Search Trajectories Networks of Multiobjective Evolutionary Algorithms

被引：9

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

页码：223 / 238

页数：16

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