Performance Analysis of Metaheuristic Algorithms on Asymmetric Travelling Salesman Problems

Karuna Panwar; Kusum Deep

doi:10.1007/978-981-95-5429-4_8

Performance Analysis of Metaheuristic Algorithms on Asymmetric Travelling Salesman Problems

Karuna Panwar^*
, Kusum Deep

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this study, the performance of five prominent metaheuristic algorithms–Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Sine-Cosine Algorithm (SCA), and Artificial Hummingbird Algorithm (AHA)–is evaluated on the Asymmetric Travelling Salesman Problem (ATSP). These algorithms were chosen for analysis due to their notable achievements in solving various optimization challenges in both theoretical and practical contexts. Originally designed for continuous optimization problem, these methods were adapted to tackle the combinatorial structure of the ATSP using an order-based decoding method. To enhance their local search capabilities, the 2-opt algorithm was also implemented. The performance assessment involved testing each algorithm across 14 distinct ATSP instances and comparing their results. Statistical tests were conducted to verify the performance outcomes, highlighting the AHA as particularly competitive and robust in solving the ATSP.

Original language	English
Title of host publication	Learning and Analytics in Intelligent Systems
Publisher	Springer Nature
Pages	131-146
Number of pages	16
DOIs	https://doi.org/10.1007/978-981-95-5429-4_8
State	Published - 2026
Externally published	Yes

Publication series

Name	Learning and Analytics in Intelligent Systems
Volume	60
ISSN (Print)	2662-3447
ISSN (Electronic)	2662-3455

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Keywords

Artificial humming bird algorithm
Asymmetric travelling salesman problem
Grey wolf optimizer
Particle swarm optimization
Sine-cosine algorithm
Whale optimization algorithm

ASJC Scopus subject areas

Control and Systems Engineering
Computer Vision and Pattern Recognition
Computer Science Applications
Artificial Intelligence

Access to Document

10.1007/978-981-95-5429-4_8

Cite this

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