改进的粒子群优化算法研究及其若干应用
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摘要
化学工业在国民经济领域中始终占有极其重要地位,它不仅牵涉千家万户的日常生活,还影响着其它行业甚至国防等重要领域。由于现代化的化工过程存在着许多复杂性,而传统的优化方法已无法解决其中的诸多问题,因此智能的建模和优化方法倍受关注。本文针对合成氨、德士古气化以及甲醇合成等复杂的煤化工过程,研究了神经网络(NN)的智能建模方法以及粒子群优化算法(PSO)进化方法,并将提出的新方法应用于煤化工过程软测量建模。此外,还针对物流集配的组合实际问题提出了一种新的智能技术。本文的主要研究成果如下：
(1)讨论了常见智能优化算法的分类和特点、粒子群优化算法及软测量建模的基本原理。着重介绍了粒子群优化算法的发展、算法改进和应用情况以及软测量技术的发展。回顾了合成氨、甲醇工业的发展现状、生产工艺及工作原理,并简述了企业内部物料配送过程特别是托盘集配问题,最后介绍了智能优化方法在这些领域的应用。
(2)为了克服粒子群优化算法(PSO)易于“早熟收敛”问题,提出一种自导式粒子群优化算法(Self-Government Particle Swarm Optimization,SGPSO)。在SGPSO中,粒子位置的更新不仅与粒子历史局部最优和粒子群全局最优的有关,而且与粒子在之前实验中所搜索到的局部最优位置信息有关,从而大大提高了算法的寻优能力。通过对典型测试函数的仿真结果表明,所提出的SGPSO和RSGPSO这2种新算法无论在收敛速度还是收敛精度上均优于标准PSO算法。将SGPSO和RSGPSO算法与BP神经网络结合,建立了基于SGPSO-NN的气化炉炉温软测量模型。模型结果表明,SGPSO-NN的测试误差较小,具有较强的泛化能力,能够满足实际生产中对气化炉炉温的测量要求。
(3)PSO的学习因子直接影响到算法的寻优能力,提出了学习因子的2种随机取值的调整方式：激进调整方式和保守调整方式,从而提出随机学习因子的PSO算法(RLFPSO)。为了进一步提高算法的性能,有效降低种群陷入局部最优的风险,提出了两类随机学习因子混沌粒子群优化算法(RLFPSOC1和RLFPSOC2)。两类算法分别在种群进化初期和后期引入混沌的遍历性特点,从而提高的算法的收敛速度和精度。通过经典函数来测试RLFPSO算法及RLFPSOC1和RLFPSOC2算法的性能,并和其它几种方法进行比较,结果表明RLFPSO算法优化经典连续函数的结果明显优于PSO算法；两种RLFPSOC算法的优化性能均较RLFPSO算法有了较大提升。最后,将基于RLFPSO和RLFPSOC的神经网络模型用于甲醇合成塔转化率的软测量建模,并与PSO-NN、 CenPSO-NN进行了比较。比较结果表明,基于RLFPSOC1-NN和RLFPSOC2-NN的甲醇合成转化率预测模型较其它几种方法具有更好的预测能力,能够较准确地估计甲醇合成塔的质量转化率。
(4)传统的粒子群算法存在较容易陷入局部极小点等缺陷。为改善算法性能,提出了一种新的粒子群优化算法——历史最优共享的粒子群优化算法(VSHBPSO)。 VSHBPSO算法的基本思想是粒子的更新不仅学习当前全局最优位置,而且向之前实验中搜索的全局历史最优位置学习。采用典型测试函数对VSHBPSO及其扩展形式VRSHBPSO、AVRSHBPSO进行仿真研究,并与标准PSO进行比较。仿真表明,基于历史最优共享的粒子群优化算法及其扩展算法对于低维、高维函数的优化问题均具有较好的适用性和有效性。同时。ARVSHPSO算法在3种改进PSO算法中的优化性能最好。将基于ARVSHPSO-NN的模型用于合成氨塔出口氨含量的估计,建立了相应的软测量模型。实验结果验证了其算法的可行性和有效性,可用于指导实际的生产过程。
(5)针对实际的物料集配问题,提出了一种新的物料分组托盘建模方式,并计算了物料集配问题的复杂度,提出了一种新的离散型粒子群优化算法。在离散PSO算法中,子代个体向个体历史最优、全局历史最优以及前次的全局最优个体学习,从而有效提高了算法性能。离散型PSO算法采用适用于托盘分组集配问题的0-1编码方式,并提出了几种个体互相学习的更新策略。为了验证算法解决物料集配问题的有效性,选取两个不同维数的实际问题进行模拟测试,找出了最优的集配方案。测试结果表明,相比于其它几种方法,提出的离散型PSO算法具有更好的寻优性能,可嵌入物流管理信息系统,对物料进行自动分组集配,实现制造业的快速物料集配。
Chemical industry is very significant in the national economy, which has respect to our daily life as well as other industries even national security. Nowadays, the complication of the chemical processes results in the inefficiency of the traditional optimization methods. Due to this, intelligent modeling and optimization methods begin to attract more and more attention. In this dissertation, for the complex coal chemical processes, such as ammonia synthesis, Texaco gasification, and methanol synthesis, the neural network (NN) based intelligent modeling and particle swarm optimization (PSO) methods are investigated, and the proposed novel methods are applied to soft sensor modeling in these processes. Furthermore, an intelligent technique is proposed to pallet grouping problem. The main results in this dissertation can be summarized as follows:
(1)The features of popular intelligent optimization algorithms are discussed as well as the principle of the particle swarm optimization and soft senor modeling. The development and improvement of PSO are underlined. Moreover, the processes of ammonia synthesis and methonal industy are reviewed, and then the material feeding especially the pallet grouping problem is outlined. Finally, the application of intelligent optimization algorithm in these areas is introduced.
