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文章编号： 1672-6987（2024）05-0142-07； DOI： 10.16351/j.1672-6987.2024.05.019
姜涛， 周艳平*(青岛科技大学 信息科学技术学院， 山东 青岛 266061)
摘要： 针对传统文化算法初始种群多样性低、收敛效率低、易陷入局部最优解等问题，本工作对其进行优化设计，提出了一种自适应混沌竞争文化算法。该算法由上层信念空间和下层种群空间组成，种群空间为优化主体，信念空间控制种群空间的优化。首先，通过混沌立方映射产生初始种群，该方法提高了初始种群的遍历性、均匀性;其次，在种群空间引入将种群分为优势群体和劣势群体的双种群竞争机制，且不同群体采用不同进化方式，提高了算法的收敛效率;最后，将本工作提出的自适应变异方式引入双种群，避免了算法后期易陷入局部最优解的困惑。函数优化结果突出了该算法的高效性，将该算法应用于流水车间调度问题求解最小化最大完工时间优化目标，仿真结果显示，该算法在收敛速度、精度方面都有较大提升，验证了该算法的优越性。
关键词： 文化算法； 混沌立方映射； 双种群竞争机制； 自适应变异； 流水车间调度
中图分类号： TP 391.9文献标志码： A
引用格式： 姜涛， 周艳平. 自适应混沌竞争文化算法及在车间调度中的应用［J］. 青岛科技大学学报(自然科学版), 2024, 45(5): 142-148.
JIANG Tao, ZHOU Yanping. Adaptive chaos competition culture algorithm and its application in workshopscheduling［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2024， 45（5）： 142-148.
Adaptive Chaos Competition Culture Algorithm and Its
Application in Workshop Scheduling
JIANG Tao, ZHOU Yanping
(College of Information Science and Technology, Qingdao University of Science and Technology， Qingdao 266061， China)
Abstract: Aiming at the problems of low initial population diversity, low convergence efficiency, and easy to fall into local optimal solution of traditional culture algorithm, this paper optimizes the design and proposes an adaptive chaotic competitive culture algorithm.The algorithm consists of upper belief space and lower population.space composition, the population space is the optimization subject, and the belief space controls the optimization of the population space.First, the dual_population competition mechanism that divides the population in to dominant group and the disadvantaged group is introduced in the population space, and different groups adopt different evolutionary methods, which improves the convergence effciency of the algorithm; secondly, the population is divided into The dual population competition mechanism of the dominant group and the disadvantaged group, and different groups adopt different evolution methods, which improves the convergence efficiency of the algorithm.Finally, the adaptive mutation method proposed in this paper is introduced into the dual population, which avoids the confusion that the algorithm is easy to fall into the local optimal solution in the later stage.The function optimization results highlight the efficiency of the algorithm.The algorithm is applied to the flow shop scheduling problem to solve the optimization objective of minimizing the maximum make-time.The simulation results show that the algorithm has greatly improved the convergence speed and accuracy, which proves the superiority of the algorithm.
Key words: cultural algorithm; chaotic cube mapping; dual population competition mechanism; adaptive mutation; flow shop scheduling
收稿日期： 2023-11-30
基金项目： 国家自然科学基金项目(61104004).
作者简介： 姜涛(1997—),男,硕士研究生.*通信联系人.
