A fitness-based adaptive differential evolution algorithm

The performance of differential evolution (DE) mainly depends on its breeding offspring strategy (i.e., trial vector generation strategies and associated control parameters). To take full advantage of several effective breeding offspring strategies proposed in recent years, a fitness-based adaptive differential evolution algorithm (FADE) is proposed in this paper. In FADE, the entire population is split into multiple small-sized swarms, and three popular breeding strategies are saved in an archive which can be utilized by the multiple swarms. In each generation, different individuals in a same swarm adaptively select their own breeding strategy from the archive based on their fitness. With the adaptive breeding strategy, the individuals in a same swarm can exhibit distinct search behaviors. Moreover, the population size can be adaptively adjusted during the evolutionary process according to the performance of the best individual. Based on the adaptive population size, computational resources can be rationally assigned in different evolutionary stages, and then to satisfy diverse requirements of different fitness landscapes. The comprehensive performance of FADE is extensively evaluated by comparisons between it and other eight state-of-art DE variants based on CEC2013 and CEC2017 test suites as well as seven real applications. In addition, the effectiveness and efficiency of the newly introduced adaptive strategies are further confirmed by a set of experiments. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces a fitness-based adaptive differential evolution algorithm (FADE) that splits the population into multiple small-sized swarms and uses an archive of breeding strategies, allowing individuals within the same swarm to adaptively select their own strategy based on fitness. By adaptively adjusting population size and allocating computational resources based on performance, FADE can effectively address diverse fitness landscapes and achieve distinct search behaviors within each swarm. The effectiveness and efficiency of the newly introduced adaptive strategies are confirmed through comprehensive evaluations and comparisons with other state-of-art DE variants.