Walsh-based surrogate-assisted multi-objective combinatorial optimization: A fine-grained analysis for pseudo-boolean functions

被引:3
|
作者
Derbel, Bilel [1 ]
Pruvost, Geoffrey [1 ]
Liefooghe, Arnaud [1 ]
Verel, Sebastien [2 ]
Zhang, Qingfu [3 ]
机构
[1] Univ Lille, CNRS, Inria, Cent Lille, F-59000 Lille, France
[2] Univ Littoral Cite Opale, UR 4491, LISIC, F-62100 Ur, France
[3] City Univ Hong Kong, Kowloon Tong, Hong Kong, Peoples R China
关键词
Multi-objective optimization; Discrete surrogates; Decomposition; NONDOMINATED SORTING APPROACH; EVOLUTIONARY ALGORITHM; SELECTION; PERFORMANCE;
D O I
10.1016/j.asoc.2023.110061
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The aim of this paper is to study surrogate-assisted algorithms for expensive multiobjective combina-torial optimization problems. Targeting pseudo-boolean domains, we provide a fine-grained analysis of an optimization framework using the Walsh basis as a core surrogate model. The considered framework uses decomposition in the objective space, and integrates three different components, namely, (i) an inner optimizer for searching promising solutions with respect to the so-constructed surrogate, (ii) a selection strategy to decide which solution is to be evaluated by the expensive objectives, and (iii) the strategy used to setup the Walsh order hyper-parameter. Based on extensive experiments using two benchmark problems, namely bi-objective NK-landscapes and unconstrained binary quadratic programming problems (UBQP), we conduct a comprehensive in-depth analysis of the combined effects of the considered components on search performance, and provide evidence on the effectiveness of the proposed search strategies. As a by-product, our work shed more light on the key challenges for designing a successful surrogate-assisted multi-objective combinatorial search process.(c) 2023 Elsevier B.V. All rights reserved.
引用
收藏
页数:17
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