Abstract
Fruit fly optimization algorithm (FOA) is a new method for finding global optimization based on food finding behavior of the fruit fly. The original FOA can only solve problems that have optimal solutions in zero vicinity. To make FOA more universal for the continuous optimization problems, especially for those problems with optimal solution that are not zero. This paper proposes a hybrid fruit fly optimization and differential evolution (DEFOA) by modifying the expression of the smell concentration judgment value and by introducing a differential vector to replace the stochastic search. In this paper, we propose an improved K-Means algorithm based on hybrid FOA and Differential Evolution (DE).
Published Version
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