混合免疫智能优化算法研究及其在复杂系统中的应用
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摘要
人工免疫系统模拟生物免疫系统进化行为的智能特征,具有自组织、自学习能力,具有解决复杂优化问题的优点。现代工业系统变得越来越复杂,而复杂系统的建模、优化与控制需要高性能的算法来辅助,依靠单一模式的优化方法难以满足系统性能要求。混合免疫智能处理技术为这类问题提供了有效的途径,同时也是人工免疫系统研究的发展方向。
基于免疫系统的机理,深入挖掘生物免疫系统中蕴含的智能学习机制并结合其它智能处理方法的优点,本文研究了几类混合免疫智能优化算法及其相关应用,从算法理论、算法设计、性能测试、比较分析到实际应用展开一系列工作。在理论上研究了四类混合免疫智能优化方法,并通过实验仿真验证了算法的有效性；在应用上研究了混沌系统自抗扰优化控制与永磁同步电机系统多参数辨识这两类典型的复杂系统,并获得了良好的控制效果和辨识结果。概括如下：
1.引入生态学中的协同进化Lotka-Volterra思想到人工免疫算法中,考虑了群体间的竞争合作关系,构造了一种竞争合作型协同进化免疫克隆选择模型。各子种群内部通过局部最优免疫优势、克隆扩增和动态高频变异等相关算子操作。运用信息熵理论改善种群多样性,所有子种群共享经过免疫杂交提升操作的高层记忆库,通过迁移操作实现整个种群信息共享与协同进化。
2.为了扩大解的搜索空间,将粒子群体分为捕食与探索两种模态,建立一种免疫双态粒子群优化方法。对处于捕食状态的精英粒子采用精英学习策略；对处于探索状态的微粒采用探索策略；对微粒个体极值进行免疫克隆优化；对不活跃个体进行免疫受体编辑。算法兼顾了抑制早熟停滞现象和避免冗余迭代。
3.融合免疫系统优化原理、协同进化思想及粒子群的邻域信息,构建免疫协同粒子群进化模型。算法采用并行计算框架,整个群体由记忆种群与若干个普通种群构成。普通微粒种群内部通过精英粒子保留、免疫网络及柯西变异等混合策略共同演化新个体；微粒个体极值采用自适应小波学习以加快收敛速度；免疫克隆选择算法对记忆库进行精细搜索；信息交互机制促进信息共享有效降低了算法的冗余迭代。扩大了算法解空间搜索范围,提高了对复杂问题的优化能力。
4.利用克隆选择算法与蚁群算法各自的优势,构造了一种免疫克隆选择与蚁群自适应融合优化模型。引入混沌扰动增加抗体种群的多样性,通过克隆扩增、免疫基因等相关算子的操作增强了克隆选择算法搜索的效率；自适应控制参数实现了克隆选择与蚁群优化的有机结合及局部最优搜索策略的应用,克服了抗体种群“早熟”问题,提高了求解精度。
5.应用免疫双态粒子群算法对自抗扰控制器进行优化设计。其一,将免疫双态粒子群算法应用于混沌系统自抗扰优化控制中,对自抗扰控制器参数进行优化,应用于混沌系统控制,构建一种基于免疫双态粒子群算法的混沌系统自抗扰优化控制器；其二,利用自抗扰控制器(ADRC)与小脑神经网络(CMAC)各自的优势并构造ADRC-CMAC并行控制器,利用免疫双态粒子群算法对ADRC-CMAC控制器参数进行自学习寻优,构造出一种自抗扰神经网络并行优化控制方法。针对离散混沌系统研究结果表明,以上两种控制方法具有更好的控制性能和较强的鲁棒性。
6.构造了一种基于免疫协同粒子群进化算法的永磁同步电机多参数辨识模型方法。永磁同步电机参数辨识结果表明该方法不需要知道电机设计参数先验知识,能够有效地辨识电机电阻,d-q轴电感与转子磁链。同时,当电机参数发生变化时,该方法依然能够有效地追踪该参数变化值。
Artificial immune system (AIS) has the advantages of solving complex optimization problems, which simulating the behavior of intelligent characteristic such as self-organization, self-learning ability in biological immune system. Modern industrial system is becoming more and more complex while the system modeling, optimization and control of complex system should need high performance algorithms to assist. It is difficult to meet the performance requirements only rely on a single optimization method. Hybrid immune intelligent processing technology not only can provide the effective way for this kind of problem but also is a direction of the development artificial immune system.
