptation in MOEA/D

Nächstes Kapitel Use of Two Reference Points in Hypervolume-Based Evolutionary Multiobjective Optimization Algorithms

It is well-known that Pareto-based MOEAs cannot properly solve many-objective problems [12].

The running time of decomposition-based MOEAs is lower than that of indicator-based MOEAs [1, 9] and reference-based MOEAs [14].

The weights of the reference point problem should be \(\sum _{i = 0}^{m}{\lambda _i} = 1\).

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Hernández Gómez, R., Coello Coello, C.A.: MOMBI: a new metaheuristic for many-objective optimization based on the R2 indicator. In: 2013 IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, México, 20–23 June 2013, pp. 2488–2495. IEEE Press (2013). ISBN 978-1-4799-0454-9

Hernández Gómez, R., Coello Coello, C.A.: Improved metaheuristic based on the R2 indicator for many-objective optimization. In: 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), Madrid, Spain, 11–15 July 2015, pp. 679–686. ACM Press (2015). ISBN 978-1-4503-3472-3

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Li, K., Deb, K., Zhang, Q., Kwong, S.: An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694–716 (2015)CrossRef

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Manoatl Lopez, E., Coello Coello, C.A.: IGD\(^+\)-EMOA: a multi-objective evolutionary algorithm based on IGD\(^{+}\). In: 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Vancouver, Canada, 24–29 July 2016, pp. 999–1006. IEEE Press (2016). ISBN 978-1-5090-0623-9

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Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRef

Titel: Use of Reference Point Sets in a Decomposition-Based Multi-Objective Evolutionary Algorithm
verfasst von: Edgar Manoatl Lopez
Carlos A. Coello Coello
Verlag: Springer International Publishing
Buch: Parallel Problem Solving from Nature – PPSN XV
Print ISBN: 978-3-319-99252-5

Electronic ISBN: 978-3-319-99253-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99253-2_30

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