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Auteurs principaux: Mendes, Cicero S. R., Araújo, Aluizio F. R., Farias, Lucas R. C.
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2410.19439
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author Mendes, Cicero S. R.
Araújo, Aluizio F. R.
Farias, Lucas R. C.
author_facet Mendes, Cicero S. R.
Araújo, Aluizio F. R.
Farias, Lucas R. C.
contents Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.
format Preprint
id arxiv_https___arxiv_org_abs_2410_19439
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Non-Dominated Sorting Bidirectional Differential Coevolution
Mendes, Cicero S. R.
Araújo, Aluizio F. R.
Farias, Lucas R. C.
Neural and Evolutionary Computing
Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.
title Non-Dominated Sorting Bidirectional Differential Coevolution
topic Neural and Evolutionary Computing
url https://arxiv.org/abs/2410.19439