Adaptive Differential Evolution Algorithm Based on Fitness Landscape Characteristic

نویسندگان

چکیده

Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for fitness landscape conditions to find the optimal solution, there not single strategy that can be suitable all landscapes. As result, developing adaptively steer population based on critical. Motivated by this fact, paper, novel adaptive DE (FL-ADE) proposed, utilizes local characteristics each generation (1) adjust size adaptively; (2) generate DE/current-to-pcbest mutation strategy. The mechanism of enables decrease or increase during search. Due adjustment landscapes evolutionary processes, computational resources rationally assigned at stages satisfy diverse requirements Besides, strategy, randomly chooses one top p% individuals from archive cbest pcbest, also an characteristic. Using approximated as optimums increases algorithm’s ability explore complex multimodal functions avoids stagnation due use good values. Experiments conducted CEC2014 benchmark test suit demonstrate proposed FL-ADE results show algorithm performs better than other seven highly performing state-of-art variants, even winner CEC2017. In addition, effectiveness paper respectively verified.

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ژورنال

عنوان ژورنال: Mathematics

سال: 2022

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math10091511