Fuzzy control of parameters to dynamically adapt the HS algorithm for optimization

This paper develops a new fuzzy harmony search algorithm (FHS) for solving optimization problems. FHS employs a novel method using fuzzy logic for adaptation of the harmony memory accepting parameter that enhances the accuracy and convergence rate of the harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented. The FHS algorithm has been successfully applied to various benchmarking optimization problems. Numerical results reveal that the proposed algorithm can find better solutions when compared to HS and other heuristic methods and is a powerful search algorithm for various benchmarking optimization problems.

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