Opposition-Based Whale Optimization Algorithm

被引:36
作者
Alamri, Hammoudeh S. [1 ]
Alsariera, Yazan A. [1 ]
Zamli, Kamal Z. [1 ]
机构
[1] Univ Malaysia Pahang, Fac Comp Syst & Software Engn, Gambang, Malaysia
关键词
Metaheuristic; Optimization; Whale Optimization Algorithm; Opposition-Based Learning; OBL;
D O I
10.1166/asl.2018.12959
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The Whale Optimization Algorithm (WOA) is a newly proposed metaheuristic optimization algorithm, which simulate humpback whales hunting behavior. Like other population-based algorithms, WOA generate its population randomly during the exploration and exploitation phases, which could generate values far from the optimum solution or stuck the exploration around local optima. In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). The OWOA use the Opposition-based method to enhance Whale Optimization Algorithm (WOA) performance. The OWOA looks for the solution in the opposite direction of suggested values to test if the opposite select has better solution. The OWOA is tested and compared with the original algorithm WOA and other metaheuristic methods. The benchmark results prove the efficiency of the OWOA being more efficient than WOA
引用
收藏
页码:7461 / 7464
页数:4
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