Inspired by the mechanism of immune system, deeply mining the evolutionary learning mechanisms contained in biological immune system and combining with other intelligent processing method advantages, several hybrid immune intelligent optimization algorithms and its related application was studied in this dissertations. A series of work of this paper was launched from algorithm theory, algorithm design, and performance testing, comparative analysis to the practical application. On the theoretical side, four types of hybrid immune intelligent optimization methods were studied and the performances of the proposed algorithm were confirmed through the simulation experiments. On the aspect of application, the hybrid immune intelligent optimization methods were introduced to provide new practical technology for complex engineering problems such as chaotic system's active disturbance rejection optimization control and permanent magnet synchronous motor system Multi parameter identification as well as good control performance and satisfactory identification are obtained. The main work can be summarized as follows:
1. Considering the competition and cooperation between populations, the thought of Lotka-Volterra in ecology was introduced into the artificial immune algorithm, a competitive cooperative coevolutionary immune-dominant clone selection algorithm (CCCICA) was proposed. The affinity maturation of antibody is enhanced by the local optimization of the immune-dominance, the clone expansion and the adaptive dynamic hyper-hybrid mutation and other factors in the species. The population diversity is evaluated and adjusted by the locus information entropy. All subpopulations share one memory which consists of the dominant representatives of each evolved subpopulation. The high level memory is optimized by using the immune genetic crossover operator. Several best individuals are migrated to subpopulations from the top excellent population based on the predefined condition. Through those operations, information is shared among populations for co-evolution.
2. In order to expand the search space of solution, the swarms group is divided into Gather State and Explore State during the search, a novel immune binary-state particle swarm optimization algorithm (IBPSO) is proposed. Elitist learning strategy is applied to the elitist particle to help the jump out of local optimal regions when the search is identified to be in a gather state. This paper propose a concept of explore strategy to encourage particle in a explore state to escape from the local territory. They exhibit a wide range exploration. Moreover, in order to increase the diversity of the population and improve the search capabilities of PSO algorithm, the mechanism of clonal selection and the mechanism of receptor edition are introduced into this algorithm.premature stagnation phenomenon is restrained and redundancy iteration is avoided.
3. Integrated with the principle of immune system optimization, the thought of co-evolutionary and particle swarm neighborhood information, an immune coevolutionary particle swarm optimization algorithm model was proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation, during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune clonal selection operator is employed for optimizing the elite subpopulation while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The ability of complex problem optimization is improved through this operation.
4. A hybrid algorithm integrating the clone selection algorithm with the ant colony algorithm by adaptive fusion (ACALA) based on local optimization search strategy is proposed. In order to increase the diversity of the antibody and improve the search capabilities of ant algorithm, a mechanism of chaotic disturbance is introduced into this algorithm. The operation of clone expansion, immune gene, etc is adopted to enhance the variety of antibody and affinity maturation. The adaptive control parameter is used to achieve the purpose of integrating the clone selection algorithm with the ant colony algorithm organically. Simultaneously, the proposed hybrid algorithm can prevent premature convergence effectively by taking advantage of local optimization search strategy.
5.The Immune Binary-State Particle Swarm Optimization Algorithm (IBPSO) is used to optimize the parameters of Active Disturbance Rejection Control.First, we apply the Immune Binary-State Particle Swarm Optimization Algorithm (IBPSO) which possesses the performance of strong global searching ability and fast real-time to optimize the parameters of Active Disturbance Rejection Control. The presented method has been successfully applied to control chaotic system. Furthermore Active Disturbance Rejection Control based on Immune Binary-State Particle Swarm Optimization Algorithm for the chaotic system is constructed. Secondly, Immune Binary-State Particle Swarm Optimization Algorithm (IBPSO) strategy integrated with Active Disturbance Rejection Control and cerebellar model articulation controller (CMAC) combined control is designed for chaotic systems. The ADRC-CMAC is comprised of a cerebellar model articulation controller (CMAC) and ADRC controller. Immune binary-state particle swarm Algorithm is used to online tune the Parameters of the ADRC-CMAC. According to results of discrete chaotic system show that the presented two control method has better control performance and strong robustness.
6.A novel Parameter identification approach to PMSM based on Immune Co-evolution Particle Swarm Optimization algorithm (ICPSO) is proposed which using the advantage of ICPSO with large space and fast parallel search capability.Finally, the proposed method is further verified by its application in multi-parameter estimation of permanent magnet synchronous machines, which shows that its performance is much better than other PSOs in simultaneously estimating the machine d-q-axis inductances, stator winding resistance and rotor flux linkage. In addition, it is also effective tracking the varied Parameter.
