Shuffled shepherd optimization method: a new Meta-heuristic algorithm

被引：107

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

页码：2357 / 2389

页数：33

共 49 条

[41] Particle swarm approach for structural design optimization [J].