Shuffled shepherd optimization method: a new Meta-heuristic algorithm

被引：103
作者：
Kaveh, Ali [1 ]
Zaerreza, Ataollah [1 ]
机构：
[1] Iran Univ Sci & Technol, Sch Civil Engn, Tehran, Iran
来源：
ENGINEERING COMPUTATIONS | 2020年 / 37卷 / 07期
关键词：
Shuffled shepherd optimization algorithm; Sheep; Shepherd; Meta-heuristic; Optimization; Mathematical bench mark; Truss structures optimization; PARTICLE SWARM; MULTIOBJECTIVE OPTIMIZATION; DIFFERENTIAL EVOLUTION; WHALE OPTIMIZATION; OPTIMAL-DESIGN;
D O I：
10.1108/EC-10-2019-0481
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Purpose This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized.
引用
收藏
页码：2357 / 2389
页数：33
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