Improved ant colony-based algorithm for solving traveling salesmen problem

Abstract

The purpose of this research is to examine how well the Improved Memetic Continuous Pheromone-based Ant Colony Optimization (ICMPACO) algorithm solves standard Traveling Salesman Problem (TSP) instances in comparison to the basic Ant Colony Optimization (ACO) algorithm and the Improved Ant Colony Optimization (IACO) algorithm. The findings of the computer simulation show that the ICMPACO algorithm has higher optimization capabilities, particularly for the berlin52, eil51, and dantzig42 examples. This superiority is further emphasized through better mean values in comparison to traditional ACO methods. The study also proposes a novel approach, Genetic Ant Colony Optimization (GACO), which integrates ACO with genetic algorithms. Pheromone data is efficiently employed to direct the choice of genetic operation points and maintain the foundational elements within the genetic algorithm. Performance results for various TSP instances reveal the potential of GACO in generating high-quality solutions. These findings suggest that both ICMPACO and GACO methods hold significant promise in addressing complex optimization challenges and could pave the way for further advancements in enhancing optimization algorithm performance.