A new artificial immune algorithm and its application for optimization problems

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

Immune response-based algorithm for optimization of dynamic environments

A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle.The dynamic process of artificial immune response with operators such as immune cloning,multi-scale variation and gradient-based diversity was modeled.Because the immune cloning operator was derived from a stimulation and suppression effect between antibodies and antigens,a sigmoid model that can clearly describe clonal proliferation was proposed.In addition,with the introduction of multiple populations and multi-scale variation,the algorithm can well maintain the population diversity during the dynamic searching process.Unlike traditional artificial immune algorithms,which require randomly generated cells added to the current population to explore its fitness landscape,AIDE uses a gradient-based diversity operator to speed up the optimization in the dynamic environments.Several reported algorithms were compared with AIDE by using Moving Peaks Benchmarks.Preliminary experiments show that AIDE can maintain high population diversity during the search process,simultaneously can speed up the optimization.Thus,AIDE is useful for the optimization of dynamic environments.

Immune Genetic Algorithm for Optimal Design

A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.

Hybrid anti-prematuration optimization algorithm

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.

Immune clonal selection optimization method with combining mutation strategies

In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination mutation operator of Gaussian and Cauchy mutation is presented in this paper, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The simulation results show the combining mutation strategy can obtain the same performance as the best of pure strategies or even better in some cases.

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

基于改进人工大猩猩部队算法的移动机器人路径规划研究

针对传统人工大猩猩部队优化算法在移动机器人路径规划问题中存在前期全局寻优能力较弱、后期收敛能力不强、容易陷入局部最优等问题,提出一种改进的人工大猩猩部队优化算法。在改进算法中,为提高初始种群质量,采用Logistic混沌映射生成种群;引入新的计算公式改进控制参数W的值,使其随着迭代次数增加而线性增大;融合鱼鹰优化算法的位置更新策略,以增强算法中个体之间交流信息;在算法开发阶段后期,应用莱维飞行策略更新个体位置,以保证算法后期的种群多样性。仿真实验结果表明,与SSA算法、GTO算法和GWO算法相比,改进算法在M1地图环境中得到的平均路径分别缩短了9.72%、6.07%和7.99%;在M2地图环境中得到的平均路径分别缩短了22.04%、44.16%和50.3%,具有明显优势。

适用于配电网无功优化的混合鱼群算法

人工鱼群算法同传统群智能算法一样,由于基础种群的随机性以及寻优路线的不确定性等原因可能导致算法寻不到最优解。为解决以上问题,利用混沌变换序列生成遍布更加均匀的初始值;采用自适应的视野与步长动态改变搜索范围;引入改进的生物“进化”机制,利用当代和精英个体的基因来对鱼群进行淘汰,加快收敛速度。将该混合算法用于IEEE33节点系统配电网的无功优化中,结果表明改进的算法更易跳出局部最优,且收敛速度较快,收敛性能较好,提高了寻优效率。

基于改进人工电场算法的泵站优化调度

针对我国大型泵站运行效率不高、耗能大的问题,考虑电机和变频器的损耗,结合分时电价,建立泵站日运行电价最小的优化模型,采用人工电场算法(AEFA)求解。针对原始引力常数的计算方法会导致算法早熟和易陷入局部最优的问题,引入混沌映射进行改进。以山西省万家寨三级泵站为例计算求解,结果表明:与实际运行方案相比,人工电场算法优化方案节能率为5.7%~11.3%,改进后的人工电场算法优化方案节能率为9.9%~28.4%。算法改进后在计算精度、稳定性、计算时长方面都表现较好。

进化多目标优化算法研究

进化多目标优化主要研究如何利用进化计算方法求解多目标优化问题,已经成为进化计算领域的研究热点之一.在简要总结2003年以前的主要算法后,着重对进化多目标优化的最新进展进行了详细讨论.归纳出当前多目标优化的研究趋势,一方面,粒子群优化、人工免疫系统、分布估计算法等越来越多的进化范例被引入多目标优化领域,一些新颖的受自然系统启发的多目标优化算法相继提出;另一方面,为了更有效的求解高维多目标优化问题,一些区别于传统Pareto占优的新型占优机制相继涌现;同时,对多目标优化问题本身性质的研究也在逐步深入.对公认的代表性算法进行了实验对比.最后,对进化多目标优化的进一步发展提出了自己的看法.

求解多目标问题的Memetic免疫优化算法

将基于Pareto支配关系的局部下山算子和差分算子引入免疫多目标优化算法之中,提出了一种求解多目标问题的Memetic免疫优化算法(Memetic immune algorithm for multiobjective optimization,简称MIAMO).该算法利用种群中抗体在决策空间上的位置关系设计了两种有效的启发式局部搜索策略,提高了免疫多目标优化算法的求解效率.仿真实验结果表明,MIAMO与其他4种有效的多目标优化算法相比,不仅在求得Pareto最优解集的逼近性、均匀性和宽广性上有明显优势,而且算法的收敛速度与免疫多目标优化算法相比明显加快.

