Dynamic-Balance-Adaptive Ant Colony Optimization Algorithm for Job-Shop Scheduling

Abstract

Ant colony optimization has been proven to be one of the effective methods to solve the job shop scheduling problem. However, there are two main defects: falling into local optimum easily, and having fairly long convergence time. Aiming at these problems, a new ant colony algorithm with dynamic balance and adaptive abilities is presented. The evaporation rate is adjusted adaptively to avoid the algorithm falling into local optimization, according to the tendency of local optimization. Furthermore, the iteration solution is also revised dynamically based on the “concentration ratio”, making the searching process save plenty of time. Simulation results confirm that the proposed algorithm outperform many other ant colony algorithms from literatures by improving many of the best-known solutions for the test problems.