Differential Evolution with Fitness-Based Niching and Adaptive Mutation for Global Optimization

To handle the problem of premature convergence and premature loss of diversity in differential evolution, a niching method fitness-based and an adaptive mutation strategy differential evolution is proposed for global optimization. This algorithm designs a niche partitioning strategy that divides the population into niches with different numbers of individuals based on their fitness and evolutionary stages. Adopting adaptive mutation strategies for the obtained niches enhances the exploitation of superior niches and the exploration of inferior niches, thereby supporting balanced evolutionary search. On the CEC'2015 benchmark function test set, the suggested method's performance is assessed and contrasted with related methods. According to the outcomes, the suggested fitness-based niching and adaptive mutation schemes hold promise for enhancing DE performance.