TY - JOUR
T1 - Killer Whale Algorithm
T2 - 4th Information Systems International Conference 2017, ISICO 2017
AU - Biyanto, Totok R.
AU - Matradji,
AU - Irawan, Sonny
AU - Febrianto, Henokh Y.
AU - Afdanny, Naindar
AU - Rahman, Ahmad H.
AU - Gunawan, Kevin S.
AU - Pratama, Januar A.D.
AU - Bethiana, Titania N.
N1 - Publisher Copyright:
© 2018 The Authors.
PY - 2017
Y1 - 2017
N2 - 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).
AB - 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).
KW - Algorithm
KW - Benchmark
KW - Killer Whale
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85041546260&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2017.12.141
DO - 10.1016/j.procs.2017.12.141
M3 - Conference article
AN - SCOPUS:85041546260
SN - 1877-0509
VL - 124
SP - 151
EP - 157
JO - Procedia Computer Science
JF - Procedia Computer Science
Y2 - 6 November 2017 through 8 November 2017
ER -