Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 50, Issue 1, Pages 233-246Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2868493
Keywords
Differential evolution (DE); multiobjective; multivalued logic (MVL); network; Pareto optimal solution
Categories
Funding
- National Natural Science Foundation of China [61472284, 61673403]
- JSPS KAKENHI [JP17K12751]
- Prospective Joint Research of University-Industry Cooperation of Jiangsu [BY2016056-02]
Ask authors/readers for more resources
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 off-springs. 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available