Paper Info
Title
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms.
Abstract
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.
Year
DOI
Venue
2017
10.1007/s00500-018-3631-x
Soft Computing
Keywords
Field
DocType
Metaheuristics, Multiobjective evolutionary algorithms, Combinatorial optimization, Traveling salesperson problem, Set covering problem
Design elements and principles,Set cover problem,Mathematical optimization,Evolutionary algorithm,Combinatorial optimization problem,Computer science,Combinatorial optimization,Travelling salesman problem,Local search (optimization),Metaheuristic
Journal
Volume
Issue
ISSN
abs/1703.09469
21.0
1432-7643
Citations 
PageRank 
References 
0
0.34
26
Authors
2
Name
Order
Citations
PageRank
Mansoureh Aghabeig100.68
A. Jaszkiewicz266050.68