基于Multi-memory机制的克隆选择算法及其在模式识别中的应用

人工免疫系统研究中大多借鉴克隆选择原理来构建免疫识别算法。描述了Castro提出的克隆选择算法CLONALG的整体框架,指出其在大规模模式识别问题中的不可收敛性。在CLONALG的基础上设计了Multi-memory机制,并以模式识别为应用背景提出了新的基于Multi-memory机制的克隆选择的免疫算法MCA,提出并深入分析了记忆抗体训练过程中的关键因素——变异概率的计算公式。实验表明,采用MCA的免疫系统具有更强的泛化能力、更高的抗原识别率以及更能适应大规模问题的优良特性。     

Learning and optimization using the clonal selection principle

The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization     

人工免疫算法及其应用研究

为了有效地解决病态的约束优化问题,提出了一种模拟生物免疫系统自我调节功能的人工免疫算法,介绍了算法的基本步骤,构造了几种人工免疫算子,分析了算法的收敛性.人工免疫算法继承了遗传算法“优胜劣汰”的自我淘汰机制,但新抗体的产生方法比遗传算法中新个体的产生方法灵活得多.在进行抗体选择时若能确保当时的最优抗体可以进入下一代抗体群,则人工免疫算法是全局收敛的.100个城市TSP问题的仿真实例显示人工免疫算法比遗传算法具有更强的全局搜索能力和收敛速度.     

基于克隆选择的多光谱遥感影像分类算法

为了对多光谱遥感影像进行更精确的分类，提出了一种基于克隆选择(clonal selection)的多光谱遥感影像分类算法。该方法首先应用基于人工免疫系统的克隆选择算法对样本进行自学习来得到全局最优的聚类中心，然后利用学习得到的聚类中心对整幅影像进行分类。由于克隆选择算法具有生物免疫系统自组织、自学习、自识别、自记忆的能力，不仅使得基于克隆选择的多光谱遥感影像分类算法具有非线性的分类能力，而且能够快速准确地得到全局最优解，从而克服了传统分类方法约束条件多、容易陷入局部最优的缺点。实验结果证明，基于克隆选择的多光谱遥感影像分类算法在分类精度上优于传统的分类方法，其总精度和Kappa系数分别达到了93．63％和0．915，因而具有实用价值。     

基于人工免疫算法的多目标函数优化

提出了一种新型的人工免疫算法用来解决多目标函数优化问题。基于自然免疫系统固有的优良特性对算法进行了设计和分析。最后,算法对3个较复杂的多目标问题进行了优化,优化结果能很好地覆盖问题的Paret。最优面,并且把算法与某些混合遗传算法进行了对比实验,表明人工免疫算法在解决多目标优化问题上具有可观的研究前景。     

Artificial immune classifier with swarm learning

Artificial immune systems are computational systems inspired by the principles and processes of the natural immune system. The various applications of artificial immune systems have been used for pattern recognition and classification problems; however, these artificial immune systems have three major problems, which are growing of the memory cell population, eliminating of the useful memory cells in next the steps, and randomly using cloning and mutation operators. In this study, a new artificial immune classifier with swarm learning is proposed to solve these three problems. The proposed algorithm uses the swarm learning to evolve the antibody population. In each step, the antibodies that belong to the same class move to the same way according to their affinities. The size of the memory cell population does not grow during the training stage of the algorithm. Therefore, the method is faster than other artificial immune classifiers. The classifier was tested on two case studies. In the first case study, the algorithm was used to diagnose the faults of induction motors. In the second case study, five benchmark data sets were used to evaluate the performance of the algorithm. The results of second case studies show that the proposed method gives better results than two well-known artificial immune systems for real word data sets. The results were compared to other classification techniques, and the method is competitive to other classifiers.     

基于免疫应答原理的多目标优化免疫算法及其应用

基于免疫应答原理，合理地构建免疫算子及引入一种新的小生境技术, 提出一种 解决多目标优化问题的免疫算法. 在此算法中，将优化问题的可行解对应抗体及Pareto最优个体对应抗原，这种抗原存于抗原群中,并应用新的聚类算法不断更新抗原群中的抗原, 进而获大量的Pareto最优解, 这些解能很好地分布在Pareto面（此指由Pareto最优解构成）上. 理论证明了该算法能获Pareto最优解. 最后，将该文的算法与文献\[3\]的算法SPEA进行仿真比较, 获该算法的有效性, 此表明免疫算法解决多目标优化问题具有广阔的前景.     

