The hybridization of ACO + GA and RVNS algorithm for solving the time-dependent traveling salesman problem

Abstract

The time-dependent traveling salesman problem is a class of combinatorial optimization problems. Naturally, metaheuristic is a suitable approach to solve the problem with large sizes in short computation time. Previously, several metaheuristics have been proposed for solving the problem. These algorithms might have a strong search intensification, and their diversification mechanisms may not be sufficient. Due to the random nature, population-based algorithms improve on the chance of finding a globally. In this paper, we propose a population-based algorithm that combines an ant colony algorithm (ACO), genetic algorithm (GA), and neighborhood descent with random neighborhood ordering (RVND). In the algorithm, the ACO and GA are used to explore the promising solution areas that are yet to refined while the RVND exploits them with the hope of improving a solution. Therefore, our metaheuristic algorithm balances between exploration and exploitation. Extensive numerical experiments and comparisons with the state-of-the-art metaheuristic algorithms in the literature show that the proposed algorithm reaches better solutions in many cases.