Symbiotic organisms search algorithm: Theory, recent advances and applications

被引：111

作者：

Ezugwu, Absalom E. ^{[1
]}

Prayogo, Doddy ^{[2
]}

机构：

[1] Univ KwaZulu Natal, Sch Comp Sci, King Edward Rd,Pietermaritzburg Campus, ZA-3201 Pietermaritzburg, Kwazulu Natal, South Africa

[2] Petra Christian Univ, Dept Civil Engn, Jalan Siwalankerto 121-131, Surabaya, Indonesia

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2019年 / 119卷

关键词：

Symbiotic organisms search algorithm; Swarm intelligence; Metaheuristic algorithms; Optimization; OPTIMIZATION ALGORITHM; TRUSS OPTIMIZATION; ECONOMIC-DISPATCH; PID CONTROLLER; LOCAL SEARCH; POWER; DESIGN; SYSTEM; LAYOUT; MODEL;

D O I：

10.1016/j.eswa.2018.10.045

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：184 / 209

页数：26

共 146 条

[1] Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment [J].