递阶遗传算法理论及其应用研究
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摘要
本文对递阶遗传算法的理论及其在神经网络的参数确定、系统的结构设计和复杂车间调度问题的应用等方面进行了深入研究。
首先,研究了递阶遗传算法的理论问题。介绍了根据所研究问题的复杂性,把染色体设计成递阶结构的形式,进而提出递阶遗传算法,并在此基础上提出自适应递阶遗传算法和模拟退火算法与递阶遗传算法的结合。
接着,研究了递阶遗传算法应用于车间调度问题。首先,探讨求解最小加权完成时间的并行机调度问题。文中提出的递阶遗传算法能清楚地反映出调度方案关系,能有效地解决大规模等同并行机调度和非等同并行机调度问题。其次,研究了求解复杂的柔性作业车间调度问题。提出了递阶遗传算法求解柔性作业车间调度问题,即找到一个最优调度,使完工时间最小。仿真结果证明,本文提出的方法能达到满意的效果,具有实际应用的价值。
然后,应用递阶遗传算法于模糊系统的结构设计及其经济预测方面。利用递阶遗传算法能够把模糊系统的模糊规则数目和参数同时通过训练确定。该方法在非线性经济系统仿真,通过比较,说明该模型的预测精确度是令人满意的。
进一步,研究了递阶遗传算法用于神经网络的参数确定问题。探讨了设计BP网络并进行财务预警。利用递阶遗传算法把网络的结构和权重同时通过训练确定。与其它模式分类模型相比较,结果证明分类精确度更令人满意。根据上市公司的财务数据用所提出的方法进行财务预警是可行的。研究了设计四层BP网络及其人口预测应用。利用很好设计的递阶遗传算法能够把网络的结构、权重和阈值同时通过训练确定。结合实际案例与传统的方法相比较,结果证明文中提出的方法是可行的。
最后,探讨了结合系统结构及参数同时确定的更复杂的问题。研究了递阶遗传算法设计连续参数小波神经网络及参数确定。采用巧妙设计的递阶遗传算法,可以把网络的结构同时通过训练确定,经过实际建模与应用结果表明,该方法具有很高的应用价值。
Theory of hierarchical genetic algorithms with applications to design of a system structure, parametic determination and job-shop scheduling problems are investigated in this dissertation.
Firstly, theory of hierarchical genetic algorithms is presented. Based on the complexity of problems at hand and different from existing conventional genetic algorithms, a hierarchical representation of a chromosome is proposed with a chromosome arranged in a layered structure and, thus, a hierarchical genetic algorithm is proposed. Furthermore, an adaptive hierarchical genetic algorithm and combination of simulated annealing algorithm with the hierarchical genetic algorithm are proposed.
Next, job-shop scheduling problems are dealt with using the algorithm proposed. A parallel machine scheduling problem with total weighted completion time is tackled based on the proposed algorithm through well-designed-coding of chromosomes which clearly reflects job scheduling policy. Results from case studies show that the algorithm proposed in this dissertation can be extended to applications to large-scale parallel identical and non-identical machine scheduling problems. Further, a flexible job-shop scheduling problem is solved, delivering optimal job scheduling policy with minimal completion time, which means the algorithm is encouraging in practical uses.
Then, it is based on hierarchical genetic algorithm that structure of a fuzzy system and related parameters are determined at the same time and the system is then used for economic forecast. Compared with the BP neural networks, the model is simple and effective.
Furthermore, structure and parameters of neural networks are determined based on the algorithm proposed. BP neural network, a financial crisis warning model, and a four-layer BP neural network for population forecast are studied, respectively. A well-designed representation of chromosomes is used to perform training task of the two BP neural networks with both connection weights and numbers of neurons in a hidden layer determined at the same time. It is shown that models trained based on hierarchical genetic algorithm are able to work well.
Finally, a more complex problem in connection with determination of both structure and parameters of a system is again investigated. A wavelet neural network with continuous parameters is modelled based on the hierarchical genetic algorithm proposed and used for stocks market forecast. Following good performance of the new method applied to case study with practical data sets, it can then be concluded that the proposed algorithm can be widely applied to modeling and forecast of complex systems such as stock markets.
