Abstract
This paper displays how to use fuzzy inference system (FIS) to control the individual uniform diversity for differential evolution algorithm (DE). DE solves nonlinear optimization problems, and a successful control mechanism for population diversity enhances the performance of DE. This study proposed a control mechanism that contains a novel mutation strategy and FIS because FIS is suitable for consecutive and hard classified inputs. The proposed control mechanism does not fix the target vector and controls the ratio of mutating toward the whole best individual by FIS. The FIS decides the F values for this novel mutation strategy. The experiments compared the winner of each evaluated functions among four uniform diversity goals (UDGs) with conventional strategies. From experimental results, the proposed method finds superior solutions to conventional mutation strategies at least 11 out of 15 evaluated functions in 10, 30, and 50 dimensions. Furthermore, not only the diversity curves confirm the control ability of FIS, but also different paths of convergence curves indicate the fast convergence and mitigation of evolutionary stagnation.
Published Version
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