ABSTRACT

The optimisation process and results are classified and stored to guide the future workshop scheduling and improve the retrieval efficiency. The results show that the random inertia weight strategy is added to the standard particle swarm optimisation (PSO) algorithm. The idea of crossover and mutation in genetic algorithm (GA) is introduced to increase the diversity of population and prevent it from falling into local optimal solution. Finally, the global optimal solution can be searched by using the strong ability of genetic algorithm to jump out of local optimal to ensure that population evolution is stagnated.

中文翻译：

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机器学习领域基于数据挖掘和粒子群优化算法的车间调度问题

摘要

对优化过程和结果进行分类存储，指导未来车间调度，提高检索效率。结果表明，在标准粒子群优化（PSO）算法中加入了随机惯性权重策略。引入遗传算法（GA）中的交叉和变异思想，以增加种群的多样性，防止其陷入局部最优解。最后，可以利用遗传算法强大的跳出局部最优的能力来寻找全局最优解，保证种群进化停滞。