遗传算法与粒子群算法的改进及应用
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摘要
人们从自然界的自适应进化现象得到启示,生物体和自然生态系统可以通过自身的演化使许多在人类看起来高度复杂的问题得到比较完美的解,由此产生了与经典优化方法截然不同的新型智能计算方法——仿生智能计算。本文对遗传算法(GA)和粒子群算法(PSO)两种仿生智能计算方法的算法机理、算法改进和应用方面做了较为系统的研究。其中主要的研究内容和成果可以归纳如下:
标准遗传算法易陷入局部最优而出现早熟,在标准遗传算法中引进了捕食搜索策略,提出了基于捕食搜索策略的遗传算法。该算法在进化中模拟动物捕食搜索的过程,根据种群中个体最优适应值的变化来动态改变交叉和变异概率,从而加强了算法的全局搜索和局部优化的平衡能力。
粒子群算法早熟收敛的主要原因是种群多样性的丧失。为了定量分析种群多样性,提出了以种群成熟度作为种群多样性的测度,并运用模糊理论和方法给出了种群成熟度的模糊型指标和计算方法,根据种群成熟度的大小动态自适应的改变惯性权重,从而在提高粒子群算法的运行效率的同时预防早熟。将此算法和基于捕食搜索策略的遗传算法成功地应用到了重油热解模型参数估计中。
分析了粒子群算法中粒子速度的收敛性以及粒子速度对算法性能的影响,提出了运用种群平均速度动态调整惯性权重避免粒子速度提前接近于0而出现早熟的粒子群算法。
多种群策略是保持种群多样性避免早熟较好的方法,本文提出了一种多种群并行粒子群算法(DPPSO),将子种群分为探测型和开发型,并考虑子种群的拓扑结构,对于探测型种群采用全局模型PSO算法,以增大探测最优个体的能力;而开发型种群采用局部模型PSO算法,加强算法局部搜索能力,在局部范围内搜索全局最优解,同时保持了种群之间的信息交流。仿真结果表明DPPSO能在一定程度上保持了群体多样性,从而避免了早熟。
针对小世界网络模型具有较小平均路径长度和较大聚集系数的特点,将其引入粒子群算法的种群结构中,提出了动态种群结构的粒子群算法。由于网络模型的演化,使算法具有动态的种群结构,从而保持了种群的多样性。同时为了使粒子尽可能地分布在不同的搜索空间,在网络模型演化过程中考虑了节点的个体价值,另外为了加快算法的收敛速度,在进化后期采用全局模型粒子群算法。仿真结果表明了算法的有效性和实用性。
提出了一种基于PSO的动态聚类算法,对标准PSO在编码机制和操作过程上做了改进,并以DB Index准则作为聚类有效性的判断准则。将其应用于模糊模型辨识中,用动态聚类算法确定模糊规则模型的前提结构和参数,然后用最小二乘法求解模糊模型的结论参数。并将该方法成功应用于热工过程模糊辨识中。
将GA和PSO应用到径向基函数(RBF)神经网络的学习中。首先将变长度染色体遗传算法应用于RBF神经网络的训练,并与最小二乘法相结合,同时确定网络的结构和中心参数,用此方法建立了热电厂热负荷预测模型,并与BP网络和增长型结构学习算法RBF网络模型进行了比较,其模型精度有明显地提高。然后研究了两种基于PSO的RBF网络学习算法,一种是先利用减聚类算法确定网络的隐层单元数,再用PSO对中心参数进行优化,并与最小二乘法相结合来训练RBF网络;另一种是借鉴递阶遗传算法的编码原理,在PSO中引进了控制基因,并与最小二乘法相结合同时确定网络的拓扑结构和中心参数。结果表明,两种算法是有效的。
最后,对全文进行了总结,指出了未来进一步研究的方向。
It is learning from the phenomena of adaptive optimization in the nature that creatures and natural ecological systems make the optimization problems with high degree of complexty solved perfectly. Under the background, biologically-inspired intelligent computations which are different from the classed optimization methods has emerged. The paper gives a comprehensive study on genetic algorithm (GA) and particle swarm optimization (PSO) from the aspects of algorithm mechanism, modifications and their applications. The main contents and contributions given in this dissertetion are as follows:
To improve performance of genetic algorithm and avoid trapping to local optima, a new genetic algorithm based on predatory search strategy is proposed. It simulates the animal predatory search in running, and adjusts the crossover and mutation probability by the best individual fitness in every generation, so can balance the ability of global exploring and local search. The effectiveness and practicability are demonstrated by the simulation results.
