基于云模型的自适应并行模拟退火遗传算法

董丽丽; 龚光红; 李妮; 孙勇

基于云模型的自适应并行模拟退火遗传算法

北京航空航天大学自动化科学与电气工程学院, 北京 100191

基金项目: 国家自然科学基金资助项目(61004089);教育部博士点基金资助项目(20091102120013)

详细信息

作者简介:
董丽丽(1986-),女,江苏镇江人,硕士生,donglili1986@163.com.

中图分类号: TP 18
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- 文章访问数: 5950
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- 被引次数: 0
出版历程
- 收稿日期: 2010-04-20
- 网络出版日期: 2011-09-30

Adaptive parallel simulated annealing genetic algorithms based on cloud models

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 针对遗传算法收敛速度慢,容易"早熟"等缺点,提出了一种改进的遗传算法,即基于云模型的自适应并行模拟退火遗传算法(PCASAGA,Adaptive Parallel Simulated Annealing Genetic Algorithms Based On Cloud Models).PCASAGA使用云模型实现交叉概率和变异概率的自适应调节;结合模拟退火避免遗传算法陷入局部最优;使用多种群优化机制实现算法的并行操作;使用英特尔推出的线程构造模块(TBB,Threading Building Blocks)并行技术,实现算法在多核计算机上的并行执行.理论分析和仿真结果表明:该算法比其他原有的或改进的遗传算法具有更快的收敛速度和更好的寻优结果,并且充分利用了当前计算机的多核资源.
- 遗传算法 /
- 模拟退火 /
- 云模型 /
- 自适应 /
- 并行
Abstract: Due to the shortcomings of genetic algorithms such as the low convergence rate and premature convergence, an improved genetic algorithms was proposed, called adaptive parallel simulated annealing genetic algorithms based on cloud models (PCASAGA). PCASAGA applied cloud models to the adaptive regulation of the crossover probability and mutation probability. Simulated annealing was combined to prevent genetic algorithms from local optimum. Multi-species optimization mechanism was used to realize algorithm parallel operation. Intel-s threading building blocks (TBB) parallel technology was also used to realize algorithm parallel execution on multi-core computers. Theoretical analysis and simulation results verify that PCASAGA has better convergence speed and optimal results than original or improved genetic algorithms, and it takes full advantage of the current computers multi-core resources.
- genetic algorithms /
- simulated annealing /
- cloud model /
- adaptive mechanism /
- parallel

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参考文献(1)

[1] Holland J.Adaptation in natural and artificial systems[M].Ann Arbor: University of Michigan Press,1975:1-5 [2] Nicol N S,Richard K B.Dynamic parameter encoding for genetic algorithms[J].Machine Learning,1992,1(9):9-21 [3] Srinivas M.Adaptive probabilities of crossover and mutation in genetic algorithms[J].IEEE Trans on Systems,Manand Cybernetics,1994,4(24):656-667 [4] 罗胜钦,马萧萧,陆忆.基于改进的NSGA遗传算法的SOC硬件划分方法[J].电子学报,2009,37(11):2595-2599 Luo Shengqi,Ma Xiaoxiao,Lu Yi.An advanced non-dominated sorting genetic algorithm based SOC hardware/software partitioning[J].Acta Electronica Sinica,2009,37(11):2595-2599 (in Chinese) [5] 袁煜明,范文慧,杨雨田,等.一种基于多样化成长策略的遗传算法[J].控制与决策,2009,24(12):1801-1804 Yuan Yuming,Fan Wenhui,Yang Yutian,et al.Genetic algorithm based on diversified development strategy[J].Control and Decision,2009,24(12):843-848 (in Chinese) [6] Herrera F,Lozano M,Verdegay J L.Fuzzy connectives based crossover operators to model genetic algorithms population diversity[J].Fuzzy Sets and Systems,1997,92(1):21-30 [7] Yun Yougsu,Gen Mitsuo.Performance analysis of adaptive genetic algorithms with fuzzy logic and heuristics[J]. Fuzzy Optimization and Decision Making,2003,2(2):161-175 [8] 彭勇刚,罗小平,韦巍.一种新的模糊自适应模拟退火遗传算法[J].控制与决策,2009,24(6):843-848 Peng Yonggang,Luo Xiaoping,Wei Wei.New fuzzy adaptive simulated annealing genetic algorithms[J].Control and Decision,2009,24(6):843-848 (in Chinese) [9] Gao Feng,Shen Yapeng,Li Luxian.Optimal design of piezoelectric actuators for plate vibroacoustic control usinggenetic algorithms with immune diversity[J].Smart Materials and Structures,2000,IOP:485-491 [10] Whitley D.The genetic algorithm and selection pressure:why rank-based allocation reproduction trials is best // James D S.Proceedings of the third International Conference on Genetic Algorithms.Los Altos: Morgan Kaufmann Publishers,1989:116-121

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