系统仿真学报 ›› 2017, Vol. 29 ›› Issue (9): 2140-2148.doi: 10.16182/j.issn1004731x.joss.201709036

• 仿真应用工程 • 上一篇    下一篇

基于参数知识鸽群算法的离散车间能效优化

单鑫, 王艳, 纪志成   

  1. 江南大学物联网技术应用教育部工程研究中心,江苏 无锡 214122
  • 收稿日期:2017-05-18 发布日期:2020-06-02
  • 作者简介:单鑫(1991-), 男, 湖北十堰, 硕士生, 研究方向为离散制造能效优化。
  • 基金资助:
    国家自然科学基金(61572238),国家高技术研究发展计划(2014AA041505),江苏省杰出青年基金(BK20160001)

Energy Efficiency Optimization for Discrete Workshop Based on Parametric Knowledge Pigeon Swarm Algorithm

Shan Xin, Wang Yan, Ji Zhicheng   

  1. Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China
  • Received:2017-05-18 Published:2020-06-02

摘要: 针对离散制造车间复杂性、约束性特点,以车间总能耗最小为优化目标,提出一种求解离散车间能效优化的离散型知识鸽群算法。该算法在优化过程中引入参数知识以平衡局部收索和全局收索,提高了算法收敛性和寻优能力。在算法加入离散过程,不仅保留算法收敛性和寻优能力的特点,而且具有处理离散问题的能力。通过实例测试,将粒子群,遗传算法与鸽群算法的结果进行比较分析,鸽群算法在收敛性和寻优能力方面明显优于另外两种算法,验证了算法的合理性和有效性。

关键词: 离散制造车间, 能效优化, 鸽群算法, 知识参数, 二次离散

Abstract: Aiming at the characteristics of complexity, constraint in discrete manufacturing workshop, in order to minimize the total energy consumption of the workshop as the target, discrete knowledge pigeons algorithm was proposed to solve discrete workshop energy efficiency optimization. In this algorithm, parameter knowledge was introduced into the optimization process to balance local search and global search, and the convergence and optimization ability of the algorithm were improved. The discrete process was added to the pigeons algorithm, which not only preserved the convergence and optimization ability of the algorithm, but also made the algorithm capable of dealing with discrete problems. Through the test of concrete examples, the particle swarm optimization, genetic algorithm and pigeons algorithm results were compared and analyzed, the pigeons algorithm in convergence and optimization ability is superior to the other two algorithms, which verifies the rationality and validity of the algorithm.

Key words: discrete manufacturing workshop, energy efficiency optimization, pigeons algorithm, knowledge parameters, second discrete process

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