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基于进化算法的数值优化问题研究及其应用
其他题名Institute of Automation,Chinese Academy of Sciences
商允伟
学位类型工学博士
导师裘聿皇
2004-05-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词进化算法 数值优化 遗传算法 进化规划 Evolutionary Algorithms Numerical Optimization Genetic Algorithms Evolutionary Programming
摘要进化算法是一类借鉴生物界自然选择和遗传机制的随机搜索算法,进化算 法与传统优化方法的主要区别是群体搜索策略和群体中个体间的信息交换,本 文研究了基于进化算法的数值优化问题,论文的主要工作及创新之处包括:1.对目前常用的实数编码交叉、变异算子的性能进行了分析,在种群进化 的不同阶段,算子的作用有较大差别,求解的问题不同,算子的寻优能力也有 较大差别,分析和仿真实验表明,混合使用多种算子可扩大算法的使用范围、 降低算法对问题的敏感程度,提高算法的总体性能。 2.研究了采用遗传算法进行多峰函数优化时适应值共享机制的引入对选择 概率的影响。提出了一种二级遗传算法一禁忌搜索的混合策略,采用Micro GA 进行细化搜索,提高解的精度,同时引入禁忌机制,防止搜索回溯到已经搜索 过的区域,算法性能优于同类其他算法。 3.对进化规划的变异算子进行了改进,对成功的变异进行适当步长延伸, 当个体变异失败时,对变异量实施Gauss或Cauchy扰动,从而使精细化搜索和 大范围搜索有机结合起来。对若干经典算例的仿真实验表明该算法的有效性。 4.讨论了前向神经网络结构、权值的同时优化设计问题,将进化规划、BP 算法、禁忌搜索相结合可设计出结构紧凑、泛化能力强的神经网络,将其应用 于妇科肿瘤的辅助临床诊断,性能优于采用BP算法设计的神经网络。 5.研究了自动化仓库货位优化配置的多目标遗传算法,提出了二维PMX交 叉算子,使其适用于矩阵编码个体的交叉,得到的解能兼顾货架稳定性和存取 效率,并可为用户提供多个候选优化解。 6. 对高雄Rosenbrock函数的极小值点的分布情况下进行了分析,提出了一种 求解高维Rosenbrock函数(4≤n≤30)极小值点的近似算法。分析表明,当 4≤n≤30时,Rosenbrock函数具有两个极小值点,对于正确使用这一函数具有 指导意义。
其他摘要Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs), Evolution Strategies (ES's), Evolutionary Programming (EP), are a class of stochastic search and optimization methods gleaned from the model of organic evolution. The differences between the EAs and the traditional search algorithms are mainly in two aspects: population-based search and information exchange between the individuals. The applications of the EAs to the numerical optimization problems are studied. The main contributions of the dissertation are summarized as follows. 1.The frequently used crossover and mutation operators of the real-coded GAs are discussed. The search abilities of these operators are compared. The analysis and the experiments show that the search ability of an operator may vary greatly at different evolutionary stages. And the search ability of an operator may vary greatly when it is used to solve different problems. If little is known about the problem, hybridization of many operators is a feasible way to extend the application range and improve the performance of the EAs. 2. The GAs can be used to solve multimodal function optimization problems because of the population-based search. The variation of the selection probability is studied when fitness sharing scheme is added to the genetic algorithm to optimize the multimodal functions. The analysis and simulation results show that the genetic algorithm with fitness sharing can maintain population diversity to a certain extent and the range of the fitness function can greatly influence the performance of the algorithm. A multi-level Genetic Algorithm & Tabu Search algorithm (MGA-TS) is proposed to optimize multimodal function. The main GA performs global search while the microgenetic algorithm (MGA) exploits the neighborhood of the current solution provided by the main GA. The TS is introduced to prevent the cycling search of some area. The simulation results for the benchmarking multimodal function show that it is superior to other algorithms. 3. A new mutation operator of EP is proposed. An extension operation is performed in order to make full use Of the good mutation direction if the offspring is better than its parent. Otherwise, a Gaussian or Cauchy perturbation is superimposed on the mutation vector based on the parent's performance. So the fine-tuning search ability of the Gaussian mutation and the coarse-grained search ability of the Cauchy mutation are combined efficiently. The experimental results show that the improved algorithm performs better than the classical EP for numerical benchmark problems. 4. The design of architecture and weights of artificial neural networks is discussed. The EP, Back Propagation (BP) and TS are hybridized to design the feedforward neural network. The experimental results show that compact neural network with good generalization ability can be produced by the algorithm in comparison with the BR And the algori
馆藏号XWLW802
其他标识符802
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5806
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
商允伟. 基于进化算法的数值优化问题研究及其应用[D]. 中国科学院自动化研究所. 中国科学院研究生院,2004.
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