A non-cooperative game for faster convergence in cooperative coevolution for multi-objective optimization

Antonio, Luis; Coello, Carlos A. Coello

doi:10.1109/cec.2015.7256881

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…A CCMOEA makes the member of the species populations collaborate to evaluate the objective functions. Some representative sequential CCMOEAs can be found in [113,114,115,116,117,6]. Due to the existence of multiple species populations in charge of subsets of decision variables, the parallelization could be a latent tool to improve these CCMOEAs.…”

Section: Coevolutionary Moeasmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Falcón-Cardona

Gómez

Coello

et al. 2021

Swarm and Evolutionary Computation

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Section: Coevolutionary Moeasmentioning

confidence: 99%

“…This scheme aims to improve the exploration of the objective space. Recent proposals are the following: [118,119,117,120]. Due to the assigment of an objective function to a species population, the application of the master-slave or the island model seems direct.…”

Section: Coevolutionary Moeasmentioning

confidence: 99%

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Falcón-Cardona

Gómez

Coello

et al. 2021

Swarm and Evolutionary Computation

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“…Each species population represents a piece of a larger problem, and it is the task of those populations to evolve increasingly fit pieces for the larger problem. Recent work in co-evolutionary algorithms (CAs) research considers co-evolution as a form of multi-objective optimization [1], [37], [38]. In our investigation, the focus is on how coevolution can be integrated in order to provide a better way of computing values for an MOEA which uses -dominance as its density estimator.…”

Section: A Co-evolutionary Algorithmsmentioning

confidence: 99%

A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on $\epsilon$ -Dominance

et al. 2019

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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. INDEX TERMS Multi-objective evolutionary algorithms,-dominance, co-evolutionary schemes, parameter setting.

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