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针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.
Aiming at the problem of flexible job shop scheduling, a novel two-phase dynamic mixed swarm intelligence optimization algorithm is proposed. In the initial stage of the algorithm, co-particle swarm optimization with dynamic neighborhood is used for rough searching. In the second phase, the chaos operator-based bee colony search , Which not only enhances the diversity of population but also improves the search accuracy of the algorithm and realizes the effective balance between the global search and the local search ability.According to the characteristics of flexible job shop scheduling problem, a unique encoding method and location updating strategy are adopted to avoid the illegal solution Finally, this algorithm is simulated on different scale instances and compared with other recently proposed algorithms to verify the effectiveness and superiority of the proposed algorithm.