A Novel Approach to solve Traveling Salesman Problem (TSP) using Metaheursitic Hybrid Algorithms - Kaur, Supreet; Kaur, Kiranbir (International Journal of Computer Sciences and Engineering, 2025)
There is a great need for Artificial Intelligence and Nature Inspired Metaheuristic Algorithms for real world problems like Traveling Salesman Problem (TSP) belonging to NP-Hard Optimization problems which are hard to solve using mathematical formulation models. They are also a requirement for fast and accurate algorithms, specifically those that find out a node from start to the goal with the minimum cost, distance, time, money, energy etc. The Traveling Salesman Problem (TSP) is a combinatorial optimization problem which in it’s the purest form has a respective application for instance planning, logistics, and manufacture of microchips, military and traffic and so on. Metaheuristic techniques are general algorithmic frameworks including nature-inspired designs to solve complex optimization problems and they are a fast-growing research domain since a few decades. This paper proposes to solve this problem using hybridization of ACO (Ant Colony Optimization) and SA (Simulated Annealing). Ant Colony Optimization (ACO) is a population-based metaheuristic that can be used to find out appropriate approximate solutions to understand difficult NP-Hard optimization problems. Simulated Annealing (SA) is also a population-based metaheuristic that is inspired by annealing process proceeded with higher level temperature rate; it starts position on a first solution to maximum temperature, where the exchange states are accepted with a desired global extreme point is out of sight among many, poor temperature and probability density function, local update extrema. Moreover, MATLAB programming is used to implement the algorithms using solved TSP are three benchmarks on the same platform conditions for ACO, SA and Hybrid ACO-SA.