基于生物免疫隐喻机制的AIS优化算法研究
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摘要
人工免疫系统（Artificial immune system，AIS）是模仿自然免疫系统功能的一种智能方法。它受生物免疫系统自然防御机制的启发并具备噪声忍耐、无教师学习、自组织、记忆等特性，同时结合了分类器、神经网络及推理系统的一些优点，为解决实际问题提供了新颖方法。其研究成果涉及优化计算、控制、数据处理和故障诊断等许多领域，成为继神经网络、模糊逻辑和进化计算后人工智能的又一个研究热点。
然而，免疫系统本身比较复杂，因此对人工免疫系统模型的研究相对较少。虽然现有的研究成果展示了人工免疫算法在解决某些现实问题上的巨大潜力和在求解一些优化问题上的优势，但是目前对人工免疫系统算法的研究还只是处于起步阶段，其广阔的应用前景还在等待更细致的开发。在优化计算方面，目前对人工免疫优化算法的研究多集中在免疫机理对已有优化算法的改进，虽然这些算法大多被冠以“免疫”的名字，但本质上只是利用了免疫系统的相关机理对遗传算法的改进，而且多数是静态的和非自适应的，也缺乏对生物免疫机理的深入分析以及与其他受自然启发算法的深入对比研究。本文在已有克隆选择算法的基础上，深入研究了生物免疫系统的识别，学习和防御机理，通过提取相关免疫隐喻机制，从不同角度分别构造了免疫反应机制与全局优化问题、约束优化问题和动态优化问题的匹配映射关系，并以此映射关系为基础，提出了相应的人工免疫算法，解决了约束优化和动态优化两个问题。论文取得的主要成果与创新工作概括如下：
①基于生物免疫机制抽取免疫隐喻，构建了生物免疫反应与最优化问题、约束优化问题和动态优化问题的匹配映射。为这些问题的解决提供了崭新的生物学的视角。详细探讨了生物免疫系统的高稳定性和可靠性的信息处理和机体防御方法。摒弃了现有的很多算法只是借用“免疫”之名，停留在概念表层的做法，对生物免疫系统进行深入的分析和研究，并以此为基础构造算法。
②提出了基于信息传递的人工免疫优化算法IAIS。以B细胞在固有免疫和自适应免疫中扮演的双重角色为立足点抽取隐喻，构造了免疫反应与约束优化问题的匹配映射。然后用B细胞的激活和非激活两种形态分别模拟了约束优化问题中因约束存在而造成的候选解的两种形式——可行解和不可行解，从信息传递的观点出发，提取并利用方向信息促进不可行解向可行域推进，进而精确定位可行解的方式解决约束优化问题。IAIS算法只采用了传统的人工免疫算子操作就提升了算法的性能，显示出人工免疫优化算法在解决约束优化问题上的巨大潜力。
③改进了IAIS算法并用于解决约束优化问题。深入挖掘人工免疫机制解决约束优化问题的潜力，并在此基础上，对算法的一些操作进行了修改，进一步提升算法的性能。为了克服传统克隆选择算法早熟收敛和搜索精度有限的问题，在算法中加入基因重组（recombination）算子，修改了免疫变异（hypermutation）算子，引入了新个体招募（recruitment）等，并调整了方向信息的提取方式，运用现有测试函数测试，达到了非常有竞争力的结果。本文提出的算法弥补了现有人工免疫算法在解决约束优化问题上的不足，而且仅仅立足于生物免疫机制，促进了人工免疫算法自身的发展。本文通过大量的实验从统计的角度验证了算法的全局搜索能力、高的求解精度以及好的稳定性。
④提出了基于类梯度、聚类和记忆机制的人工免疫算法GCMAIS解决动态环境下的优化问题。在已建立的生物免疫反应与动态优化问题的映射基础上，提取了一般的人工免疫优化算法框架并进行了深入的分析，找出了其在解决动态优化问题上的不足，并提出了三种应对机制从不同的方面提升了算法的性能。为了提高算法的搜索速度，从克隆个体携带的冗余信息中提取了类梯度信息，扩展了传统雅各比向量和正切向量等常用的梯度信息提取方法的使用范围，提升了算法的搜索性能；为了促进算法的搜索能力和保持种群的多样性，本文采用了聚类的方法引入了多种群处理机制，并加强了子群中的个体之间以及子群与子群之间的相互作用，删除了种群的冗余信息，并促进了精确搜索；为了应对动态优化的周期和非周期变化的动态特性，深入研究了生物免疫记忆机制，并根据记忆细胞生命周期的不同，提出了长期-短期记忆机制。短期记忆提取了紧邻的上一个环境的重要信息，对于不太剧烈的外部环境变化起到了跟踪作用，而长期记忆提取了以往环境的历史信息，对于环境的周期、类周期变化的初始种群的设定提供了有用的参考。实验验证了所提出策略的有效性。
Artificial immune system (AIS) is an intelligent method of simulating the naturalimmune system, which realizes a natural defense mechanism inspired from biologicalimmune system, and provides noise tolerance, teacherless learning, self-organizing,memory and other characteristics. Moreover, equipped with advantages as neuralnetwork, classifier, and inference system, AIS provides a novel solution to realisticproblems. The involved research achievements include optimization, control, dataprocessing and fault diagnosis etc. AIS has been a new hot topic in the field of artificialintelligence since neural network, fuzzy logic and evolutionary computation.