(2)In order to overcome the premature convergence of PSO algorithm, a self-government particles swarm optimization (SGPSO) is proposed. In the SGPSO, the update of particle position not only depends on the particle personal best position currently and the swarm global best position found so far, but also is related to the local best information found in the previous experiments, thus greatly improving the optimization capability. The simulation results in the benchmark function tests indicate that the proposed SGPSO and RSGPSO algorithms are superior to the standard algorithm for convergence speed and acurracy. The SGPSO and RSGPSO are integrated with BP neural network, and SGPSO-NN based soft sensor model for gasifier temperature is established. The results of the model show that the soft sensor based on SGPSO-NN model provides small testing error and has good generalization capability, which is very suitable for measurement of gasifier temperature in the real-world application.
(3)The learning factors directly influence the optimizatioin capability of PSO, thus two random tuning rules of learning factors, i.e. radical tuning and conservative tuning, are presented. Based on the rules, the random learning factor particle swarm optimization (RLFPSO) is proposed. In order to further improve the performance of RLFPSO and reduce the risk of being trapped to local optima, two random learning facto particle swarm optimization algorithms with chaos (RLFPSOC1and RLFPSOC2) are proposed. In the two improved algorithms, the ergodicity of chaos is introduced at the prophase and anaphase of evolution, respectively. The performance of RLFPSO, RLFPSOC1and RLFPSOC2is tested by benchmark functions, and then compared with other methods. The test results reveal that RLFPSO outperforms PSO, and two RLFPSOC algorithms have further improvements on the basis of RLFPSO. Finally, RLFPSO and RLFPSOC based neural networks are used for soft-sensing the methanol conversion rate, and compared with PSO-NN and CenPSO-NN. The comparison results indicate that the prediction models of methanol conversion rate based on RLFPSOC1-NN and RLFPSOC2-NN have better prediction capability than other methods, and can provide accurate estimation of the methanol conversion rate.
(4) The traditional PSO algorithm has the drawback of falling into local minima easily. In order to improve the performance, a novel PSO algorithm——Velocity Share Historical Best Particle Swarm Optimization (VSHBPSO) is proposed. The basic idea of VSHBPSO is that the particle is attracted by the global historical best position searched in the former experiment as well as the current global best particle. The simulation studies on VSHBPSO and two variants VRSHBPSO, AVRSHBPSO are executed, and are compared with PSO. The simulation states that VSHBPSO and two variants are applicable and effective for both the low and high dimensional optimization problems. Meawhile, AVRSHBPSO performs best among the algorithms. The AVRSHBPSO-NN soft sensing model is applied to estimation of the ammonia concentration at the converter outlet. The experiment results verify the reliability and effectiveness of the proposed model, which is capable of providing the instruction in the real-world production.
