基于知识的多智能体思维进化算法及其工程应用
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摘要
作为新一代智能计算方法，进化计算、粗糙集理论和粒计算方法不仅在各自的学科领域取得了引人注目的进展，而且它们之间的相互渗透和有机结合也将会更有力地促进软智能信息处理技术的发展和应用。思维进化算法是一种模仿人类思维机制的进化算法，但其本身并没有形成明确的知识载体、知识处理体系和思维模式，没有体现人类思维活动的本质特性。本文从智能计算的角度出发，将智能体技术和知识发现技术引入进化计算，形成一个“混合智能系统”。把进化过程中产生的数据视为知识库，通过基于粗糙集和粒计算的知识发现，获取关于被求解问题的知识，形成基于知识的多智能体思维进化算法(Knowledge based Multi-Agent Mind Evolutionary Algorithm，KMMEA)。本文的主要工作和创新性成果如下：
(1)在深入分析基本思维进化算法的基础上，提出基于知识的多智能体思维进化算法。算法以智能体为思维活动的载体，将粗糙集和粒度计算作为智能体进行知识发现、推理和决策的工具，并结合具体的进化算子来实现智能群体的信念、愿望和意图，形成具有粒度层次结构的多智能体进化系统。
(2)通过粗糙集的相关概念，将解空间和目标函数空间采用等价类的形式进行划分，形成子空间粒和目标函数粒。提出了个体变量与目标函数关系、粒度适应度景象和个体特征向量的概念，有助于从宏观上认识和掌握被求解问题的内在规律，将其作为知识来描述被求解问题的特征。通过二进制粒计算，快速获取个体变量与目标函数关系，然后计算出个体特征向量和粒度适应度景象，判断被求解问题的类型。利用所获取的知识求取寻优子空间，以缩小搜索范围，提高寻优效率和精度。
(3)针对多目标问题的复杂性，利用信息系统中的优势关系和优势类的概念，研究并讨论了KMMEA在多目标优化问题中的应用。利用信息系统的优劣关系对解空间进行划分，形成了在Pareto优势空间进行“开采”而在其余空间中“探索”的进化策略。利用子空间粒和目标函数粒的特征进行种群多样性的维护，以期找到全面和分布均匀的Pareto最优解。
(4)提出了基于优势粒粒度多目标快速排序算法。利用信息系统的优势关系和粒计算的方法获取优势粒，将优势粒粒度作为多目标排序和适应度赋值的依据，并设计了基于优势粒的快速排序算法，可极大地降低排序运算量。
(5)对思维进化中的两个重要算子“趋同”和“异化”进行了基于知识的改造，提高了算子操作的智能性，用于宏群体进化。提出了基于知识指导的变异算子，是粒度适应度景象和个体特征向量的具体运用。该算子能有效提高算法的收敛效率和寻优精度。同时研究了按照优秀模式类产生新个体的方法。
(6)探索KMMEA在工程领域中的应用。将KMMEA用于多传感器信息融合系统中神经网络的优化，提高测量结果的准确性和稳定性。将KMMEA用于线性调频连续波雷达物位计压控振荡器的非线性校正，能够显著提高系统的调频线性度，降低频谱展宽和相位噪声对距离分辨率的影响，有效提高测量信号的信噪比。这一应用已申报国家发明专利“线性调频雷达物位计非线性进化校正方法”(专利号：200410092447．2)，已经得到授权。
本文的主线是研究知识发现技术在进化过程中的应用，通过所发现的知识指导进化过程，形成混合智能计算系统。
As a new generation of intelligent computing methods, evolution computing, rough set and granular computing have not only made the remarkable progress in the fields of their own, but also the fusion of them will powerfully improve the development and application in the soft intelligent information processing technique. Mind evolutionary algorithm(MEA) belongs to evolution computing and it imitates the thought mechanism of mankind. However MEA does not form the explicit knowledge carrier, knowledge processing system and thinking model. It does not embody the essential trait of people's thinking activities. From intelligent computing point of view this thesis introduces the intelligent agent and knowledge discovery technique into the evolution computing and forms a hybrid intelligent system (HIS). The generated data in the evolution process can be regarded as the knowledge database. Knowledge in the unsolved problem can be acquired through the knowledge discovery based on rough set theory and granular computing theory. It forms the knowledge-based multi-agent mind evolutionary algorithm (KMMEA). The main work and the innovative achievements of this thesis can be concluded as follows:
On the basis of the basic mind evolutionary algorithm, the knowledge-based multi-agent mind evolutionary algorithm was put forward. The KMMEA takes the agent as the carrier of the thinking activity. As agents rough set and granular computing are used as the tools to discover the knowledge, to inference and to make the decision. Furthermore, the specific evolution operators are applied to realize the belief, the desire and the intention of the agent, which form the multi-agent evolution system with the granular-hierarchy structure.
