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| 遗传算法与蚂蚁算法动态融合的软硬件划分 |
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熊志辉1,2, 李思昆1, 陈吉华1
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1.国防科学技术大学,计算机学院,湖南,长沙,410073;2.国防科学技术大学,信息系统与管理学院,湖南,长沙,410073
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| 摘要: |
| 面向嵌入式系统和SoC(system-on-a-chip)软硬件双路划分问题,提出遗传算法与蚂蚁算法动态融合的软硬件划分算法.基本思想是:(1)利用遗传算法群体性、全局、随机、快速搜索的优势生成初始划分解,将其转化为蚂蚁算法所需的初始信息素分布,然后利用蚂蚁算法正反馈、高效6收敛的优势求取最优划分解;(2)在遗传算法运行过程中动态确定遗传算法与蚂蚁算法的最佳融合时机,避免由于遗传算法过早或过晚结束而影响划分算法的整体性能.该算法既发挥了遗传算法与蚂蚁算法在寻优搜索中各自的优势,又克服了遗传算法在搜索到一定阶段时最优解搜索效率低以及蚂蚁算法初始信息素匮乏的不足,并且在算法中提出了遗传算法与蚂蚁算法动态融合的衔接策略.实验结果表明,该算法在性能上明显优于遗传算法和蚂蚁算法,并且划分问题规模越大,优势越明显. |
| 关键词: 遗传算法 蚂蚁算法 嵌入式系统 软硬件划分 信息素 |
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| 基金项目:Supported bythe National Natural Science Foundation of China under Grant No.90207019(国家自然科学基金);the National High-Tech Research and Development Plan of China under Grant No2002AA1Z1480(国家高技术研究发展计划(863)) |
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| Hardware/Software Partitioning Based on Dynamic Combination of Genetic Algorithm and Ant Algorithm |
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XIONG Zhi-Hui,LI Si-Kun,CHEN Ji-Hua
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| Abstract: |
| Genetic algorithm can do colony global searching quickly and stochastically, but can’t efficiently get to optimal results, since it slows down when solving to certain scope. On the other hand, ant algorithm gets to optimal results efficiently, but lacks initial pheromone at the beginning. To solve the hardware/software bi-partitioning problem in embedded system and system-on-a-chip design, the authors put forward a new algorithm based on dynamic combination of genetic algorithm and ant algorithm. The basic idea is: (1) using genetic algorithm to generate preliminary partitioning results, converting them into initial pheromone distribution for ant algorithm, and then using ant algorithm to search for optimal partitioning scheme; (2) while running genetic algorithm, dynamically determining the best combination time of genetic algorithm and ant algorithm to avoid too early or too late termination of the genetic algorithm. The algorithm utilizes the advantages of the two algorithms and overcomes their disadvantages, and it introduces a dynamic combination strategy between them. Experimental results show the algorithm excels genetic algorithm and ant algorithm in performance, and it is discovered that the bigger the partitioning problem is concerned, the better the algorithm performs. |
| Key words: genetic algorithm ant algorithm embedded system hardware/software partitioning pheromone |
