%0 Conference Paper
%A Biyanto, T.R.
%A Matradji
%A Irawan, S.
%A Febrianto, H.Y.
%A Afdanny, N.
%A Rahman, A.H.
%A Gunawan, K.S.
%A Pratama, J.A.D.
%A Bethiana, T.N.
%D 2017
%F scholars:9077
%I Elsevier B.V.
%K Algorithms; Benchmarking; Functions; Genetic algorithms; Information systems; Simulated annealing, Black-box optimization; Global optimum; Imperialist competitive algorithm (ICA); Killer whale; Mathematical functions; Maximum velocity; Objective functions; Optimizers, Optimization
%P 151-157
%R 10.1016/j.procs.2017.12.141
%T Killer Whale Algorithm: An Algorithm Inspired by the Life of Killer Whale
%U https://khub.utp.edu.my/scholars/9077/
%V 124
%X 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). © 2018 The Authors.
%Z cited By 47; Conference of 4th Information Systems International Conference 2017, ISICO 2017 ; Conference Date: 6 November 2017 Through 8 November 2017; Conference Code:139318