Through the concepts in rough set the solution space and the objective function spaces can be divided into the subspace granule and objective function granule in the form of equivalence classes. This thesis had put forward the concepts as the relationship between the individual variable and the objective function, granular fitness landscape and individual eigenvector which help one to understand and master the internal law of the problem. The internal law can be taken as the knowledge to describe the characteristic of the unsolved problem. Through the binary granular computing, the relationship between individual variable and the objective function can be obtained fast, and then the characteristic of the individual variable and the granular fitness landscape can be calculated to judge the type of the unsolved problem. In terms of the achieved knowledge to acquire the optimizing subspace, it can reduce the searching scope and enhance the optimization efficiency and precision.
Aiming at the complication of multi-objective problem, the application of KMMEA has been studied and discussed in the multi-objective optimization by the dominance relation and dominance classes in the information system. The solution space can be divided by the dominance relation of the information system and forms the evolution strategy which mines in the Pareto dominance space and searches in the rest space. The characteristic of subspace granule and objective function granule can be applied to maintain the population diversity in order to find the global and uniform-distributed Pareto optimum.
The thesis put forward dominance granule multi-objective sorting algorithm(DGSA). The dominance granule can be obtained by the dominance relation in the information system and granulation computing. It is the basis of multi-objective sorting and fitness assignment. Therefore, the dominance granule multi-objective sorting algorithm is designed and reduces the computational complexity highly.
Two important operators 'similar-taxis' and 'dissimilation' in the MEA are reconstructed based on knowledge discovered in evolution process. It enhances the intelligence of operator which can be used in macro-population evolution. This thesis introduced the mutation operator on the basis of knowledge guidance which is the specific application of granular fitness landscape and individual eigenvector. The mutation operator can enhance the algorithm convergence effectively and the optimization precision. Meantime, new individual generation method is researched according to the dominance-class model.
Furthermore, KMMEA's application in engineering is researched. KMMEA was used in neural network optimization in the multi-sensor fusion system and nonlinear updating of voltage-controlled oscillator in the linear frequency modulation radar level meter. It can remarkably improve the frequency-modulation linearity of voltage-controlled oscillator and depress the influence of spectrum spread and phrase noise to distance resolution. KMMEA enhances the signal-to-noise ratio of measurement signals. The application has been granted by the national invention patent(patent No. 200410092447.2) .
The main idea of the thesis is to research the application of knowledge discovery technique in the evolution process, guide the evolution process by the discovered knowledge and form the hybrid intelligent computing system.
(1)在深入分析基本思维进化算法的基础上，提出基于知识的多智能体思维进化算法。算法以智能体为思维活动的载体，将粗糙集和粒度计算作为智能体进行知识发现、推理和决策的工具，并结合具体的进化算子来实现智能群体的信念、愿望和意图，形成具有粒度层次结构的多智能体进化系统。
(2)通过粗糙集的相关概念，将解空间和目标函数空间采用等价类的形式进行划分，形成子空间粒和目标函数粒。提出了个体变量与目标函数关系、粒度适应度景象和个体特征向量的概念，有助于从宏观上认识和掌握被求解问题的内在规律，将其作为知识来描述被求解问题的特征。通过二进制粒计算，快速获取个体变量与目标函数关系，然后计算出个体特征向量和粒度适应度景象，判断被求解问题的类型。利用所获取的知识求取寻优子空间，以缩小搜索范围，提高寻优效率和精度。
(3)针对多目标问题的复杂性，利用信息系统中的优势关系和优势类的概念，研究并讨论了KMMEA在多目标优化问题中的应用。利用信息系统的优劣关系对解空间进行划分，形成了在Pareto优势空间进行“开采”而在其余空间中“探索”的进化策略。利用子空间粒和目标函数粒的特征进行种群多样性的维护，以期找到全面和分布均匀的Pareto最优解。
(4)提出了基于优势粒粒度多目标快速排序算法。利用信息系统的优势关系和粒计算的方法获取优势粒，将优势粒粒度作为多目标排序和适应度赋值的依据，并设计了基于优势粒的快速排序算法，可极大地降低排序运算量。
(5)对思维进化中的两个重要算子“趋同”和“异化”进行了基于知识的改造，提高了算子操作的智能性，用于宏群体进化。提出了基于知识指导的变异算子，是粒度适应度景象和个体特征向量的具体运用。该算子能有效提高算法的收敛效率和寻优精度。同时研究了按照优秀模式类产生新个体的方法。
(6)探索KMMEA在工程领域中的应用。将KMMEA用于多传感器信息融合系统中神经网络的优化，提高测量结果的准确性和稳定性。将KMMEA用于线性调频连续波雷达物位计压控振荡器的非线性校正，能够显著提高系统的调频线性度，降低频谱展宽和相位噪声对距离分辨率的影响，有效提高测量信号的信噪比。这一应用已申报国家发明专利“线性调频雷达物位计非线性进化校正方法”(专利号：200410092447．2)，已经得到授权。
本文的主线是研究知识发现技术在进化过程中的应用，通过所发现的知识指导进化过程，形成混合智能计算系统。
