关键词: 蚁群算法, 遗传算法, 目标函数变化率, 车间调度基准问题

Abstract: According to the complementary on convergence of ant colony algorithm and genetic algorithm, a hybrid ant colony genetic algorithm based on the change rate of objective function is put forward. The basic idea of new algorithm is that the solutions of ant colony algorithm are given as the initial population of genetic algorithm, and the ant colony algorithm and genetic algorithm are dynamically called according to the change rate of objective function. When population evolutionary is close to stagnation, ant colony algorithm is called. This approach can dynamically control the call timing of ant colony algorithm and genetic algorithm. Together with the pheromone update methods, the convergence of hybrid algorithm is improved. The new algorithm is used to solve the Job shop scheduling benchmarks problem, and the simulation results show that the global and local convergence performance of new algorithm has been significantly improved compared with basic hybrid ant colony genetic algorithm.

Key words: Ant Colony Optimization（ACO）, Genetic Algorithm（GA）, change rate of objective function, Job shop scheduling benchmarks problem