(5) A new modeling method is presented to describe the pallet grouping problem, whose complexity is analyzed. a new learning algorithm, which is defined as Learning Algorithm Only from Excellent-Pbest, Gbest and Gpbest, for short LAOE～PGpG is developed to solve the pallet loading problem. A new encoding scheme namely0-1encoding is adopted in LAOE～PGpG, and several Inter-learning schemes are developed to update the individual. Two application cases with different dimensions are employed to verify the performance of the proposed algorithm, and the optimal pallet grouping is given. The results indicate that the proposed algorithm outperforms the compared methods, and can be integrated into the logistics management system for realizing the automatic collection and distribution.
(1)讨论了常见智能优化算法的分类和特点、粒子群优化算法及软测量建模的基本原理。着重介绍了粒子群优化算法的发展、算法改进和应用情况以及软测量技术的发展。回顾了合成氨、甲醇工业的发展现状、生产工艺及工作原理,并简述了企业内部物料配送过程特别是托盘集配问题,最后介绍了智能优化方法在这些领域的应用。
(2)为了克服粒子群优化算法(PSO)易于“早熟收敛”问题,提出一种自导式粒子群优化算法(Self-Government Particle Swarm Optimization,SGPSO)。在SGPSO中,粒子位置的更新不仅与粒子历史局部最优和粒子群全局最优的有关,而且与粒子在之前实验中所搜索到的局部最优位置信息有关,从而大大提高了算法的寻优能力。通过对典型测试函数的仿真结果表明,所提出的SGPSO和RSGPSO这2种新算法无论在收敛速度还是收敛精度上均优于标准PSO算法。将SGPSO和RSGPSO算法与BP神经网络结合,建立了基于SGPSO-NN的气化炉炉温软测量模型。模型结果表明,SGPSO-NN的测试误差较小,具有较强的泛化能力,能够满足实际生产中对气化炉炉温的测量要求。
(3)PSO的学习因子直接影响到算法的寻优能力,提出了学习因子的2种随机取值的调整方式：激进调整方式和保守调整方式,从而提出随机学习因子的PSO算法(RLFPSO)。为了进一步提高算法的性能,有效降低种群陷入局部最优的风险,提出了两类随机学习因子混沌粒子群优化算法(RLFPSOC1和RLFPSOC2)。两类算法分别在种群进化初期和后期引入混沌的遍历性特点,从而提高的算法的收敛速度和精度。通过经典函数来测试RLFPSO算法及RLFPSOC1和RLFPSOC2算法的性能,并和其它几种方法进行比较,结果表明RLFPSO算法优化经典连续函数的结果明显优于PSO算法；两种RLFPSOC算法的优化性能均较RLFPSO算法有了较大提升。最后,将基于RLFPSO和RLFPSOC的神经网络模型用于甲醇合成塔转化率的软测量建模,并与PSO-NN、 CenPSO-NN进行了比较。比较结果表明,基于RLFPSOC1-NN和RLFPSOC2-NN的甲醇合成转化率预测模型较其它几种方法具有更好的预测能力,能够较准确地估计甲醇合成塔的质量转化率。
(4)传统的粒子群算法存在较容易陷入局部极小点等缺陷。为改善算法性能,提出了一种新的粒子群优化算法——历史最优共享的粒子群优化算法(VSHBPSO)。 VSHBPSO算法的基本思想是粒子的更新不仅学习当前全局最优位置,而且向之前实验中搜索的全局历史最优位置学习。采用典型测试函数对VSHBPSO及其扩展形式VRSHBPSO、AVRSHBPSO进行仿真研究,并与标准PSO进行比较。仿真表明,基于历史最优共享的粒子群优化算法及其扩展算法对于低维、高维函数的优化问题均具有较好的适用性和有效性。同时。ARVSHPSO算法在3种改进PSO算法中的优化性能最好。将基于ARVSHPSO-NN的模型用于合成氨塔出口氨含量的估计,建立了相应的软测量模型。实验结果验证了其算法的可行性和有效性,可用于指导实际的生产过程。
(5)针对实际的物料集配问题,提出了一种新的物料分组托盘建模方式,并计算了物料集配问题的复杂度,提出了一种新的离散型粒子群优化算法。在离散PSO算法中,子代个体向个体历史最优、全局历史最优以及前次的全局最优个体学习,从而有效提高了算法性能。离散型PSO算法采用适用于托盘分组集配问题的0-1编码方式,并提出了几种个体互相学习的更新策略。为了验证算法解决物料集配问题的有效性,选取两个不同维数的实际问题进行模拟测试,找出了最优的集配方案。测试结果表明,相比于其它几种方法,提出的离散型PSO算法具有更好的寻优性能,可嵌入物流管理信息系统,对物料进行自动分组集配,实现制造业的快速物料集配。
Chemical industry is very significant in the national economy, which has respect to our daily life as well as other industries even national security. Nowadays, the complication of the chemical processes results in the inefficiency of the traditional optimization methods. Due to this, intelligent modeling and optimization methods begin to attract more and more attention. In this dissertation, for the complex coal chemical processes, such as ammonia synthesis, Texaco gasification, and methanol synthesis, the neural network (NN) based intelligent modeling and particle swarm optimization (PSO) methods are investigated, and the proposed novel methods are applied to soft sensor modeling in these processes. Furthermore, an intelligent technique is proposed to pallet grouping problem. The main results in this dissertation can be summarized as follows:
(1)The features of popular intelligent optimization algorithms are discussed as well as the principle of the particle swarm optimization and soft senor modeling. The development and improvement of PSO are underlined. Moreover, the processes of ammonia synthesis and methonal industy are reviewed, and then the material feeding especially the pallet grouping problem is outlined. Finally, the application of intelligent optimization algorithm in these areas is introduced.