As a new generation of intelligent computing methods, evolution computing, rough set and granular computing have not only made the remarkable progress in the fields of their own, but also the fusion of them will powerfully improve the development and application in the soft intelligent information processing technique. Mind evolutionary algorithm(MEA) belongs to evolution computing and it imitates the thought mechanism of mankind. However MEA does not form the explicit knowledge carrier, knowledge processing system and thinking model. It does not embody the essential trait of people's thinking activities. From intelligent computing point of view this thesis introduces the intelligent agent and knowledge discovery technique into the evolution computing and forms a hybrid intelligent system (HIS). The generated data in the evolution process can be regarded as the knowledge database. Knowledge in the unsolved problem can be acquired through the knowledge discovery based on rough set theory and granular computing theory. It forms the knowledge-based multi-agent mind evolutionary algorithm (KMMEA). The main work and the innovative achievements of this thesis can be concluded as follows:
On the basis of the basic mind evolutionary algorithm, the knowledge-based multi-agent mind evolutionary algorithm was put forward. The KMMEA takes the agent as the carrier of the thinking activity. As agents rough set and granular computing are used as the tools to discover the knowledge, to inference and to make the decision. Furthermore, the specific evolution operators are applied to realize the belief, the desire and the intention of the agent, which form the multi-agent evolution system with the granular-hierarchy structure.
Through the concepts in rough set the solution space and the objective function spaces can be divided into the subspace granule and objective function granule in the form of equivalence classes. This thesis had put forward the concepts as the relationship between the individual variable and the objective function, granular fitness landscape and individual eigenvector which help one to understand and master the internal law of the problem. The internal law can be taken as the knowledge to describe the characteristic of the unsolved problem. Through the binary granular computing, the relationship between individual variable and the objective function can be obtained fast, and then the characteristic of the individual variable and the granular fitness landscape can be calculated to judge the type of the unsolved problem. In terms of the achieved knowledge to acquire the optimizing subspace, it can reduce the searching scope and enhance the optimization efficiency and precision.
Aiming at the complication of multi-objective problem, the application of KMMEA has been studied and discussed in the multi-objective optimization by the dominance relation and dominance classes in the information system. The solution space can be divided by the dominance relation of the information system and forms the evolution strategy which mines in the Pareto dominance space and searches in the rest space. The characteristic of subspace granule and objective function granule can be applied to maintain the population diversity in order to find the global and uniform-distributed Pareto optimum.
The thesis put forward dominance granule multi-objective sorting algorithm(DGSA). The dominance granule can be obtained by the dominance relation in the information system and granulation computing. It is the basis of multi-objective sorting and fitness assignment. Therefore, the dominance granule multi-objective sorting algorithm is designed and reduces the computational complexity highly.
Two important operators 'similar-taxis' and 'dissimilation' in the MEA are reconstructed based on knowledge discovered in evolution process. It enhances the intelligence of operator which can be used in macro-population evolution. This thesis introduced the mutation operator on the basis of knowledge guidance which is the specific application of granular fitness landscape and individual eigenvector. The mutation operator can enhance the algorithm convergence effectively and the optimization precision. Meantime, new individual generation method is researched according to the dominance-class model.
Furthermore, KMMEA's application in engineering is researched. KMMEA was used in neural network optimization in the multi-sensor fusion system and nonlinear updating of voltage-controlled oscillator in the linear frequency modulation radar level meter. It can remarkably improve the frequency-modulation linearity of voltage-controlled oscillator and depress the influence of spectrum spread and phrase noise to distance resolution. KMMEA enhances the signal-to-noise ratio of measurement signals. The application has been granted by the national invention patent(patent No. 200410092447.2) .
The main idea of the thesis is to research the application of knowledge discovery technique in the evolution process, guide the evolution process by the discovered knowledge and form the hybrid intelligent computing system.
引文
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