User-preference based decomposition in MOEA/D without using an ideal point

Qi, Y, Li, X, Yu, J and Miao, Q 2019, 'User-preference based decomposition in MOEA/D without using an ideal point', Swarm and Evolutionary Computation, vol. 44, pp. 597-611.

Document type: Journal Article
Collection: Journal Articles

Title User-preference based decomposition in MOEA/D without using an ideal point
Author(s) Qi, Y
Li, X
Yu, J
Miao, Q
Year 2019
Journal name Swarm and Evolutionary Computation
Volume number 44
Start page 597
End page 611
Total pages 15
Publisher Elsevier BV
Abstract This paper proposes a novel decomposition method based on user-preference and developed a variation of the decomposition based multi-objective optimization algorithm (MOEA/D) targeting only solutions in a small region of the Pareto-front defined by the preference information supplied by the decision maker (DM). This is particularly advantageous for solving multi-objective optimization problems (MOPs) with more than 3 objectives, i.e., many-objective optimization problems (MaOPs). As the number of objectives increases, the ability of an EMO algorithm to approximate the entire Pareto front (PF) is rapidly diminishing. In this paper, we first propose a novel scalarizing function making use of a series of new reference points derived from a reference point specified by the DM in the preference model. Based on this scalarizing function, we then develop a user-preference-based EMO algorithm, namely R-MOEA/D. One key merit of R-MOEA/D is that it does not rely on an estimation of the ideal point, which may impact significantly the performances of state-of-the-art decomposition based EMO algorithms. Our experimental results on multi-objective and many-objective benchmark problems have shown that R-MOEA/D provides a more direct and efficient search towards the preferred PF region, resulting in competitive performances. In an interactive setting when the DM changes the reference point during optimization, R-MOEA/D has a faster response speed and performance than the compared algorithms, showing its robustness and adaptability to changes of the preference model. Furthermore, the effectiveness of R-MOEA/D is verified on a real-world problem of reservoir flood control operations.
Subject Optimisation
Keyword(s) Evolutionary multi-objective optimization
Preference articulation and modeling
Reservoir flood control operation
DOI - identifier 10.1016/j.swevo.2018.08.002
Copyright notice © 2018 Elsevier B.V. All rights reserved.
ISSN 2210-6502
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