Bi-objective Elite Differential Evolution Algorithm for Multivalued Logic Networks

Document Type

Article

Publication Date

1-1-2020

Abstract

In this paper, a novel algorithm called bi-objective elite differential evolution (BOEDE) is proposed to optimize multivalued logic (MVL) networks. It is a multiobjective algorithm completely different from all previous single-objective optimization ones. The two objective functions, error and optimality, are put into evaluating the fitness of individuals in evolution simultaneously. BOEDE innovatively uses an archive population with different ranks to store elite individuals and offsprings. Moreover, a characteristic updating method based on this archive structure is designed to produce the parent population. Because of the particularity of MVL network problems, the performance of BOEDE to solve them is further improved by strictly distinguishing elite solutions and Pareto optimal solutions, and by modifying the method of dealing with illegal variables. The simulations show that BOEDE can collect a great number of solutions to provide decision support for a variety of applications. The comparison results also indicate that BOEDE is significantly better than the existing algorithms.

Identifier

85054524026 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2018.2868493

e-ISSN

21682275

ISSN

21682267

PubMed ID

30295636

First Page

233

Last Page

246

Issue

1

Volume

50

Grant

17K12751

Fund Ref

Japan Society for the Promotion of Science

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