基于免疫系统的机理,深入挖掘生物免疫系统中蕴含的智能学习机制并结合其它智能处理方法的优点,本文研究了几类混合免疫智能优化算法及其相关应用,从算法理论、算法设计、性能测试、比较分析到实际应用展开一系列工作。在理论上研究了四类混合免疫智能优化方法,并通过实验仿真验证了算法的有效性；在应用上研究了混沌系统自抗扰优化控制与永磁同步电机系统多参数辨识这两类典型的复杂系统,并获得了良好的控制效果和辨识结果。概括如下：
1.引入生态学中的协同进化Lotka-Volterra思想到人工免疫算法中,考虑了群体间的竞争合作关系,构造了一种竞争合作型协同进化免疫克隆选择模型。各子种群内部通过局部最优免疫优势、克隆扩增和动态高频变异等相关算子操作。运用信息熵理论改善种群多样性,所有子种群共享经过免疫杂交提升操作的高层记忆库,通过迁移操作实现整个种群信息共享与协同进化。
2.为了扩大解的搜索空间,将粒子群体分为捕食与探索两种模态,建立一种免疫双态粒子群优化方法。对处于捕食状态的精英粒子采用精英学习策略；对处于探索状态的微粒采用探索策略；对微粒个体极值进行免疫克隆优化；对不活跃个体进行免疫受体编辑。算法兼顾了抑制早熟停滞现象和避免冗余迭代。
3.融合免疫系统优化原理、协同进化思想及粒子群的邻域信息,构建免疫协同粒子群进化模型。算法采用并行计算框架,整个群体由记忆种群与若干个普通种群构成。普通微粒种群内部通过精英粒子保留、免疫网络及柯西变异等混合策略共同演化新个体；微粒个体极值采用自适应小波学习以加快收敛速度；免疫克隆选择算法对记忆库进行精细搜索；信息交互机制促进信息共享有效降低了算法的冗余迭代。扩大了算法解空间搜索范围,提高了对复杂问题的优化能力。
4.利用克隆选择算法与蚁群算法各自的优势,构造了一种免疫克隆选择与蚁群自适应融合优化模型。引入混沌扰动增加抗体种群的多样性,通过克隆扩增、免疫基因等相关算子的操作增强了克隆选择算法搜索的效率；自适应控制参数实现了克隆选择与蚁群优化的有机结合及局部最优搜索策略的应用,克服了抗体种群“早熟”问题,提高了求解精度。
5.应用免疫双态粒子群算法对自抗扰控制器进行优化设计。其一,将免疫双态粒子群算法应用于混沌系统自抗扰优化控制中,对自抗扰控制器参数进行优化,应用于混沌系统控制,构建一种基于免疫双态粒子群算法的混沌系统自抗扰优化控制器；其二,利用自抗扰控制器(ADRC)与小脑神经网络(CMAC)各自的优势并构造ADRC-CMAC并行控制器,利用免疫双态粒子群算法对ADRC-CMAC控制器参数进行自学习寻优,构造出一种自抗扰神经网络并行优化控制方法。针对离散混沌系统研究结果表明,以上两种控制方法具有更好的控制性能和较强的鲁棒性。
6.构造了一种基于免疫协同粒子群进化算法的永磁同步电机多参数辨识模型方法。永磁同步电机参数辨识结果表明该方法不需要知道电机设计参数先验知识,能够有效地辨识电机电阻,d-q轴电感与转子磁链。同时,当电机参数发生变化时,该方法依然能够有效地追踪该参数变化值。
Artificial immune system (AIS) has the advantages of solving complex optimization problems, which simulating the behavior of intelligent characteristic such as self-organization, self-learning ability in biological immune system. Modern industrial system is becoming more and more complex while the system modeling, optimization and control of complex system should need high performance algorithms to assist. It is difficult to meet the performance requirements only rely on a single optimization method. Hybrid immune intelligent processing technology not only can provide the effective way for this kind of problem but also is a direction of the development artificial immune system.
