GA-ACO in job-shop schedule problem research

Abstract

For Job-Shop schedule problem's unique characteristic, we proposed a method whose encode method is based on the ranking job number named GA-ACO(Genetic Algorithm - Ant Colony Optimization algorithm) to solve this problem. The algorithm attempts to integrate the two algorithms dynamically to solve Job-Shop schedule problem. The dynamic fusion idea of the two algorithms is: before the best point (the genetic algorithm and ant colony integration point), use the characteristics of the genetic algorithm, quickly and comprehensively generate excellent chromosomes, from which select the part of the most outstanding and convert them to initial chromosome distribution for ant colony optimization algorithem, after the best fusion point use ant colony algorithm's positive feedback, efficiently to obtain the optimal solution of shop schedule problems[1]. © 2010 Springer-Verlag.