基于细菌觅食和蚁群算法的工艺路线优化

针对工艺路线规划中满足多重约束的最优方案选择问题，提出一种细菌觅食和蚁群优化（bacteria foraging ant colony optimization，BFACO）算法。首先，将工艺路线规划转化为对加工元顺序的优化问题，构造满足多种工艺准则的加工元拓扑优先顺序图，并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数；其次，设计加工元序列与加工资源两个搜索阶段的蚁群搜索，拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点，而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作，可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列；最后，基于细菌觅食与蚁群算法的融合优化，完成多个加工元序列的信息素积累并输出最优解，解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比，结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率，验证了BFACO算法的可行性与有效性。研究表明，BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划，为实际生产提供高效且灵活的工艺路线的优化选择。

Process route optimization based on bacteria foraging and ant colony algorithm

A bacteria foragingant colony optimization (BFACO) algorithm was proposed to solve the problem of optimal solution selection satisfying multiple constraints in process route planning. Firstly, the process route planning was transformed into the sequential optimization of machining elements, and the topological priority diagram of machining elements was constructed for satisfying various process criteria, and the objective function of the minimum processing resource replacement cost under the goal of shortening the processing cycle, improving the processing quality and reducing the processing cost was constructed. Secondly, the ant colony search in the two stages of processing element sequence and processing resource was designed, and the lack of pheromones in the sequential search stage of machining element could be compensated by the topological priority diagram. The replication and trend operations of the bacterial foraging optimization algorithm were introduced in the processing resource search stage, and the processing element could obtain the processing sequence with the lowest replacement cost of processing resources in the case of multiple alternative processing resources. Finally, based on the fusion optimization of bacterial foraging and ant colony algorithm, the pheromone accumulation of multiple machining element sequences was completed and the optimal solution was output to solve the problem of local convergence and slow calculation speed of ant colony algorithm. BFACO algorithm was applied to an example and the optimization result was compared with that obtained by other optimization algorithms. The results showed that BFACO algorithm had higher computational efficiency in process route optimization, which verified the feasibility and effectiveness of BFACO algorithm. BFACO algorithm can be effectively applied to process planning considering both process constraints and replacement cost of processing resources, providing efficient and flexible optimization selection of process routes for actual production.