Valley-Adaptive Clearing Scheme for Multimodal Optimization Evolutionary Search

Recent studies have shown that clearing schemes are efficient multi-modal optimization methods. They efficiently reduce genetic drift which is the direct reason for premature convergence in genetic algorithms. However, clearing schemes assumed a landscape containing equal-spaced basins when using a fixed niche radius. Further, most clearing methods employ policies that favor elitists, thus affecting the explorative capabilities of the search. In this paper, we present a valley adaptive clearing scheme, aiming at adapting to non-uniform width of the valleys in the problem landscape. The framework of the algorithm involves hill-valley initialization, valley-adaptive clearing and archiving. Experimental results on benchmark functions are presented to demonstrate that the proposed scheme uncovers more local optima solutions and displays excellent robustness to varying niche radius than other clearing compeers.