A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on ϵ-Dominance
Journal
IEEE Access
Date Issued
2019-01-01
Author(s)
Menchaca-Mendez, Adriana
Antonio, Luis Miguel
Zapotecas-Martinez, Saul
Coello Coello, Carlos A.Coello
Riff Maria-cristina
DOI
10.1109/ACCESS.2019.2896962
Abstract
Convergence and diversity of solutions play an essential role in the design of multi-objective
evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the -dominance has shown
a proper balance between convergence and diversity. When using -dominance, diversity is ensured by
partitioning the objective space into boxes of size and, typically, a single solution is allowed at each of
these boxes. However, there is no easy way to determine the precise value of . In this paper, we investigate
how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of
along the search without any need of a previous user’s knowledge. We include the proposed co-evolutionary
scheme into an MOEA based on -dominance giving rise to a new MOEA. We evaluate the proposed MOEA
solving standard benchmark test problems. According to our results, it is a promising alternative for solving
multi-objective optimization problems because three main reasons: 1) it is competitive concerning stateof-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally
efficient
evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the -dominance has shown
a proper balance between convergence and diversity. When using -dominance, diversity is ensured by
partitioning the objective space into boxes of size and, typically, a single solution is allowed at each of
these boxes. However, there is no easy way to determine the precise value of . In this paper, we investigate
how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of
along the search without any need of a previous user’s knowledge. We include the proposed co-evolutionary
scheme into an MOEA based on -dominance giving rise to a new MOEA. We evaluate the proposed MOEA
solving standard benchmark test problems. According to our results, it is a promising alternative for solving
multi-objective optimization problems because three main reasons: 1) it is competitive concerning stateof-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally
efficient
