This paper proposed a new algorithm inspired by the life of Killer Whale. A group of Killer Whale called Matriline that consist of a leader and members. The leader's duty searches prey position and the optimum direction to chase the prey, meanwhile chase the prey is performed by the members. Optimum direction means minimum direction and maximum velocity. Global optimum is obtained by comparing the results of member's actions. In this algorithm, if value of objective function of members more than leader, hence the leader must find out another new potential prey. In order to obtain the performance of proposed algorithm, it is necessary to test the new algorithm together with other algorithm using known mathematical function that available in Comparing Continuous Optimizers (COCO) especially Black Box Optimization Benchmarking (BBOB). Optimization results show that the performances of purposed algorithm has outperformed than others algorithms such as Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Simulated Annealing (SA).
Biyanto, T. R., Matradji, Irawan, S., Febrianto, H. Y., Afdanny, N., Rahman, A. H., … Bethiana, T. N. (2017). Killer Whale Algorithm: An Algorithm Inspired by the Life of Killer Whale. In Procedia Computer Science (Vol. 124, pp. 151–157). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.12.141