基于协同进化的混合智能优化算法及其应用研究
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摘要
优化问题一直是国际上公认的热点和难点问题之一。受自然现象、社会现象或生物智能的启发,计算智能提供了简单、通用、鲁棒、并行的方法,能够有效解决大多数优化问题。面对当今社会日益增多的复杂优化问题,传统的智能优化方法尚存在诸多的局限性,现有混合智能优化的混合机制(策略)上都有待深入,混合智能优化的内在联系与内部机理的研究尚显不足。作为一类将协同进化机制引入到传统计算智能的新算法,协同进化具有抽象的算法模型,可以根据实际求解问题来灵活构造。由于协同进化算法能有效克服其它计算智能算法的早熟现象、优化精度不高等缺陷,因此研究基于协同进化的混合智能优化算法已成为人工智能领域的一个研究热点。
本文在概要介绍计算智能方法相关知识的基础上,重点分析协同进化算法、混合智能优化算法的发展现状,以及混合智能优化算法在旅行商问题、客运量预测和故障诊断中的应用研究现状。围绕混合智能优化算法存在的问题,开展了以下研究工作：
(1)通过对协同进化策略和现有智能优化算法特性的反思,将协同进化模式和并行进化机制引入到遗传算法和粒子群算法中,建立一种并行协同进化(PCEGP)算法。该算法按个体适应度值的大小将整个种群划分为相等的2个子种群,在每次迭代中,个体适应度度值较好的子种群利用遗传算法进化,个体适应度值较差的子种群利用粒子群算法处理,然后求出整个种群迄今为止搜索到的最优解。理论分析证明,PCEGP算法在有界封闭和连续的条件下以概率1收敛于全局最优解。
(2)针对RBF神经网络结构和参数优化问题,利用PCEGP算法来优化RBF神经网络,提出一种高性能的并行混合智能优化(PHIO)算法。该算法通过设计一个开关函数把RBF神经网络优化转化成单纯的函数优化问题,利用PCEGP算法寻求全局最优解,以此构造出高性能的PHIO算法。通过函数优化测试,实验表明PHIO算法具有收敛速度快、全局搜索能力强、稳定性好、求解精度高的特点。
(3)基于“阶段混合”的思想,提出一种基于PCEGP算法和蚁群算法的两阶段混合智能优化(TSHIO)算法。该算法将整个过程分为粗搜索和细搜索两个阶段,在时间效率上优于蚁群算法,在求精效率上优于PCEGP算法。通过旅行商问题,对TSHIO算法进行了仿真比较,结果表明该算法具有较好的收敛性、较高的求解精度、较强的全局搜索能力。
(4)基于混合智能优化策略,提出一种基于粗糙集理论和BP神经网络的混合智能优化(RSBPNN)算法,建立一种新的铁路客运量预测方法。RSBPNN算法利用粗糙集理论的知识约简能力,对样本进行预处理和约简,再确定网络输入层变量和神经元个数,利用神经网络强大的任意函数逼近能力、学习能力,建立基于RSBPNN算法的铁路客运量预测方法。在实际复杂问题应用中表明该方法预测我国铁路客运量结果接近于实际值。
(5)为了形成智能优化算法相互融合与互补,以混合智能优化策略为基础,提出一种基于粗糙集、遗传算法和神经网络的新故障智能诊断(RGBNFD)方法。该方法充分利用了粗糙集知识约简能力,遗传算法保持种群多样性,BP神经网络的分类能力。以电机滚动轴承故障诊断为应用来验证提出的RGBNFD方法,结果表明RGBNFD方法不仅能有效的求解故障诊断,而且诊断准确率高,具有一定的容错能力。
The optimization problem has always been recognized as one of the most difficult but important problems. Inspired by the natural phenomena, social phenomena or biological intelligence, computational intelligence provides simple, versatile, robust and parallel methods that can effectively solve most of the optimization problems. In response to an increasing number of complex optimization problems in morden society, traditional intelligent optimization methods have many limitations, we need know more about hybrid intelligent optimization in hybrid mechanism and strategies, and there is no thorough study of inherent relationship and inner machanism in combining intelligent optimization algorithms. As a new algorithm introducing co-evolution into traditional computational intelligence, co-evolution provides an abstraction algorithmic model and can be flexibly constructed according to the real problems to be solved. Because co-evolutionary algorithm can effectively overcome the problems of premature phenomenon and low optimization precision etc., hybrid intelligent optimization algorithms based co-evolution has recently become a hot research topic in the field of artificial intelligence.
