Fuzzy Rule-Based Design of Evolutionary Algorithm for Optimization

During the last two decades, many multioperator- and multimethod-based evolutionary algorithms for solving optimization problems have been proposed. Although, in general terms, they outperform single-operator-based traditional ones, they do not perform consistently for all the problems tested in the...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 49; no. 1; pp. 301 - 314
Main Authors: Elsayed, Saber, Sarker, Ruhul, Coello Coello, Carlos A.
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Design optimization
Evolutionary algorithms
Fuzzy logic
Genetic algorithms
Heuristic algorithms
Linear programming
multimethod
multioperator
Optimization
Sociology
State of the art
Statistics
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:During the last two decades, many multioperator- and multimethod-based evolutionary algorithms for solving optimization problems have been proposed. Although, in general terms, they outperform single-operator-based traditional ones, they do not perform consistently for all the problems tested in the literature. The designs of such algorithms usually follow a trial and error approach that can be improved by using a rule-based approach. In this paper, we propose a new way for two algorithms to cooperate as an effective team, in which a heuristic is applied using fuzzy rules of two complementary characteristics, the quality of solutions and diversity in the population. In this process, two subpopulations are used, one for each algorithm, with greater emphasis placed on the better-performing one. Inferior algorithms learn from trusted ones and a fine-tuning procedure is applied in the later stages of the evolutionary process. The proposed algorithm was analyzed on the CEC2014 unconstrained problems and then tested on other three sets (CEC2013, CEC2005, and 12 classical problems), with its results showing a high success rate and that it outperformed both single-operator-based and different state-of-the-art algorithms.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2017.2772849