(2)In order to overcome the premature convergence of PSO algorithm, a self-government particles swarm optimization (SGPSO) is proposed. In the SGPSO, the update of particle position not only depends on the particle personal best position currently and the swarm global best position found so far, but also is related to the local best information found in the previous experiments, thus greatly improving the optimization capability. The simulation results in the benchmark function tests indicate that the proposed SGPSO and RSGPSO algorithms are superior to the standard algorithm for convergence speed and acurracy. The SGPSO and RSGPSO are integrated with BP neural network, and SGPSO-NN based soft sensor model for gasifier temperature is established. The results of the model show that the soft sensor based on SGPSO-NN model provides small testing error and has good generalization capability, which is very suitable for measurement of gasifier temperature in the real-world application.
(3)The learning factors directly influence the optimizatioin capability of PSO, thus two random tuning rules of learning factors, i.e. radical tuning and conservative tuning, are presented. Based on the rules, the random learning factor particle swarm optimization (RLFPSO) is proposed. In order to further improve the performance of RLFPSO and reduce the risk of being trapped to local optima, two random learning facto particle swarm optimization algorithms with chaos (RLFPSOC1and RLFPSOC2) are proposed. In the two improved algorithms, the ergodicity of chaos is introduced at the prophase and anaphase of evolution, respectively. The performance of RLFPSO, RLFPSOC1and RLFPSOC2is tested by benchmark functions, and then compared with other methods. The test results reveal that RLFPSO outperforms PSO, and two RLFPSOC algorithms have further improvements on the basis of RLFPSO. Finally, RLFPSO and RLFPSOC based neural networks are used for soft-sensing the methanol conversion rate, and compared with PSO-NN and CenPSO-NN. The comparison results indicate that the prediction models of methanol conversion rate based on RLFPSOC1-NN and RLFPSOC2-NN have better prediction capability than other methods, and can provide accurate estimation of the methanol conversion rate.
(4) The traditional PSO algorithm has the drawback of falling into local minima easily. In order to improve the performance, a novel PSO algorithm——Velocity Share Historical Best Particle Swarm Optimization (VSHBPSO) is proposed. The basic idea of VSHBPSO is that the particle is attracted by the global historical best position searched in the former experiment as well as the current global best particle. The simulation studies on VSHBPSO and two variants VRSHBPSO, AVRSHBPSO are executed, and are compared with PSO. The simulation states that VSHBPSO and two variants are applicable and effective for both the low and high dimensional optimization problems. Meawhile, AVRSHBPSO performs best among the algorithms. The AVRSHBPSO-NN soft sensing model is applied to estimation of the ammonia concentration at the converter outlet. The experiment results verify the reliability and effectiveness of the proposed model, which is capable of providing the instruction in the real-world production.
(5) A new modeling method is presented to describe the pallet grouping problem, whose complexity is analyzed. a new learning algorithm, which is defined as Learning Algorithm Only from Excellent-Pbest, Gbest and Gpbest, for short LAOE～PGpG is developed to solve the pallet loading problem. A new encoding scheme namely0-1encoding is adopted in LAOE～PGpG, and several Inter-learning schemes are developed to update the individual. Two application cases with different dimensions are employed to verify the performance of the proposed algorithm, and the optimal pallet grouping is given. The results indicate that the proposed algorithm outperforms the compared methods, and can be integrated into the logistics management system for realizing the automatic collection and distribution.
引文
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