However, researches on the model of AIS are quite few since the biologicalimmune system is very complex. Although the existing researches demonstrate the greatpotential of artificial immune algorithm in solving some practical problems and theadvantages in handling some optimization problems, current research on AIS is still inits infancy. Its broad application prospect still need more detailed development. In thefield of optimization, researches on AIS mainly focus on improving existing algorithmsthrough realizing immune mechanisms. Although most of these algorithms are titledwith "immune", they are essentially just the modified versions to the genetic algorithmbased on immune mechanisms, and most of them are static and non-adaptive. Moreover,comprehensive analysis on biological immune mechanism and exhaustive comparisonwith other natural heuristic algorithms are also ignored. Based on the existing clonalselection algorithm, learning recognition and defense mechanism of biological immunesystem are elaborated in the thesis. Through extracting the relative immune metaphor,the AIS-based algorithms are proposed to solve the constrained optimization anddynamic optimization problems. The main achievements and innovation are as follows:
①Based on immune metaphor, global optimization, constrained optimization andoptimization in dynamic environment are mapped with biological immune responsefrom different angles. A novel biological perspective and solid biological backgroundare provided to solve these problems. Stable information processing and reliabledefense mechanism are elaborated. We construct the AIS based algorithm on account ofcarefully exploring the functions and mechanisms of biological immune system ratherthan borrowing the "immune" concept directly.
②An immune-inspired algorithm based on information transfer (IAIS) is proposed. Immune metaphor is extracted from the dual roles B cells played in innateimmunity and adaptive immunity. Accordingly, the analogy between the mechanism ofbiological immune response and constrained optimization formulation is drawn.Individuals in population are classified into feasible and infeasible groups according totheir constraint violations that closely match with the two states, inactivated andactivated, of B cells in the immune response. Feasible group focuses on exploitation inthe feasible region, while infeasible group facilitates exploration along the feasibilityboundary. Although adopts only the traditional artificial immune operator, the IAISalgorithm with proposed framework and information transfer strategy shows greatpotential in solving constrained optimization problems.
③The IAIS algorithm was modified to solve the constrained optimizationproblems. On premise of great potential of immune-based algorithm in handlingconstrained optimization problems, some operators of IAIS are modified and theperformance of the modified IAIS algorithm is further improved. In order to overcomethe disadvantages that traditional clonal selection algorithm with prematureconvergence and limited search precision, Recombination operator and Recruitmentoperator are introduced, and hypermutation operator is modified. Meanwhile, Directioninformation extraction is adjusted. The modified IAIS is validated to be competitive incomparing with the state-of-the-art algorithms. The proposed algorithm compensatesthe weakness of the existing artificial immune algorithms in solving constrainedoptimization problems, and promotes the development of artificial immune algorithmitself. Through a lot of statistical experiments, the Modified IAIS is verified to haveglobal searching ability, high accuracy and good stability.
④To solve the optimization problems in dynamic environment, animmune-inspired algorithm based on quasi-gradient, clustering and memory mechanism(GCMAIS) is developed. On the basis of careful analysis, shortages of the generalframework of AIS in solving dynamic optimization problems are summarized at first,and then three coping mechanisms from different aspects to improve the performance ofalgorithm are put forward. Firstly, in order to improve the search speed, quasi-gradientinformation is extracted from the redundant information carried by clones. TraditionalJacobian vector and the Tangent vector are extended through the quasi-gradient toextend the search performance. Secondly, in order to improve the search ability of thealgorithm and maintain the diversity of population, clustering method is adopted formulti-population mechanism. The interaction among individuals in each subpopulation and among subpopulations is enhanced. The multi-population mechanism not onlydecreases the redundant information of population, but also promotes the precise search.Thirdly, in order to deal with dynamic characteristics of the cyclic and noncyclic changein dynamic environment, the immune memory mechanism is elaborated. The long-termand short-term memory mechanism is established according to the different life span ofmemory cells. Short-term memory extracts important information from last scenario,which is benefit for tracking the less dramatic changes, while long-term memoryretrieves history information from previous environments, providing references forpopulation initialization under cyclic environment or with recurrent change.Experiments validate the efficiency of the proposed method.
