A fuzzy adaptive differential evolution algorithm

Experimental results, provided by the proposed algorithm for a set of standard test functions, outperformed those of the standard differential evolution algorithm for optimization problems with higher dimensionality.

Abstract

The differential evolution is a floating-point encoded evolutionary algorithm for global optimization over continuous spaces. This algorithm so far uses empirically chosen fixed search parameters. This study is to make the search more responsive to changes in the problem. This paper proposes a new adaptive form of DE having lower number of search parameters required to be set by the user a priori. The fuzzy differential evolution algorithm uses fuzzy logic controllers whose inputs incorporate the relative function values and individuals of the successive generations to adapt the search parameters for the mutation operation and the crossover operation. Standard test functions are used to demonstrate. This new algorithm results a faster convergence for these functions.