Adaptive FA based on evaluation and control of search state for superior solution set search problem.

In conventional single‐objective optimization, presenting multiple alternatives is difficult because the aim is to search for only one global optimal or suboptimal solution. In this paper, we propose a superior solution set search problem as an optimization problem to simultaneously find multiple excellent solutions in multimodal functions. In addition, we analyze the characteristics of the Firefly Algorithm (FA), which divides the search process into multiple clusters, and using this property, we efficiently search for the superior solution set. We interpret the metaheuristics strategy (i.e., diversification and intensification) of the superior solution set search problem and clarify the effects of parameters on the cluster search dynamics of FA in terms of the metaheuristics strategy. After the above analysis, we propose an adaptive FA that preliminarily performs parameter adjustment according to set target value schedule, evaluate indicators of diversification and intensification for the superior solution set search problem, and verify their usefulness by conducting a numerical experiment using three typical benchmark functions. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]