然而，免疫系统本身比较复杂，因此对人工免疫系统模型的研究相对较少。虽然现有的研究成果展示了人工免疫算法在解决某些现实问题上的巨大潜力和在求解一些优化问题上的优势，但是目前对人工免疫系统算法的研究还只是处于起步阶段，其广阔的应用前景还在等待更细致的开发。在优化计算方面，目前对人工免疫优化算法的研究多集中在免疫机理对已有优化算法的改进，虽然这些算法大多被冠以“免疫”的名字，但本质上只是利用了免疫系统的相关机理对遗传算法的改进，而且多数是静态的和非自适应的，也缺乏对生物免疫机理的深入分析以及与其他受自然启发算法的深入对比研究。本文在已有克隆选择算法的基础上，深入研究了生物免疫系统的识别，学习和防御机理，通过提取相关免疫隐喻机制，从不同角度分别构造了免疫反应机制与全局优化问题、约束优化问题和动态优化问题的匹配映射关系，并以此映射关系为基础，提出了相应的人工免疫算法，解决了约束优化和动态优化两个问题。论文取得的主要成果与创新工作概括如下：
①基于生物免疫机制抽取免疫隐喻，构建了生物免疫反应与最优化问题、约束优化问题和动态优化问题的匹配映射。为这些问题的解决提供了崭新的生物学的视角。详细探讨了生物免疫系统的高稳定性和可靠性的信息处理和机体防御方法。摒弃了现有的很多算法只是借用“免疫”之名，停留在概念表层的做法，对生物免疫系统进行深入的分析和研究，并以此为基础构造算法。
②提出了基于信息传递的人工免疫优化算法IAIS。以B细胞在固有免疫和自适应免疫中扮演的双重角色为立足点抽取隐喻，构造了免疫反应与约束优化问题的匹配映射。然后用B细胞的激活和非激活两种形态分别模拟了约束优化问题中因约束存在而造成的候选解的两种形式——可行解和不可行解，从信息传递的观点出发，提取并利用方向信息促进不可行解向可行域推进，进而精确定位可行解的方式解决约束优化问题。IAIS算法只采用了传统的人工免疫算子操作就提升了算法的性能，显示出人工免疫优化算法在解决约束优化问题上的巨大潜力。
③改进了IAIS算法并用于解决约束优化问题。深入挖掘人工免疫机制解决约束优化问题的潜力，并在此基础上，对算法的一些操作进行了修改，进一步提升算法的性能。为了克服传统克隆选择算法早熟收敛和搜索精度有限的问题，在算法中加入基因重组（recombination）算子，修改了免疫变异（hypermutation）算子，引入了新个体招募（recruitment）等，并调整了方向信息的提取方式，运用现有测试函数测试，达到了非常有竞争力的结果。本文提出的算法弥补了现有人工免疫算法在解决约束优化问题上的不足，而且仅仅立足于生物免疫机制，促进了人工免疫算法自身的发展。本文通过大量的实验从统计的角度验证了算法的全局搜索能力、高的求解精度以及好的稳定性。
④提出了基于类梯度、聚类和记忆机制的人工免疫算法GCMAIS解决动态环境下的优化问题。在已建立的生物免疫反应与动态优化问题的映射基础上，提取了一般的人工免疫优化算法框架并进行了深入的分析，找出了其在解决动态优化问题上的不足，并提出了三种应对机制从不同的方面提升了算法的性能。为了提高算法的搜索速度，从克隆个体携带的冗余信息中提取了类梯度信息，扩展了传统雅各比向量和正切向量等常用的梯度信息提取方法的使用范围，提升了算法的搜索性能；为了促进算法的搜索能力和保持种群的多样性，本文采用了聚类的方法引入了多种群处理机制，并加强了子群中的个体之间以及子群与子群之间的相互作用，删除了种群的冗余信息，并促进了精确搜索；为了应对动态优化的周期和非周期变化的动态特性，深入研究了生物免疫记忆机制，并根据记忆细胞生命周期的不同，提出了长期-短期记忆机制。短期记忆提取了紧邻的上一个环境的重要信息，对于不太剧烈的外部环境变化起到了跟踪作用，而长期记忆提取了以往环境的历史信息，对于环境的周期、类周期变化的初始种群的设定提供了有用的参考。实验验证了所提出策略的有效性。
Artificial immune system (AIS) is an intelligent method of simulating the naturalimmune system, which realizes a natural defense mechanism inspired from biologicalimmune system, and provides noise tolerance, teacherless learning, self-organizing,memory and other characteristics. Moreover, equipped with advantages as neuralnetwork, classifier, and inference system, AIS provides a novel solution to realisticproblems. The involved research achievements include optimization, control, dataprocessing and fault diagnosis etc. AIS has been a new hot topic in the field of artificialintelligence since neural network, fuzzy logic and evolutionary computation.