首先,研究了递阶遗传算法的理论问题。介绍了根据所研究问题的复杂性,把染色体设计成递阶结构的形式,进而提出递阶遗传算法,并在此基础上提出自适应递阶遗传算法和模拟退火算法与递阶遗传算法的结合。
接着,研究了递阶遗传算法应用于车间调度问题。首先,探讨求解最小加权完成时间的并行机调度问题。文中提出的递阶遗传算法能清楚地反映出调度方案关系,能有效地解决大规模等同并行机调度和非等同并行机调度问题。其次,研究了求解复杂的柔性作业车间调度问题。提出了递阶遗传算法求解柔性作业车间调度问题,即找到一个最优调度,使完工时间最小。仿真结果证明,本文提出的方法能达到满意的效果,具有实际应用的价值。
然后,应用递阶遗传算法于模糊系统的结构设计及其经济预测方面。利用递阶遗传算法能够把模糊系统的模糊规则数目和参数同时通过训练确定。该方法在非线性经济系统仿真,通过比较,说明该模型的预测精确度是令人满意的。
进一步,研究了递阶遗传算法用于神经网络的参数确定问题。探讨了设计BP网络并进行财务预警。利用递阶遗传算法把网络的结构和权重同时通过训练确定。与其它模式分类模型相比较,结果证明分类精确度更令人满意。根据上市公司的财务数据用所提出的方法进行财务预警是可行的。研究了设计四层BP网络及其人口预测应用。利用很好设计的递阶遗传算法能够把网络的结构、权重和阈值同时通过训练确定。结合实际案例与传统的方法相比较,结果证明文中提出的方法是可行的。
最后,探讨了结合系统结构及参数同时确定的更复杂的问题。研究了递阶遗传算法设计连续参数小波神经网络及参数确定。采用巧妙设计的递阶遗传算法,可以把网络的结构同时通过训练确定,经过实际建模与应用结果表明,该方法具有很高的应用价值。
Theory of hierarchical genetic algorithms with applications to design of a system structure, parametic determination and job-shop scheduling problems are investigated in this dissertation.
Firstly, theory of hierarchical genetic algorithms is presented. Based on the complexity of problems at hand and different from existing conventional genetic algorithms, a hierarchical representation of a chromosome is proposed with a chromosome arranged in a layered structure and, thus, a hierarchical genetic algorithm is proposed. Furthermore, an adaptive hierarchical genetic algorithm and combination of simulated annealing algorithm with the hierarchical genetic algorithm are proposed.
Next, job-shop scheduling problems are dealt with using the algorithm proposed. A parallel machine scheduling problem with total weighted completion time is tackled based on the proposed algorithm through well-designed-coding of chromosomes which clearly reflects job scheduling policy. Results from case studies show that the algorithm proposed in this dissertation can be extended to applications to large-scale parallel identical and non-identical machine scheduling problems. Further, a flexible job-shop scheduling problem is solved, delivering optimal job scheduling policy with minimal completion time, which means the algorithm is encouraging in practical uses.
Then, it is based on hierarchical genetic algorithm that structure of a fuzzy system and related parameters are determined at the same time and the system is then used for economic forecast. Compared with the BP neural networks, the model is simple and effective.
Furthermore, structure and parameters of neural networks are determined based on the algorithm proposed. BP neural network, a financial crisis warning model, and a four-layer BP neural network for population forecast are studied, respectively. A well-designed representation of chromosomes is used to perform training task of the two BP neural networks with both connection weights and numbers of neurons in a hidden layer determined at the same time. It is shown that models trained based on hierarchical genetic algorithm are able to work well.
Finally, a more complex problem in connection with determination of both structure and parameters of a system is again investigated. A wavelet neural network with continuous parameters is modelled based on the hierarchical genetic algorithm proposed and used for stocks market forecast. Following good performance of the new method applied to case study with practical data sets, it can then be concluded that the proposed algorithm can be widely applied to modeling and forecast of complex systems such as stock markets.
引文
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