多峰函数优化的自适应小生境克隆选择算法

为了解决de Castro在2000年提出的CLONALG算法在多峰值函数优化时多峰搜索能力弱,训练时间长的问题,提出自适应小生境克隆选择算法（ANCSA）。该算法运用自适应小生镜技术、高频变异算子和小生镜免疫优势选择技术来对原有算法进行改进。新算法具有较强的全局和局部搜索能力,并且搜索时间较短。理论分析和仿真研究结果表明,相比CLONALG算法,提出的算法能够在较短的时间内搜索到所有的全局最优解和更多的局部最优解。     

GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization

In this paper, we propose a new Pareto generic algorithm, called GISMOO, which hybridizes genetic algorithm and artificial immune systems. GISMOO algorithm is generic in the sense that it can be used to solve both combinatorial and continuous optimization problems. The proposed approach offers an original iterative process in two phases: a Genetic Phase and an Immune Phase. The Immune Phase is used to identify and to emphasize the solutions located in less crowded regions found during the iterative process of the algorithm. Simulation results on difficult test problems, both in combinatorial and continuous optimization, show that the proposed approach, in most problems, is able to obtain better results than state of the art algorithms.     

Predication based immune network for multimodal function optimization

For the problem of indeterminate direction of local search, lacking of efficient regulation mechanism between local search and global search and regenerating new antibodies randomly in the original optimization version of artificial immune network (opt-aiNet), this paper puts forward a novel predication based immune network (PiNet) to solve multimodal function optimization more efficiently, accurately and reliably. The algorithm mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune predication. The proposed algorithm includes two main features with opt-aiNet. The information of antibodies in continuous generations is utilized to point out the direction of local search and to adjust the balance between local and global search. PiNet also employs memory cells to generate new antibodies with high affinities. Theory analysis and experiments on 10 widely used benchmark problems show that when compared with opt-aiNet method, PiNet algorithm is capable of improving search performance significantly in successful rate, convergence speed, search ability, solution quality and algorithm stability.     

Negative selection based immune optimization

An immune optimization algorithm is proposed in this paper based on the immune negative selection. The algorithm NSIOA is motivated by the negative selection mechanism in biological immune recognition. Different from the existing immune optimization methods, NSIOA constantly removes the worst solutions to get the optimal solution. Considering that removal of poor members of a population might lead to the loss of design information that may actually help identify better solutions in the search space, the proposed NSIOA is designed to keep the diversity of antibodies while removing poor members, therefore the algorithm will converge to global optimal solution with high probability. The convergence property and the complexity of the algorithm have also been analyzed. To illustrate the efficiency of the algorithm is used in solving the travel salesman problem. The theoretical analysis and experimental results show that the algorithm is of a strong potential in solving practical problems.     

改进免疫算法在Flow Shop调度上的应用

为了高效地解决Flow Shop问题,提出了一种利用免疫算法求解Flow Shop调度问题的方法.该算法是根据人或者其它高等动物的免疫系统机理设计的,将调度目标和约束条件作为抗原,将问题的解作为抗体,对抗体采用按工件加工顺序进行自然数编码,并把最大流程时间的倒数作为适应度函数,通过引入隔离小生境等技术提高了免疫算法的适应能力,保证了种群的多样性,克服了早熟收敛,提高了收敛速度.通过对Flow Shop问题的基准测试表明,该算法不仅在求解问题的规模上具有很好的可伸缩性,而且在运算时间上也低于禁忌搜索算法和模拟退火算法,从而验证了该算法的有效性.     

