Bi-objective Elite Differential Evolution Algorithm for Multivalued Logic Networks

Jian Sun, Shangce Gao, Hongwei Dai, Jiujun Cheng, Meng Chu Zhou, Jiahai Wang

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


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.

Original languageEnglish (US)
Article number8478769
Pages (from-to)233-246
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number1
StatePublished - Jan 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Differential evolution (DE)
  • multiobjective
  • multivalued logic (MVL)
  • network
  • pareto optimal solution


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