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A new adaptive differential evolution optimization algorithm based on fuzzy inference system
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DOI：10.1016/j.jestch.2017.01.004.png)
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Abstract
En 中文
In this paper, a new version of differential evolution (DE) with adaptive mutation factor has been proposed for solving complex optimization problems. The proposed algorithm uses fuzzy logic inference system to dynamically tune the mutation factor of DE and improve its exploration and exploitation. In this way, two factors, named, the number of generation and population diversity are considered as inputs and, one factor, named, the mutation factor as output of the fuzzy logic inference system. The performance of the suggested approach has been tested firstly by using some popular single objective test functions. It has been shown that the proposed method finds better solutions than the classical differential evolution and also the convergence rate of that is really fast. Secondly, a five degree of freedom vehicle vibration model is chosen to be optimally designed by the aforesaid proposed approach. Comparison of the obtained results with those in the literature demonstrates the superiority of the results of this work. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.
Keywords:
Differential evolution
Fuzzy logic
Mutation factor
Population diversity
Optimization
Vehicle vibration model
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