基于免疫进化的粒子群混洗蛙跳算法

为了避免混洗蛙跳算法易于出现不成熟收敛,提高求解质量,提出了基于免疫进化的粒子群混洗蛙跳算法。该算法将粒子群算法中粒子追踪全局极值的思想融入混洗蛙跳算法中,对族群内的最差个体同时跟踪族群内和全局两个最优个体的信息,进行深度搜索;并引入免疫进化算法对群体中的最优个体进行免疫进化迭代计算,以达到充分利用最优个体的信息的目的。该算法不仅避免了陷入局部极值的局限,以更高的精度逼近全局最优解,而且能加速收敛。对多个典型测试函数的计算表明：基于免疫进化的粒子群混洗蛙跳算法比传统的混洗蛙跳算法具有更好的寻优能力、稳定效果和更快的收敛速度。     

一种改进的克隆选择算法在多峰值函数优化中的应用

为了解决deCastro2002年提出的CLONALG算法在多峰值函数优化时多峰搜索能力弱、训练时间长的问题,本文提出了一种改进的克隆选择算法。该算法运用新的克隆选择操作、克隆变异操作和最佳抗体停止进化操作,并且引入了抗体抑制操作,不仅可以动态调整种群大小,具有较强的全局和局部搜索能力,而且搜索时间较短。与Castro的克隆选
择算法相比,本文算法在较短的时间内可以搜索到全局最优解和更多的局部最优解。     

一种改进的多目标粒子群优化算法及其应用

针对多目标粒子群优化算法在求解约束优化问题时存在难以兼顾收敛性能和求解质量这一问题,提出一种基于免疫网络的改进多目标粒子群优化算法.该算法通过免疫网络互通种群最优信息达到粒子群算法与人工免疫网络算法的协同搜索,同时给出了速度迁移策略、自适应方差变异策略和基于聚类的免疫网络策略.最后将所提出的方法应用于求解电弧炉供电优化模型,达到了减少电量消耗、缩短冶炼时间、延长炉衬使用寿命的目的,同时表明了该算法的有效性.     

基于相似性矢量距选择的改进人工免疫算法

针对基于信息熵和基于欧氏距离的免疫算法存在的不足,提出了一种改进的免疫算法。该算法重新定义了浓度的计算方法,提出一种新的保持抗体群多样性的策略,在将其应用在中国旅行商问题（CTSP）的求解中,具体针对旅行商问题提出了新的免疫疫苗的提取和注射方法,通过实验表明了新的算法能更快地收敛到最优解,且求得最优解的效率更高,是一种较理想的求解复杂优化问题的改进算法。     

Using the method of artificial immune systems to seek the suboptimal program control of deterministic systems

Determination of the suboptimal programmed control for the continuous deterministic systems by the use of the method of artificial immune systems for search of the conditional global extremum was proposed. An algorithm to solve it which underlies the corresponding software was generated, and a modification of the method was suggested to enhance its efficiency. Examples of solutions of the model problems were given, and the method of artificial immune systems was compared with its modification, as well as with other metaheuristic methods.     

基于免疫记忆的蚁群算法

充分利用前期迭代中解的信息是构造高效蚁群算法实现的关键之一.文中把免疫记忆和克隆选择的思想引入蚁群算法,提出了基于免疫记忆的蚁群算法(IMBACA).算法通过在原有蚁群模型上增加一个免疫记忆库,将记忆库中的解对应为免疫记忆细胞(及其产生的抗体),将问题对应为抗原,并借鉴克隆选择和免疫记忆的思想进行解的构造和信息素更新.算法从解的质量和时间方面与传统蚁群算法进行了比较,实验结果表明,所提出的IMBACA算法可明显提高传统蚁群算法的性能,同时也为解决其他组合优化问题提出了一个新的思路.     

冯诺依曼人工蜂群算法求解RFID网络优化问题

本文提出了一种具有冯诺依曼社会结构的新型人工蜂群算法(VNABC)。本文采用四个测试函数验证VNABC算法性能,并将其应用于求解射频识别系统中的读写器网络覆盖和防冲突问题。试验结果表明,与基本人工蜂群算法和粒子群优化算法比较,VNABC算法求解复杂优化问题收敛速度较快、求解精度更高,从而为应用智能方法求解RFID系统优化问题提供了有效的可行方案。     

Optimization of multiple input–output fuzzy membership functions using clonal selection algorithm

A clonal selection algorithm (CLONALG) inspires from clonal selection principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed CLONALG program for a multiple input–output (MI–O) fuzzy system. In this study, GA and binary particle swarm optimization (BPSO) are used for implementing the proposed method as well and they are compared. It has been shown that using clonal selection algorithm is advantageous for finding optimum values of fuzzy membership functions