Modified Krill Herd Optimization algorithm using chaotic parameters
Mahdi Bidar, Edris Fattahi, Hamidreza Rashidy Kanan · 2014
Krill Herd optimization algorithm which is a new metaheuristic search algorithm mimics the herding behavior of krill individuals. The significant characteristic of meta-heuristic algorithms is their ability in combination of local search and global search. This property can adjust the contribution of local search and global search in initial step and during searching process and plays crucial role in the algorithm performance. One hazard which threats the meta-heuristic algorithms is getting stuck in local optimum traps. An appropriate solution to deal with this problem is using chaos theory which brings dynamism and instability properties to the algorithm so that by strengthening the performance of random search helps the algorithm to escape from local optimum traps. In this paper, we propose a new method called chaotic Krill Herd optimization algorithm which by adopting chaos theory in Krill Herd optimization algorithm heighten its performance in dealing with various optimization problems. The obtained results by the proposed method in comparison with those of the standard Krill Herd optimization algorithm indicates the higher performance of the proposed algorithm.