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求解卫星舱布局问题的蚁群劳动分工优化算法
引用本文:王英聪,肖人彬.求解卫星舱布局问题的蚁群劳动分工优化算法[J].控制与决策,2021,36(7):1637-1646.
作者姓名:王英聪  肖人彬
作者单位:郑州轻工业大学 电气信息工程学院,郑州 450002;华中科技大学 人工智能与自动化学院,武汉 430074
基金项目:国家自然科学基金项目(61702463,51875220);河南省科技攻关项目(192102210111);郑州轻工业大学博士科研基金项目(2017BSJJ004).
摘    要:卫星舱布局是卫星总体设计的重要组成部分,其研究的是仪器设备在卫星舱内的最佳摆放方式.从空间的角度出发,卫星舱布局的一个设计方案就是不同的仪器设备在容器内占据着不同的空间,当仪器设备所占空间发生变化时,就会形成新的设计方案.因此,卫星舱布局可以看成是将容器空间合理地分配给仪器设备(分配特性),并达到某种最优指标(优化特性).在借鉴蚁群劳动分工任务分配实现卫星舱布局空间分配的基础上,进一步融合卫星舱布局的优化特性,提出一种蚁群劳动分工优化算法.在具体的实现过程中,为基本蚁群劳动分工算法设计启发式占位动作、自适应环境刺激和个性化响应阈值,同时引入禁忌搜索、跳坑策略和接收准则等优化技术,对16个代表性算例的计算结果表明,所提出算法是求解卫星舱布局的有效算法.

关 键 词:卫星舱  布局设计  优化  空间分配  蚁群  劳动分工

Ant colony labor division optimization algorithm for satellite module layout design
WANG Ying-cong,XIAO Ren-bin.Ant colony labor division optimization algorithm for satellite module layout design[J].Control and Decision,2021,36(7):1637-1646.
Authors:WANG Ying-cong  XIAO Ren-bin
Affiliation:School of Electrical and Information Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002, China; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology,Wuhan 430074,China
Abstract:Satellite module layout is an important part of satellite system design, which deals with the optimal placement of payloads (equipments and instruments) in the module. From the perspective of space, a design scheme of the satellite module layout is that different payloads occupying different spaces in the container. When the spaces occupied by payloads change, a new design scheme will be formed. Therefore, the satellite module layout can be viewed as reasonably allocating the module space to payloads and optimally achieving some goals. On the basis of using the task allocation in ant colony labor division to achieve the space allocation in satellite module layout, an ant colony labor division optimization algorithm is proposed by incorporating the optimization features in satellite module layout. During the implementation process, heuristic position-occupying actions, adaptive environment stimulus and personalized response thresholds are designed based on the basic ant colony labor division algorithm. Meanwhile, some optimization techniques (such as tabu search, off-trap strategy and acceptance criteria) are introduced. Experiments are performed on 16 representative instances, and computational results show the high effectiveness of the proposed algorithm.
Keywords:
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