A hybrid genetic algorithm for Job-Shop scheduling problem

Abstract

A hybrid optimization algorithm is proposed for Job-Shop scheduling problem, which is based on the combination of adaptive genetic algorithm and improved ant algorithm. The algorithm gets the initial pheromone distribution using adaptive genetic algorithm at first, then runs improved ant algorithm. The algorithm utilizes the advantages of the two algorithms and overcomes their disadvantages. Experimental results show the algorithm excels genetic algorithm and ant algorithm in performance, and it is discovered that the bigger the problem is concerned, the better the algorithm performs.