Paper Info
Title
Effective Anytime Algorithm For Multiobjective Combinatorial Optimization Problems
Abstract
In multiobjective optimization, the result of an optimization algorithm is a set of efficient solutions from which the decision maker selects one. It is common that not all the efficient solutions can be computed in a short time and the search algorithm has to be stopped prematurely to analyze the solutions found so far. A set of efficient solutions that are well-spread in the objective space is preferred to provide the decision maker with a great variety of solutions. However, just a few exact algorithms in the literature exist with the ability to provide such a well-spread set of solutions at any moment: we call them anytime algorithms. We propose a new exact anytime algorithm for multiobjective combinatorial optimization combining three novel ideas to enhance the anytime behavior. We compare the proposed algorithm with those in the state-of-the-art for anytime multiobjective combinatorial optimization using a set of 480 instances from different well-known benchmarks and four different performance measures: the overall non-dominated vector generation ratio, the hypervolume, the general spread and the additive epsilon indicator. A comprehensive experimental study reveals that our proposal outperforms the previous algorithms in most of the instances.(c) 2021 Elsevier Inc. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.ins.2021.02.074
INFORMATION SCIENCES
Keywords
DocType
Volume
Multiobjective combinatorial optimization, Anytime algorithm, Well-spread non-dominated points
Journal
565
ISSN
Citations 
PageRank 
0020-0255
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Miguel Ángel Domínguez-Ríos100.34
J. Francisco Chicano21329.27
Alba Enrique31438.74