β-Chaotic map enabled Grey Wolf Optimizer

The diversification (exploration) and intensification (exploitation) are two main attributes of any population-based metaheuristic algorithm. It is essential for any algorithm that in exploration phase the search space is utilized and explored properly through random behavior, on the other hand, the progression of the search in a viable direction to obtain global minima, should be performed through strategic behavior in exploitation phase. A proper balance between these two can be achieved by an adaptive mechanism in every algorithm. Robustness of an algorithm is judged by the efficacy of these two attributes along with the efficiency of the bridging mechanism. In literature, the positive impact of inculcation of chaotic sequences on the efficacy of these attributes has been reported. With this motivation, the paper presents an adaptive bridging mechanism based on beta-chaotic sequence for the improvement of Grey Wolf Optimizer (GWO). The control vector of classical GWO is integrated with the beta-chaotic sequence for better exploration and exploitation virtues. The new variant beta-GWO is benchmarked on two benchmark suites 1 and 2 that include 12 shifted and biased functions and 29 Congress on Evolutionary Computation-2017 (CEC-2017) functions. Sensitivity Dependence of Initial Conditions (SDIC) is performed for tuning the initial parameters. The comparison of the proposed variant with other contemporary algorithms is carried out and different statistical tests are performed to judge the efficacy of the proposed variant. Further, the applicability of the proposed variant is checked with two real engineering problems namely frequency modulated sound waves parameter estimation problem and strategic bidding in the energy market. Results reveal that the proposed chaotic variant exhibits better exploration and exploitation qualities. (C) 2018 Elsevier B.V. All rights reserved.