基于激素调节机制改进型自适应粒子群算法在置换流水车间调度中的应用研究

研究以最小化最大流程时间为调度目标的离散型生产作业中的置换流水车间调度问题,将基于激素调节机制的改进型自适应粒子群算法应用到其中。在该算法中,粒子群算法的个体最优初始值不再是随机生成,而是由基于启发式信息的贪婪随机自适应算法得到的工件加工顺序转换而成,同时借鉴激素调节机制,引入激素调节因子,根据单个粒子周围的粒子的信息,对粒子的飞行方程进行改进,以提高搜索效率和搜索质量。对置换流水车间调度实例Rec系列基准问题进行测试,结果验证算法的有效性。

Research on Permutation Flow-shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm with Hormone Modulation Mechanism

An improved adaptive particle swarm optimization algorithm(IAPSO),which is inspired from hormone modulation mechanism,is used to minimize the maximal makespan of the permutation flow-shop scheduling problem(FSSP).The initial best position of each particle is no longer the randomly generated initial position of each particle;it is converted from the sequence of jobs,which is generated by greedy randomized adaptive search based on heuristics.Inspired from hormone modulation mechanism,the hormonal regular factor(HF) is used to modify the updating equations of particle swarm,which is based on the information of the particles around the single particle.it improves the flying function of the particle swarm in order to obtain better searching efficiency and searching quality.The simulation results based on benchmarks demonstrate its feasibility and effectiveness.