The reason of premature convengence is large lost in population diversity. The measure of population diversity for measuring poplation maturing degree is presented and its calculation is given on the basis fuzzy system theory. Finally, the method of adaptively adjusting inertia weight with poplation maturing degree is proposed to prevent premature convegence. The algorithm and genetic algorithm based on predatory search strategy have been applied successfully to the parameter estimation of heavy oil thermal cracking model.
The convengence of particle velocity in PSO and effect on optimization performances are analyzed. A new PSO is proposed with dynamical adjust inertia weight using information defined as the average absolute value of velocity of all of the particles, which can avoid premature convengence for the velocity is closed to 0 in the early search part.
To improve performance of PSO algorithm and avoid trapping to local minima, a multi-population parallel particle swarm optimization (DPPSO) algorithm is proposed. In the algorithm, sub populations are divided into exploration and exploitation types. The global version PSO is used in the exploration population to enhance ability of exploring the best individual, and the local version PSO is used in the exploitation population to enhance ability of local search and find the best global result in the local range. Simultaneously, keep communication with sub populations in running. The experimental results show that the restraining premature convergence is enhanced for maintaining the individual diversity.
A new dynamic population structure PSO is proposed, in which the small world network model is introduced into population structure, and the topology structure is dynamical with the evolvement of small world network. So the population diversity is enhanced. The individual value is applied to evolution of small world network for particles can distribute in different search space. In order to perform a better local search, a global PSO version is used in the end of the search. The effectiveness and practicability is demonstrated by the simulation results.
A dynamic clustering algorithm based on PSO is proposed, in which a novel coding and operation on the basis of standard PSO is introduced and DB Index rule is used to determine the validity of clustering. The proper fuzzy rule number and exact premise parameters can be obtained by using the dynamic clustering algorithm to identify fuzzy models, and result parameters by the least squared method. It is used in fuzzy modeling for thermal processes.
GA and PSO are used in training radial basis (RBF) function network. Firstly, a new RBF network algorithm is introduced by combining genetic algorithm of chromosomes with changeable length and least-square method which can determine the structure and parameters of network. The algorithm is used to model heat loading forecasting for co-generation power plants and compares with BP neural network and RBF neural network based on a trainning algorithm of automatic increase in hidden nodes, the simulation results are satisfied. Second, two methods of RBF neural network algorithm based on PSO are proposed. First is to determine units’number in RBF layer using subtractive clustering method, and optimize central position and directional width based on PSO algorithm, and then train RBF neural network combining with least-square method. Second is to introduce a control gene into standard PSO, and determine network structure and parameters, such as centers and widths of hidden units by combining with least square method. Simulation results illustrate their efficiency.
Finally, the whole research contents are summarized, and further research directions are indicated.
标准遗传算法易陷入局部最优而出现早熟,在标准遗传算法中引进了捕食搜索策略,提出了基于捕食搜索策略的遗传算法。该算法在进化中模拟动物捕食搜索的过程,根据种群中个体最优适应值的变化来动态改变交叉和变异概率,从而加强了算法的全局搜索和局部优化的平衡能力。
粒子群算法早熟收敛的主要原因是种群多样性的丧失。为了定量分析种群多样性,提出了以种群成熟度作为种群多样性的测度,并运用模糊理论和方法给出了种群成熟度的模糊型指标和计算方法,根据种群成熟度的大小动态自适应的改变惯性权重,从而在提高粒子群算法的运行效率的同时预防早熟。将此算法和基于捕食搜索策略的遗传算法成功地应用到了重油热解模型参数估计中。
分析了粒子群算法中粒子速度的收敛性以及粒子速度对算法性能的影响,提出了运用种群平均速度动态调整惯性权重避免粒子速度提前接近于0而出现早熟的粒子群算法。
多种群策略是保持种群多样性避免早熟较好的方法,本文提出了一种多种群并行粒子群算法(DPPSO),将子种群分为探测型和开发型,并考虑子种群的拓扑结构,对于探测型种群采用全局模型PSO算法,以增大探测最优个体的能力;而开发型种群采用局部模型PSO算法,加强算法局部搜索能力,在局部范围内搜索全局最优解,同时保持了种群之间的信息交流。仿真结果表明DPPSO能在一定程度上保持了群体多样性,从而避免了早熟。
针对小世界网络模型具有较小平均路径长度和较大聚集系数的特点,将其引入粒子群算法的种群结构中,提出了动态种群结构的粒子群算法。由于网络模型的演化,使算法具有动态的种群结构,从而保持了种群的多样性。同时为了使粒子尽可能地分布在不同的搜索空间,在网络模型演化过程中考虑了节点的个体价值,另外为了加快算法的收敛速度,在进化后期采用全局模型粒子群算法。仿真结果表明了算法的有效性和实用性。
提出了一种基于PSO的动态聚类算法,对标准PSO在编码机制和操作过程上做了改进,并以DB Index准则作为聚类有效性的判断准则。将其应用于模糊模型辨识中,用动态聚类算法确定模糊规则模型的前提结构和参数,然后用最小二乘法求解模糊模型的结论参数。并将该方法成功应用于热工过程模糊辨识中。
将GA和PSO应用到径向基函数(RBF)神经网络的学习中。首先将变长度染色体遗传算法应用于RBF神经网络的训练,并与最小二乘法相结合,同时确定网络的结构和中心参数,用此方法建立了热电厂热负荷预测模型,并与BP网络和增长型结构学习算法RBF网络模型进行了比较,其模型精度有明显地提高。然后研究了两种基于PSO的RBF网络学习算法,一种是先利用减聚类算法确定网络的隐层单元数,再用PSO对中心参数进行优化,并与最小二乘法相结合来训练RBF网络;另一种是借鉴递阶遗传算法的编码原理,在PSO中引进了控制基因,并与最小二乘法相结合同时确定网络的拓扑结构和中心参数。结果表明,两种算法是有效的。
