A survey of symbiotic organisms search algorithms and applications

被引:35
|
作者
Abdullahi, Mohammed [1 ]
Ngadi, Md Asri [2 ]
Dishing, Salihu Idi [1 ,2 ]
Abdulhamid, Shafi'i Muhammad [3 ]
Usman, Mohammed Joda [4 ]
机构
[1] Ahmadu Bello Univ, Dept Comp Sci, Zaria, Nigeria
[2] Univ Teknol Malaysia, Fac Comp, Dept Comp Sci, Johor Baharu 81310, Malaysia
[3] Fed Univ Technol Minna, Dept Cyber Secur Sci, Minna, Nigeria
[4] Bauchi State Univ Gadau, Dept Math, PMB 068, Bauchi, Bauchi State, Nigeria
来源
NEURAL COMPUTING & APPLICATIONS | 2020年 / 32卷 / 02期
关键词
Symbiotic organisms search; Metaheuristics algorithms; Optimization; Bio-inspired algorithms; Local search; Global search; PARTICLE SWARM OPTIMIZATION; ANT COLONY OPTIMIZATION; MULTIOBJECTIVE OPTIMIZATION; DISPATCH PROBLEM; ECONOMIC-DISPATCH; GENETIC ALGORITHM; TASK ALLOCATION; NETWORK; DESIGN; SYSTEMS;
D O I
10.1007/s00521-019-04170-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.
引用
收藏
页码:547 / 566
页数:20
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