| 引用本文: | 王静,王春梅,姚秀娟.基于改进协同遗传算法的有效载荷系统功能序列规划方法.[J].国防科技大学学报,2019,41(6):19-24.[点击复制] |
| WANG Jing,WANG Chunmei,YAO Xiujuan.Functional sequence planning method based on improved co-evolutionary genetic algorithm for payload system[J].Journal of National University of Defense Technology,2019,41(6):19-24[点击复制] |
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| 基于改进协同遗传算法的有效载荷系统功能序列规划方法 |
| 王静1,2,王春梅1,姚秀娟1 |
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(1. 中国科学院国家空间科学中心, 北京 100190;2. 中国科学院大学, 北京 100049)
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| 摘要: |
| 针对传统回溯算法在求解基于知识模型的有效载荷系统功能序列规划问题中搜索效率低的问题,提出一种基于“择劣变异”(Worst Individual Mutation,WIM)策略的协同遗传算法(Co-evolutionary Genetic Algorithm,CGA)的改进算法WIM-CGA。该算法在遗传过程中采用双路线进化方案,即“择优实施标准遗传过程,择劣实施变异操作”,达到提高求解精确度及搜索效率的目的。仿真结果表明,同等测试条件下,当功能规模为50,约束密度为1.0时,WIM-CGA算法在限定时间内最优解的平均精确度比优化的回溯算法提高了54.15%,比CGA算法提高了6.18%,且当所得解的精确度大于90%时,WIM-CGA算法比CGA算法的迭代次数减少了65.79%,耗时降低了48.97%,显著提高了功能序列规划的效率。 |
| 关键词: 知识模型 功能序列规划 协同遗传算法 择劣变异 生存期适应度评估 |
| DOI：10.11887/j.cn.201906003 |
| 投稿日期：2018-07-18 |
| 基金项目:国防科技工业民用专项科研工程研制资助项目(Y76602FH6S) |
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| Functional sequence planning method based on improved co-evolutionary genetic algorithm for payload system |
| WANG Jing1,2, WANG Chunmei1, YAO Xiujuan1 |
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(1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China;2. University of Chinese Academy of Sciences, Beijing 100049, China)
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| Abstract: |
| Aiming at the low search efficiency of the traditional backtracking algorithm when planning the function sequence of the payload system based on the knowledge model, an improved algorithm named as WIM-CGA for CGA (co-evolutionary genetic algorithm) was proposed, which was based on the WIM (worst individual mutation) strategy. The algorithm adopted a dual-route evolution scheme in the genetic process, which was “the better individuals perform standard genetic processes, and the worse individuals perform mutation operation”, to improve the solution accuracy and search efficiency. Simulation results show that under the same test conditions, when the function scale is 50 and the constraint density is 1.0, the average accuracy of the optimal solution of WIM-CGA within the limited time is 54.15% higher than that of GAC-BS (BS based on generalized arc consistency) and 6.18% higher than CGA, and when optimal solution accuracy reaches 90%, the iteration times of WIM-CGA is 65.79% lower than that of CGA, and the time consumed is reduced by 48.97%. The efficiency of functional sequence planning is improved significantly. |
| Keywords: knowledge model functional sequence planning co-evolutionary genetic algorithm worst individual mutation life time fitness evaluation |
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