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.
The first author acknowledges support from CONACyT and CINVESTAV-IPN to pursue graduate studies in Computer Science. The second author gratefully acknowledges support from CONACyT project no. 221551.
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Notes
- 1.
It is well-known that Pareto-based MOEAs cannot properly solve many-objective problems [12].
- 2.
- 3.
The weights of the reference point problem should be \(\sum _{i = 0}^{m}{\lambda _i} = 1\).
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Manoatl Lopez, E., Coello Coello, C.A. (2018). Use of Reference Point Sets in a Decomposition-Based Multi-Objective Evolutionary Algorithm. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_30
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