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.

差异进化（DE）的性能主要取决于其繁殖后代策略（即试验载体生成策略和相关的控制参数）。为了充分利用近年来提出的几种有效的育种后代策略，本文提出了一种基于适应度的自适应差分进化算法（FADE）。在FADE中，将整个种群分为多个小型群，并将三种流行的育种策略保存在档案中，以供多个群使用。在每一代中，同一群中的不同个体根据自己的适应性从档案中自适应地选择自己的繁殖策略。通过自适应育种策略，同一群中的个体可以表现出独特的搜索行为。此外，种群数量可以根据最佳个体的表现在进化过程中进行自适应调整。基于自适应人口规模，可以在不同的演化阶段合理分配计算资源，然后满足不同健身环境的不同需求。通过与基于CEC2013和CEC2017测试套件以及七个实际应用的其他八个最新的DE变型进行比较，对FADE的综合性能进行了广泛的评估。此外，通过一组实验进一步证实了新引入的自适应策略的有效性和效率。可以在不同的演化阶段合理分配计算资源，然后满足不同健身环境的不同要求。通过与基于CEC2013和CEC2017测试套件以及七个实际应用的其他八个最新的DE变型进行比较，对FADE的综合性能进行了广泛的评估。此外，通过一组实验进一步证实了新引入的自适应策略的有效性和效率。可以在不同的演化阶段合理分配计算资源，然后满足不同健身环境的不同要求。通过与基于CEC2013和CEC2017测试套件以及七个实际应用程序的其他八个最新的DE变型进行比较，对FADE的综合性能进行了广泛的评估。此外，通过一组实验进一步证实了新引入的自适应策略的有效性和效率。