Memetic Differential Evolution With Adaptive Niching Selection and Diversity-Driven Strategies for Multimodal Optimization

Abstract

Simultaneously identifying a set of optimal solutions within the landscape of multimodal optimization problem presents a significant challenge. In this work, a differential evolution algorithm with adaptive niching selection, diversity-driven exploration and adaptive local search strategies is proposed to tackle the challenge. In the proposed method, an adaptive niching selection strategy is devised to dynamically select appropriate niching methods from a diverse pool to evolve the population. The pool encompasses niching methods with varying search properties and is dynamically updated during evolution. Further, to enhance exploration, a diversity-driven exploration strategy, which leverages redundant individuals from convergence regions to explore the solution space, is introduced. Additionally, an adaptive local search operation, in which the probability of applying local search and corresponding sampling area are dynamically determined based on the potential of solutions as well as the stage of evolution, is developed to fine-tune promising solutions. The effectiveness of proposed method has been demonstrated on 20 test functions from CEC2013 benchmark suite. Experimental results confirm the effectiveness of our method, demonstrating its superiority compared to related algorithms.