Chaotic krill herd optimization algorithm

Abstract

The Krill Herd (KH) optimization algorithm is one of the most recent heuristic optimization techniques. This algorithm mimics the lifecycle of krill in oceans. Despite high performance of KH, stagnation in local optima and slow convergence speed are two probable problems in solving challenging optimization problems. This work enhances the performance of the KH algorithm by the chaos theory. To be exact, three one-dimensional chaotic maps (Circle, Sine, and Tent) are integrated into KH. The results prove that the proposed chaotic KH algorithms are able to show superior results compared to KH in terms of local optima avoidance and convergence speed.