最后,对全文进行了总结,指出了未来进一步研究的方向。
It is learning from the phenomena of adaptive optimization in the nature that creatures and natural ecological systems make the optimization problems with high degree of complexty solved perfectly. Under the background, biologically-inspired intelligent computations which are different from the classed optimization methods has emerged. The paper gives a comprehensive study on genetic algorithm (GA) and particle swarm optimization (PSO) from the aspects of algorithm mechanism, modifications and their applications. The main contents and contributions given in this dissertetion are as follows:
To improve performance of genetic algorithm and avoid trapping to local optima, a new genetic algorithm based on predatory search strategy is proposed. It simulates the animal predatory search in running, and adjusts the crossover and mutation probability by the best individual fitness in every generation, so can balance the ability of global exploring and local search. The effectiveness and practicability are demonstrated by the simulation results.
The reason of premature convengence is large lost in population diversity. The measure of population diversity for measuring poplation maturing degree is presented and its calculation is given on the basis fuzzy system theory. Finally, the method of adaptively adjusting inertia weight with poplation maturing degree is proposed to prevent premature convegence. The algorithm and genetic algorithm based on predatory search strategy have been applied successfully to the parameter estimation of heavy oil thermal cracking model.
The convengence of particle velocity in PSO and effect on optimization performances are analyzed. A new PSO is proposed with dynamical adjust inertia weight using information defined as the average absolute value of velocity of all of the particles, which can avoid premature convengence for the velocity is closed to 0 in the early search part.
To improve performance of PSO algorithm and avoid trapping to local minima, a multi-population parallel particle swarm optimization (DPPSO) algorithm is proposed. In the algorithm, sub populations are divided into exploration and exploitation types. The global version PSO is used in the exploration population to enhance ability of exploring the best individual, and the local version PSO is used in the exploitation population to enhance ability of local search and find the best global result in the local range. Simultaneously, keep communication with sub populations in running. The experimental results show that the restraining premature convergence is enhanced for maintaining the individual diversity.
A new dynamic population structure PSO is proposed, in which the small world network model is introduced into population structure, and the topology structure is dynamical with the evolvement of small world network. So the population diversity is enhanced. The individual value is applied to evolution of small world network for particles can distribute in different search space. In order to perform a better local search, a global PSO version is used in the end of the search. The effectiveness and practicability is demonstrated by the simulation results.
A dynamic clustering algorithm based on PSO is proposed, in which a novel coding and operation on the basis of standard PSO is introduced and DB Index rule is used to determine the validity of clustering. The proper fuzzy rule number and exact premise parameters can be obtained by using the dynamic clustering algorithm to identify fuzzy models, and result parameters by the least squared method. It is used in fuzzy modeling for thermal processes.
GA and PSO are used in training radial basis (RBF) function network. Firstly, a new RBF network algorithm is introduced by combining genetic algorithm of chromosomes with changeable length and least-square method which can determine the structure and parameters of network. The algorithm is used to model heat loading forecasting for co-generation power plants and compares with BP neural network and RBF neural network based on a trainning algorithm of automatic increase in hidden nodes, the simulation results are satisfied. Second, two methods of RBF neural network algorithm based on PSO are proposed. First is to determine units’number in RBF layer using subtractive clustering method, and optimize central position and directional width based on PSO algorithm, and then train RBF neural network combining with least-square method. Second is to introduce a control gene into standard PSO, and determine network structure and parameters, such as centers and widths of hidden units by combining with least square method. Simulation results illustrate their efficiency.
Finally, the whole research contents are summarized, and further research directions are indicated.
引文
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