Search Trajectories Networks of Multiobjective Evolutionary Algorithms

被引：7
|
作者：
Lavinas, Yuri [1 ]
Aranha, Claus [1 ]
Ochoa, Gabriela [2 ]
机构：
[1] Univ Tsukuba, Tsukuba, Ibaraki, Japan
[2] Univ Stirling, Stirling, Scotland
来源：
APPLICATIONS OF EVOLUTIONARY COMPUTATION (EVOAPPLICATIONS 2022) | 2022年
关键词：
Algorithm analysis; Search trajectories; Continuous optimization; Visualization; Multi-objective optimization; MOEA/D;
D O I：
10.1007/978-3-031-02462-7_15
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives. Our findings suggest that we can improve our understanding of MOEAs using STNs for algorithm analysis.
引用
收藏
页码：223 / 238
页数：16
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