An Efficient Hybrid Algorithm with Novel Inver-over Operator and Ant Colony Optimization for Traveling Salesman Problem

Abstract

In this research paper, we present a hybrid algorithm that merges the principles of Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Our algorithm consists of two distinct stages. In the first stage, we employ Ant Colony Optimization to establish an initial population, and we utilize the proposed Inver-over (IO) heuristic to obtain suboptimal solutions for the Euclidean Traveling Salesman Problem (TSP). The proposed Inver-over operator is used to refine the solution obtained through ACO. Subsequently, this refined solution is incorporated into the Genetic Algorithm (GA) for the second stage. In the second stage of our algorithm, we apply GA with our proposed crossover operator and a 2-optimal heuristic to further refine the solution with the goal of achieving global optimality. To assess the effectiveness of our proposed algorithm, we rely on standard benchmark data from TSPLIB. The experimental results indicate that our hybrid algorithm outperforms recent methods and exhibits greater efficiency when compared to other reported methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.