Grasshopper Optimisation Algorithm: Theory and application

被引：1962

作者：

Saremi, Shahrzad ^{[1
,2
]}

Mirjalili, Seyedali ^{[1
,2
]}

Lewis, Andrew ^{[1
]}

机构：

[1] Griffith Univ, Sch Informat & Commun Technol, Brisbane, Qld 4111, Australia

[2] Griffith Coll, Brisbane, Qld 4122, Australia

来源：

ADVANCES IN ENGINEERING SOFTWARE | 2017年 / 105卷

关键词：

Optimization; Optimization techniques; Heuristic algorithm; Metaheuristics; Constrained optimization; Benchmark; Algorithm; MINE BLAST ALGORITHM; TRUSS STRUCTURES; PARTICLE SWARM; EVOLUTIONARY ALGORITHMS; SEARCH; MATTER; STATES;

D O I：

10.1016/j.advengsoft.2017.01.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm (GOA) and applies it to challenging problems in structural optimisation. The proposed algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems. The GOA algorithm is first benchmarked on a set of test problems including CEC2005 to test and verify its performance qualitatively and quantitatively. It is then employed to find the optimal shape for a 52-bar truss, 3-bar truss, and cantilever beam to demonstrate its applicability. The results show that the proposed algorithm is able to provide superior results compared to well-knowri and recent algorithms in the literature. The results of the real applications also prove the merits of GOA in solving real problems with unknown search spaces. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：30 / 47

页数：18

共 67 条

[1]

[Anonymous], 1997, Journal of Global Optimization, DOI DOI 10.1023/A:1008202821328

[2]

[Anonymous], 2001, MATH0102188 ARXIV

[3] A survey on optimization metaheuristics [J].