In introducing the relative knowledge of computational intelligence methods, this thesis mainly analyzes the state-of-art of the co-evolutionary algorithm and hybrid intelligent optimization algorithm, and recent applications of the hybrid intelligent optimization algorithm to the Traveling Salesman Problem (TSP), the railway passenger volume forecasting and fault diagnosis. Focused on the existing problems, this thesis has achieved the following research work:
(1) In reflection of the co-evolutionary strategies and the characteristics of existing intelligent optimization algorithms, a parallel co-evolutionary (PCEGP) algorithm is proposed by introducing the co-evolutionary mode and parallel evolution mechanism into genetic algorithm and particle swarm optimization. The PCEGP algorithm divides the individuals into two equal-sized groups according to their fitness values. The subgroup of the top fitness values is evolved by GA and the other subgroup is evolved by the PSO algorithm. The optimal solution is found out in whole group. The theoretical analysis proves that the PCEGP algorithm can100percent converge to the global optimal solution under conditions of the bounded closure and continuousness.
(2) To optimize the structure and parameters of RBF neural network, a parallel hybrid intelligence optimization (PHIO) algorithm based on PCEGP algorithm and RBFNN is proposed. By designing a switching function, the RBFNN optimization is translated into a simple function optimization problem, the PCEGP algorithm is then used to find the global optimal solution and thus PHIO algorithm with high performance is constructed. The experiments with the given function optimization show the PHIO algorithm has the characteristics of the quick convergence, strong global search ability, good stability and high solving accuracy.
(3) Based on phased hybrid intelligence, a two-stage hybrid intelligent optimization (TSHIO) algorithm based on PCEGP and ACO is proposed. The whole process of the TSHIO algorithm is divided into the rough searching and the detailed searching. The TSHIO algorithm is better than ACO in time efficiency and PCEGP in the refining efficiency. Various scale TSPs are tested to validate the effectiveness of the TSHIO algorithm, and the simulation results indicate that the TSHIO algorithm has better convergence, higher accuracy and stronger global search ability.
(4) On the basis of hybrid intelligent optimization strategy, a hybrid intelligent optimization (RSBPNN) algorithm based on rough set and BP neural network is proposed, and a new railway passenger volume forecast method based on RSBPNN algorithm is presented. The RSBPNN algorithm uses the knowledge reduction ability of rough set to deal with and reduce the staple data in order to determine input layer variables and the number of neurons of BPNN. Then arbitrary function approximation and learning ability of BPNN is used to construct the railway passenger volume forecast method. The experiments show that the forecast results are closer to the real statistical values.
(5) In order to form the fusion and complementary of the intelligent optimization algorithms, a new intelligent fault diagnosis (RGBNFD) method based on combining hybrid intelligent optimization strategy, rough set, genetic algorithm and neural network is proposed. The RGBNFD method takes full advantage of the knowledge reduction ability of rough set, the maintaining population diversity of genetic algorithm and the classification ability of BP neural network. In order to verify the effectiveness of the proposed RGBNFD method, the RGBNFD method is used to diagnose the motor rolling bearing fault. The results show that the RGBNFD method can not only effectively solve the fault diagnosis, but also obtain high accuracy rate, and take on the certain fault tolerance ability.
本文在概要介绍计算智能方法相关知识的基础上,重点分析协同进化算法、混合智能优化算法的发展现状,以及混合智能优化算法在旅行商问题、客运量预测和故障诊断中的应用研究现状。围绕混合智能优化算法存在的问题,开展了以下研究工作：
(1)通过对协同进化策略和现有智能优化算法特性的反思,将协同进化模式和并行进化机制引入到遗传算法和粒子群算法中,建立一种并行协同进化(PCEGP)算法。该算法按个体适应度值的大小将整个种群划分为相等的2个子种群,在每次迭代中,个体适应度度值较好的子种群利用遗传算法进化,个体适应度值较差的子种群利用粒子群算法处理,然后求出整个种群迄今为止搜索到的最优解。理论分析证明,PCEGP算法在有界封闭和连续的条件下以概率1收敛于全局最优解。
(2)针对RBF神经网络结构和参数优化问题,利用PCEGP算法来优化RBF神经网络,提出一种高性能的并行混合智能优化(PHIO)算法。该算法通过设计一个开关函数把RBF神经网络优化转化成单纯的函数优化问题,利用PCEGP算法寻求全局最优解,以此构造出高性能的PHIO算法。通过函数优化测试,实验表明PHIO算法具有收敛速度快、全局搜索能力强、稳定性好、求解精度高的特点。
(3)基于“阶段混合”的思想,提出一种基于PCEGP算法和蚁群算法的两阶段混合智能优化(TSHIO)算法。该算法将整个过程分为粗搜索和细搜索两个阶段,在时间效率上优于蚁群算法,在求精效率上优于PCEGP算法。通过旅行商问题,对TSHIO算法进行了仿真比较,结果表明该算法具有较好的收敛性、较高的求解精度、较强的全局搜索能力。
(4)基于混合智能优化策略,提出一种基于粗糙集理论和BP神经网络的混合智能优化(RSBPNN)算法,建立一种新的铁路客运量预测方法。RSBPNN算法利用粗糙集理论的知识约简能力,对样本进行预处理和约简,再确定网络输入层变量和神经元个数,利用神经网络强大的任意函数逼近能力、学习能力,建立基于RSBPNN算法的铁路客运量预测方法。在实际复杂问题应用中表明该方法预测我国铁路客运量结果接近于实际值。
(5)为了形成智能优化算法相互融合与互补,以混合智能优化策略为基础,提出一种基于粗糙集、遗传算法和神经网络的新故障智能诊断(RGBNFD)方法。该方法充分利用了粗糙集知识约简能力,遗传算法保持种群多样性,BP神经网络的分类能力。以电机滚动轴承故障诊断为应用来验证提出的RGBNFD方法,结果表明RGBNFD方法不仅能有效的求解故障诊断,而且诊断准确率高,具有一定的容错能力。
