Fully Informed Fuzzy Logic System Assisted Adaptive Differential Evolution Algorithm for Noisy Optimization

Abstract

The parameter adaptation enhanced differential evolution (DE) algorithm has demonstrated promising performance for noiseless optimization. However, its efficiency degrades when confronted with noise in a noisy environment, which makes the fitness comparison for adaptation unreliable. To deal with the issue and improve the performance, this article proposes a fuzzy logic system (FLS)-assisted parameter adaptation for noisy optimization, inspired by the strength of FLS in handling uncertainties. The proposed FLS is fully informed by search feedback from both the objective and solution spaces, as well as their correlation, allowing for a more comprehensive estimation of parameters. Experimental studies confirm the superiority of the proposed method in noisy environments over adaptation methods that solely rely on fitness comparison. The constructed fully informed FLS-assisted noisy DE exhibits state-of-the-art performance compared to other evolutionary algorithms.