However, researches on the model of AIS are quite few since the biologicalimmune system is very complex. Although the existing researches demonstrate the greatpotential of artificial immune algorithm in solving some practical problems and theadvantages in handling some optimization problems, current research on AIS is still inits infancy. Its broad application prospect still need more detailed development. In thefield of optimization, researches on AIS mainly focus on improving existing algorithmsthrough realizing immune mechanisms. Although most of these algorithms are titledwith "immune", they are essentially just the modified versions to the genetic algorithmbased on immune mechanisms, and most of them are static and non-adaptive. Moreover,comprehensive analysis on biological immune mechanism and exhaustive comparisonwith other natural heuristic algorithms are also ignored. Based on the existing clonalselection algorithm, learning recognition and defense mechanism of biological immunesystem are elaborated in the thesis. Through extracting the relative immune metaphor,the AIS-based algorithms are proposed to solve the constrained optimization anddynamic optimization problems. The main achievements and innovation are as follows:
①Based on immune metaphor, global optimization, constrained optimization andoptimization in dynamic environment are mapped with biological immune responsefrom different angles. A novel biological perspective and solid biological backgroundare provided to solve these problems. Stable information processing and reliabledefense mechanism are elaborated. We construct the AIS based algorithm on account ofcarefully exploring the functions and mechanisms of biological immune system ratherthan borrowing the "immune" concept directly.
②An immune-inspired algorithm based on information transfer (IAIS) is proposed. Immune metaphor is extracted from the dual roles B cells played in innateimmunity and adaptive immunity. Accordingly, the analogy between the mechanism ofbiological immune response and constrained optimization formulation is drawn.Individuals in population are classified into feasible and infeasible groups according totheir constraint violations that closely match with the two states, inactivated andactivated, of B cells in the immune response. Feasible group focuses on exploitation inthe feasible region, while infeasible group facilitates exploration along the feasibilityboundary. Although adopts only the traditional artificial immune operator, the IAISalgorithm with proposed framework and information transfer strategy shows greatpotential in solving constrained optimization problems.
③The IAIS algorithm was modified to solve the constrained optimizationproblems. On premise of great potential of immune-based algorithm in handlingconstrained optimization problems, some operators of IAIS are modified and theperformance of the modified IAIS algorithm is further improved. In order to overcomethe disadvantages that traditional clonal selection algorithm with prematureconvergence and limited search precision, Recombination operator and Recruitmentoperator are introduced, and hypermutation operator is modified. Meanwhile, Directioninformation extraction is adjusted. The modified IAIS is validated to be competitive incomparing with the state-of-the-art algorithms. The proposed algorithm compensatesthe weakness of the existing artificial immune algorithms in solving constrainedoptimization problems, and promotes the development of artificial immune algorithmitself. Through a lot of statistical experiments, the Modified IAIS is verified to haveglobal searching ability, high accuracy and good stability.
④To solve the optimization problems in dynamic environment, animmune-inspired algorithm based on quasi-gradient, clustering and memory mechanism(GCMAIS) is developed. On the basis of careful analysis, shortages of the generalframework of AIS in solving dynamic optimization problems are summarized at first,and then three coping mechanisms from different aspects to improve the performance ofalgorithm are put forward. Firstly, in order to improve the search speed, quasi-gradientinformation is extracted from the redundant information carried by clones. TraditionalJacobian vector and the Tangent vector are extended through the quasi-gradient toextend the search performance. Secondly, in order to improve the search ability of thealgorithm and maintain the diversity of population, clustering method is adopted formulti-population mechanism. The interaction among individuals in each subpopulation and among subpopulations is enhanced. The multi-population mechanism not onlydecreases the redundant information of population, but also promotes the precise search.Thirdly, in order to deal with dynamic characteristics of the cyclic and noncyclic changein dynamic environment, the immune memory mechanism is elaborated. The long-termand short-term memory mechanism is established according to the different life span ofmemory cells. Short-term memory extracts important information from last scenario,which is benefit for tracking the less dramatic changes, while long-term memoryretrieves history information from previous environments, providing references forpopulation initialization under cyclic environment or with recurrent change.Experiments validate the efficiency of the proposed method.
引文
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