Tchebycheff Fractal Decomposition Algorithm for Bi-objective Optimization Problems

被引：0
|
作者：
Aslimani, N. [1 ]
Talbi, E-G [1 ]
Ellaia, R. [2 ]
机构：
[1] Univ Lille, Lille, France
[2] Mohammed V Univ Rabat, LERMA EMI, Rabat, Morocco
来源：
METAHEURISTICS, MIC 2022 | 2023年 / 13838卷
关键词：
Bi-objective optimization; Fractal decomposition; Tchebycheff scalarization; Adaptive reference points; EVOLUTIONARY ALGORITHM; WEIGHT DESIGN; MOEA/D;
D O I：
10.1007/978-3-031-26504-4_18
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In most of the existing multi-objective metaheuristics based on decomposition, the reference points and the subspaces are statically defined. In this paper, a new adaptive strategy based on Tchebycheff fractals is proposed. A fractal decomposition of the objective space based on Tchebycheff functions, and adaptive strategies for updating the reference points are performed. The proposed algorithm outperforms popular multi-objective evolutionary algorithms both in terms of the quality of the obtained Pareto fronts (convergence, cardinality, diversity) and the search time.
引用
收藏
页码：246 / 259
页数：14
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