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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Hybrid Niching Sparrow search algorithm for solving traveling salesman problem

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pp. 737–746Vol. 46Issue 3April 2025DOI: 10.47974/JIOS-1791XML
Received:
11 Sep 2024
Published Online:
05 Apr 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1791
Pages:
737–746

Abstract

The Traveling-Salesman-Problem (TSP) is a classic combinatorial optimization challenge with significant real-world applications in different fields, such as logistics, transportation, and manufacturing. In this research Sparrow Search Algorithm (SSA) is considered to solve TSP for random cities. SSA, when it comes to solving the Traveling Salesman Problem (TSP) alone, it does not exploit specific problem characteristics of the TSP. SSA basically operates on continuous domains and originate for solving optimization problems in real-valued spaces. The TSP, on the other hand, requires finding the optimal permutation of cities. So, this research paper introduces a novel hybrid algorithm, the hybrid Niching Sparrow Search Algorithm (NSSA) to handle TSP instances proficiently. The proposed algorithm integrates the analytic capabilities of the SSA with the niching techniques to find high-quality solutions to TSP instances. The integration of a niching strategy within NSSA aims to tackle these challenges by fostering solution diversity and encouraging exploration across various regions of SSA’s search space. To evaluate the efficacy of the NSSA, computational experiments are conducted on a set of random cities for TSP. The outcomes of the experiment demonstrate the superior performance of the NSSA as compared to SSA.

Keywords

Subject Classifications

90B2590C4790C90

References

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