Spotted Hyena Optimizer: An Approach to Travelling Salesman Problems

被引：6

作者：

Van Du Nguyen ^{[1
]}

Tram Nguyen ^{[1
,2
]}

Tan Luc Nguyen ^{[1
]}

Van Cuong Tran ^{[3
]}

Hai Bang Truong ^{[4
]}

机构：

[1] Nong Lam Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam

[2] VSB Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Dept Comp Sci, Ostrava, Czech Republic

[3] Quang Binh Univ, Fac Engn & Informat Technol, Dong Hoi City, Vietnam

[4] Nguyen Tat Thanh Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam

来源：

COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2020 | 2020年 / 12496卷

关键词：

Spotted hyena optimizer; Swarm intelligence; Meta-heuristic; TSP; PARTICLE SWARM OPTIMIZATION;

D O I：

10.1007/978-3-030-63007-2_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The traveling salesman problem (TSP) is a well-knownNPproblem. Its main objective is to find a shortest close trip that visits all given cities, once for each. In this paper, we present an approach to Travelling Salesman Problems (TSPs) using a novel swarm-based algorithm named Spotted Hyena Optimizer (SHO). The algorithm is inspired by the social relationship between spotted hyenas and their collaborative behavior. Experimental results with four benchmark datasets in TSPLIB (Burma14, Bays29, Att48, and Berlin52) indicated that SHO algorithm could provide optimal solutions to TSPs. Even in some cases, it outperforms other metaheuristic algorithms such as GA, ACO, and PSO.

引用

页码：217 / 228

页数：12

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