An Efficient Meta-Heuristic Methods for Travelling Salesman Problem

Abstract

AbstractOver the past several decades, researchers have increasingly attempted to create an autonomous problem solver that might address problems in computer science, mathematics, economics, and engineering. When faced with a problem, people often go to nature for inspiration. Intelligent multi-agent systems have been inspired by the collective behavior of social insects like ants and bees, as well as other animals, such as bird flocking and fish schooling. Solutions to NP-hard issues, including the Traveling Salesman Problem (TSP), may be found through the application of algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). The TSP is a classic example of an NP-hard problem that has received a great deal of attention from researchers. Numerous mathematical models, software implementations, and methodological proposals have been made for TSP. TSP has been the subject of several exact and metaheuristic methods. In this research, we applied six effective metaheuristic algorithms to solve seven benchmark TSPs, including Bays29, att48, eil51, berlin52, st70, pr76 and kroa100. Using the identical settings for each simulation, we assessed the empirical data that existed in a certain arrangement. We illustrate the performance of optimal and metaheuristic solutions for TSP. ABC is shown to be near-optimal with only 1.5% degradation.KeywordsTraveling salesman problemMetaheuristicACOPSOABCGASATS