Hybrid Niching Sparrow search algorithm for solving traveling salesman problem

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.