The optimization problem has always been recognized as one of the most difficult but important problems. Inspired by the natural phenomena, social phenomena or biological intelligence, computational intelligence provides simple, versatile, robust and parallel methods that can effectively solve most of the optimization problems. In response to an increasing number of complex optimization problems in morden society, traditional intelligent optimization methods have many limitations, we need know more about hybrid intelligent optimization in hybrid mechanism and strategies, and there is no thorough study of inherent relationship and inner machanism in combining intelligent optimization algorithms. As a new algorithm introducing co-evolution into traditional computational intelligence, co-evolution provides an abstraction algorithmic model and can be flexibly constructed according to the real problems to be solved. Because co-evolutionary algorithm can effectively overcome the problems of premature phenomenon and low optimization precision etc., hybrid intelligent optimization algorithms based co-evolution has recently become a hot research topic in the field of artificial intelligence.
In introducing the relative knowledge of computational intelligence methods, this thesis mainly analyzes the state-of-art of the co-evolutionary algorithm and hybrid intelligent optimization algorithm, and recent applications of the hybrid intelligent optimization algorithm to the Traveling Salesman Problem (TSP), the railway passenger volume forecasting and fault diagnosis. Focused on the existing problems, this thesis has achieved the following research work:
(1) In reflection of the co-evolutionary strategies and the characteristics of existing intelligent optimization algorithms, a parallel co-evolutionary (PCEGP) algorithm is proposed by introducing the co-evolutionary mode and parallel evolution mechanism into genetic algorithm and particle swarm optimization. The PCEGP algorithm divides the individuals into two equal-sized groups according to their fitness values. The subgroup of the top fitness values is evolved by GA and the other subgroup is evolved by the PSO algorithm. The optimal solution is found out in whole group. The theoretical analysis proves that the PCEGP algorithm can100percent converge to the global optimal solution under conditions of the bounded closure and continuousness.
(2) To optimize the structure and parameters of RBF neural network, a parallel hybrid intelligence optimization (PHIO) algorithm based on PCEGP algorithm and RBFNN is proposed. By designing a switching function, the RBFNN optimization is translated into a simple function optimization problem, the PCEGP algorithm is then used to find the global optimal solution and thus PHIO algorithm with high performance is constructed. The experiments with the given function optimization show the PHIO algorithm has the characteristics of the quick convergence, strong global search ability, good stability and high solving accuracy.
(3) Based on phased hybrid intelligence, a two-stage hybrid intelligent optimization (TSHIO) algorithm based on PCEGP and ACO is proposed. The whole process of the TSHIO algorithm is divided into the rough searching and the detailed searching. The TSHIO algorithm is better than ACO in time efficiency and PCEGP in the refining efficiency. Various scale TSPs are tested to validate the effectiveness of the TSHIO algorithm, and the simulation results indicate that the TSHIO algorithm has better convergence, higher accuracy and stronger global search ability.
(4) On the basis of hybrid intelligent optimization strategy, a hybrid intelligent optimization (RSBPNN) algorithm based on rough set and BP neural network is proposed, and a new railway passenger volume forecast method based on RSBPNN algorithm is presented. The RSBPNN algorithm uses the knowledge reduction ability of rough set to deal with and reduce the staple data in order to determine input layer variables and the number of neurons of BPNN. Then arbitrary function approximation and learning ability of BPNN is used to construct the railway passenger volume forecast method. The experiments show that the forecast results are closer to the real statistical values.
(5) In order to form the fusion and complementary of the intelligent optimization algorithms, a new intelligent fault diagnosis (RGBNFD) method based on combining hybrid intelligent optimization strategy, rough set, genetic algorithm and neural network is proposed. The RGBNFD method takes full advantage of the knowledge reduction ability of rough set, the maintaining population diversity of genetic algorithm and the classification ability of BP neural network. In order to verify the effectiveness of the proposed RGBNFD method, the RGBNFD method is used to diagnose the motor rolling bearing fault. The results show that the RGBNFD method can not only effectively solve the fault diagnosis, but also obtain high accuracy rate, and take on the certain fault tolerance ability.
引文
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