An improved ant colony optimization for job-shop scheduling problem

Abstract

The job-shop scheduling problem (JSP), proven to be an NP-hard problem, is difficult to obtain the optimal or near-optimal solution. Ant colony optimization (ACO) is a metaheuristic that takes inspiration from the foraging behavior of a real ant colony to solve the optimization problem. This paper proposes an improved ant colony optimization (IACO) to solve JSP. At the beginning, by adjusting pheromone approach and introducing the approach of crossover and mutation, the IACO is able to prevent the search process from getting trapped in the local optimal solution. Then, by combining the ACO with other heuristics, the IACO improves the convergence speed. Furthermore, the IACO is tested using the classical sets. The effectiveness of IACO on solving JSP is validated by comparing the computational results with those previously presented in the literature.