多群体混合进化算法求解IPPS问题

针对工艺规划与调度集成（Integration of Process Planning and Scheduling, IPPS）问题求解复杂性，为提高求解效率，设计了包含探索种群，寻优种群和最优种群的多群体混合进化算法，通过运用混合遗传算法和基于聚类淘汰机制的差分进化算法分别更新探索种群中工艺链和加工顺序链，保持可行解多样性和差异性。然后利用克隆领域搜索算法完成寻优种群中可行解的克隆和领域搜索，进一步提高种群质量。最后按照精英保留策略更新最优种群获得全局最优解。并通过实例计算对比，结果显示算法搜索效率和求解质量均有明显改善，且稳定性较好，表明该算法求解IPPS问题的可行性及优越性。

Multi-group hybrid evolutionary algorithm for solving IPPS problem

For integrated process planning and scheduling (IPPS) problems solving complexity, in order to improve computational efficiency, this paper designed a multi-group hybrid evolutionary algorithm including explore population, optimum population and optimal population. Using Hybrid genetic algorithm and the differential evolution algorithm based clustering mechanism updated processing chains and process order chains of explore population respectively, keeping the diversity and difference of feasible solutions. Then using the clone and field searching algorithm completed clone and search of feasible solutions in the optimum populations, the quality of populations making further improvement. Finally, through the example calculation and comparison, the calculation results indicated that the algorithm could improve searching efficiency and solving quality, with good stability, which showed the feasibility and superiority of the algorithm to solve the IPPS problem.