Inspired by the mechanism of immune system, deeply mining the evolutionary learning mechanisms contained in biological immune system and combining with other intelligent processing method advantages, several hybrid immune intelligent optimization algorithms and its related application was studied in this dissertations. A series of work of this paper was launched from algorithm theory, algorithm design, and performance testing, comparative analysis to the practical application. On the theoretical side, four types of hybrid immune intelligent optimization methods were studied and the performances of the proposed algorithm were confirmed through the simulation experiments. On the aspect of application, the hybrid immune intelligent optimization methods were introduced to provide new practical technology for complex engineering problems such as chaotic system's active disturbance rejection optimization control and permanent magnet synchronous motor system Multi parameter identification as well as good control performance and satisfactory identification are obtained. The main work can be summarized as follows:
1. Considering the competition and cooperation between populations, the thought of Lotka-Volterra in ecology was introduced into the artificial immune algorithm, a competitive cooperative coevolutionary immune-dominant clone selection algorithm (CCCICA) was proposed. The affinity maturation of antibody is enhanced by the local optimization of the immune-dominance, the clone expansion and the adaptive dynamic hyper-hybrid mutation and other factors in the species. The population diversity is evaluated and adjusted by the locus information entropy. All subpopulations share one memory which consists of the dominant representatives of each evolved subpopulation. The high level memory is optimized by using the immune genetic crossover operator. Several best individuals are migrated to subpopulations from the top excellent population based on the predefined condition. Through those operations, information is shared among populations for co-evolution.
2. In order to expand the search space of solution, the swarms group is divided into Gather State and Explore State during the search, a novel immune binary-state particle swarm optimization algorithm (IBPSO) is proposed. Elitist learning strategy is applied to the elitist particle to help the jump out of local optimal regions when the search is identified to be in a gather state. This paper propose a concept of explore strategy to encourage particle in a explore state to escape from the local territory. They exhibit a wide range exploration. Moreover, in order to increase the diversity of the population and improve the search capabilities of PSO algorithm, the mechanism of clonal selection and the mechanism of receptor edition are introduced into this algorithm.premature stagnation phenomenon is restrained and redundancy iteration is avoided.
3. Integrated with the principle of immune system optimization, the thought of co-evolutionary and particle swarm neighborhood information, an immune coevolutionary particle swarm optimization algorithm model was proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation, during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune clonal selection operator is employed for optimizing the elite subpopulation while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The ability of complex problem optimization is improved through this operation.
4. A hybrid algorithm integrating the clone selection algorithm with the ant colony algorithm by adaptive fusion (ACALA) based on local optimization search strategy is proposed. In order to increase the diversity of the antibody and improve the search capabilities of ant algorithm, a mechanism of chaotic disturbance is introduced into this algorithm. The operation of clone expansion, immune gene, etc is adopted to enhance the variety of antibody and affinity maturation. The adaptive control parameter is used to achieve the purpose of integrating the clone selection algorithm with the ant colony algorithm organically. Simultaneously, the proposed hybrid algorithm can prevent premature convergence effectively by taking advantage of local optimization search strategy.
5.The Immune Binary-State Particle Swarm Optimization Algorithm (IBPSO) is used to optimize the parameters of Active Disturbance Rejection Control.First, we apply the Immune Binary-State Particle Swarm Optimization Algorithm (IBPSO) which possesses the performance of strong global searching ability and fast real-time to optimize the parameters of Active Disturbance Rejection Control. The presented method has been successfully applied to control chaotic system. Furthermore Active Disturbance Rejection Control based on Immune Binary-State Particle Swarm Optimization Algorithm for the chaotic system is constructed. Secondly, Immune Binary-State Particle Swarm Optimization Algorithm (IBPSO) strategy integrated with Active Disturbance Rejection Control and cerebellar model articulation controller (CMAC) combined control is designed for chaotic systems. The ADRC-CMAC is comprised of a cerebellar model articulation controller (CMAC) and ADRC controller. Immune binary-state particle swarm Algorithm is used to online tune the Parameters of the ADRC-CMAC. According to results of discrete chaotic system show that the presented two control method has better control performance and strong robustness.
6.A novel Parameter identification approach to PMSM based on Immune Co-evolution Particle Swarm Optimization algorithm (ICPSO) is proposed which using the advantage of ICPSO with large space and fast parallel search capability.Finally, the proposed method is further verified by its application in multi-parameter estimation of permanent magnet synchronous machines, which shows that its performance is much better than other PSOs in simultaneously estimating the machine d-q-axis inductances, stator winding resistance and rotor flux linkage. In addition, it is also effective tracking the varied Parameter.
引文
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