Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms

作     者：Aghabeig, Mansoureh Jaszkiewicz, Andrzej 

作者机构：Poznan Univ Tech Fac Comp Inst Comp Sci Ul Piotrowo 2 PL-60965 Poznan Poland 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2019年第23卷第21期

页      面：10769-10780页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Polish National Science Center [UMO-2013/11/B/ST6/01075] 

主　　题：Metaheuristics Multiobjective evolutionary algorithms Combinatorial optimization Traveling salesperson problem Set covering problem 

摘      要：In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large.