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任丹萍,郑子威,陈湘国.基于动态学习和个体淘汰的鲸鱼算法求解订单接受与调度问题[J].河北工程大学自然版,2022,39(1):99-105
基于动态学习和个体淘汰的鲸鱼算法求解订单接受与调度问题
Whale Optimization Algorithm Based on Dynamic Learning and Individual Elimination Strategy to Solve Order Acceptance and Scheduling Problems
投稿时间:2021-08-01  
DOI:10.3969/j.issn.1673-9469.2022.01.015
中文关键词:  订单接受与调度  拒绝成本  改进鲸鱼优化算法  动态学习
英文关键词:order acceptance and scheduling  rejection cost  improved whale optimization algorithm  dynamic learning
基金项目:国家重点研发计划项目(2018YFF0301004)
作者单位
任丹萍 河北工程大学 信息与电气工程学院, 河北 邯郸 056038 
郑子威 河北工程大学 河北省 安防信息感知与处理重点实验室, 河北 邯郸 056038 
陈湘国 河北工程大学 信息与电气工程学院, 河北 邯郸 056038 
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中文摘要:
      结合订单型企业生产线的实际情况,在传统的订单接受与调度模型的基础上加入因客户优先级而带来的订单拒绝成本这一重要因素,并使用新型的鲸鱼优化算法(WOA)进行求解。WOA被提出是用于求解实数域的问题而且存在容易陷入局部最优的缺陷,针对这一问题提出一种改进的鲸鱼优化算法(IWOA)。使用基于排序和偏离度的编码方式用于求解订单接受与调度模型的整数域问题。加入向历史个体动态学习策略在一定程度上避免算法出现早熟。为了防止鲸鱼个体在随机搜寻的过程中偏离最优方向从而影响收敛速度,利用遗传算法的交叉选择策略淘汰劣质个体。通过实验将IWOA和WOA以及改进的灰狼算法(HGWO)分别求解订单接受与调度模型并进行比较,证明了IWOA对模型的求解结果、结果的稳定性以及算法本身的收敛速度、初始解的优质程度等方面都优于其它两种算法。
英文摘要:
      Combined with the actual situation of the production line of order-oriented enterprises, the important factor of order rejection cost due to customer priority was added to the traditional order acceptance model, and the new whale optimization algorithm (WOA) was used to solve the problem. WOA was proposed to solve real number domain problems and had the defect of easily falling into local optimum. To solve this problem, an improved whale optimization algorithm (IWOA) was proposed. The coding method based on ranking and deviation degree was used to solve the integer domain problem of the order acceptance model. Adding a dynamic learning strategy from historical individuals could avoid premature algorithms to a certain extent. In order to prevent whale individuals from deviating from the optimal direction in the process of random search and thus affecting the convergence speed, the cross-selection strategy of genetic algorithm was used to eliminate inferior individuals. The experiment compares IWOA with WOA and the improved gray wolf algorithm (HGWO), which proves the advantages of IWOA in solving the order acceptance model, the stability of result, the convergence speed of the algorithm itself and the quality of initial solution, etc.
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