电力系统经济负荷分配的人工免疫混沌优化算法

提出了一种用于求解复杂电力系统经济负荷分配问题的新的人工免疫混沌优化算法,该算法融合了人工免疫算法极强的全局搜索能力以及混沌优化方法适合局部搜索的特点。在优化过程中,人工免疫算法通过克隆选择、克隆扩增和高频变异形成记忆细胞,并将其作为最优解的近似解,然后按混沌运动规律在近似解的邻域内进行局部搜索,进而获得精确的最优解。多个算例仿真结果表明,所提出的算法能够有效地解决经济负荷分配问题。

配电网供电恢复的混沌免疫算法

为在配电网供电恢复的方案计算中提高获得最优解的概率并加快算法的收敛速度,分析混沌优化算法和免疫算法原理的基础上,提出了一种混沌免疫算法。该算法融合了混沌优化算法的全局搜索和人工免疫算法的局域搜索能力的特点,在优化过程中采用人工免疫算法代替混沌优化算法中的"细搜索",同时用混沌优化算法中的"粗搜索"来初始化免疫算法中的初始抗体。实例分析表明,该算法具有较快的收敛速度和较强的全局搜索能力,避免了不成熟收敛,有效地提高了供电恢复的速度和精度。

人工免疫粒子群算法在系统可靠性优化中的应用

为了优化舰载装备系统在其设计初期的可靠性,根据模糊优选理论,建立了基于正负理想方案的可靠性分配的多指标模糊优化模型.针对基本粒子群(PSO)算法易陷入早熟状态以及群体缺乏多样性等不足之处,将人工免疫系统(AIS)原理与改进的粒子群算法有机结合,并对粒子的飞行速度进行控制,提出一种基于人工免疫的粒子群算法(AI-PSO).将该算法应用于系统可靠性优化求解中,仿真试验结果表明,相比其他算法而言,该算法具有较强的全局搜索能力,其优化结果更为合理.

基于正交试验设计的克隆选择函数优化

将正交试验设计引入到克隆选择操作中,设计出基于正交试验的克隆选择操作(clonal selection operation based on orthogonal experiment design,简称CSO-OED),并将其加入到典型的克隆选择算法中,设计出并联式的CSO+CSO-OED(Ⅰ)算法和串联式的CSO+CSO-OED(Ⅱ)算法.将新设计的算法用于9个经典的测试函数和6个复杂的测试函数进行对比测试,实验结果表明,CSO-OED能够有效地保持种群的多样性,避免算法不成熟收敛.CSO+CSO-OED(Ⅰ)和CSO+CSO-OED(Ⅱ)将全局搜索和局部搜索分开进行优化,对比实验表明,这种搜索策略不但能够保证算法的收敛性,还能有效地提高搜索解的精度,增强算法的鲁棒性.

人工免疫系统研究的新进展

综述了人工免疫系统的最新研究成果。首先简述了生物免疫系统的信息处理机理 ,其次介绍了独特型人工免疫网络、多值免疫网络、免疫联想记忆等人工免疫模型 ,以及反向选择、免疫遗传、克隆选择等五类免疫学习算法 ,最后介绍了人工免疫系统的应用 。

一种新的免疫进化算法及其性能分析

基于免疫系统中的进化机理,提出了一种免疫进化算法。首先引入了邻域概念,并通过定义扩展半径和突变半径两个新算法参数而构造了较小和较大两个邻域。进而给出了扩展和突变操作分别利用这两个邻域进行局部和全局搜索,实现了从全局到局部的两层邻域搜索机制。分析了算法的优化机理和收敛性。仿真结果表明该算法具有不易陷入局部最优、解的精度高、收敛速度快等优点。

约束优化问题的免疫混沌算法

结合免疫算法极强的全局搜索能力以及混沌优化方法适合局部搜索的特点,提出了一种新的免疫混沌算法.从一组可行解出发,采用免疫算法通过克隆选择、克隆扩增、高频变异和审查形成记忆细胞,并将其作为全局近似最优解,然后采用混沌优化方法按照混沌运动规律在近似最优解的邻域内进行局部搜索并审查,从而获得全局精确最优解.审查过程包含了对约束条件的处理,即对新产生的候选解进行审查,保留满足约束条件的可行解.利用该算法对几个经典约束优化问题进行了仿真测试,与以往方法相比获得了更优的结果,表明该算法是一种解决约束优化问题的有效方法.

基于人工免疫算法的电梯交通动态分区的优化

提出了一种利用新兴的人工免疫算法优化电梯高峰客流动态分区的新方法 ,实现了非连续楼层的优化计算 .剖析了电梯交通的动态分区模型 ,设计了动态分区模型的人工免疫算法 ,并对之做了比较仿真研究 .仿真结果表明 ,基于该算法的动态分区在处理客流分布不均匀的电梯交通时 ,表现出良好的性能 .该算法能快速地找到电梯交通的最优分区或者次最优分区 ,具有一定的应用价值 .

基于免疫算法和EDA的混合多目标优化算法

在免疫多目标优化算法的基础上,引入了分布估计算法(EDA)对进化种群进行建模采样的思想,提出了一种求解复杂多目标优化问题的混合优化算法HIAEDA(hybrid immune algorithm with EDA for multi-objective optimization).HIAEDA的进化过程混合了两种后代产生策略:一种是基于交叉变异的克隆选择算子,用于在父代种群周围进行局部搜索的同时开辟新的搜索区域;另一种是基于EDA的模型采样算子,用于学习多目标优化问题决策变量之间的相关性,提高算法求解复杂多目标优化问题的能力.在分析两种算子搜索行为的基础上,讨论了两者在功能上的互补性,并利用有限马尔可夫链的性质证明了HIAEDA算法的收敛性.对测试函数和实际工程问题的仿真实验结果表明,HIAEDA与NSGAII算法和基于EDA的进化多目标优化算法RM-MEDA相比,在收敛性和多样性方面均表现出明显优势,尤其是对于决策变量之间存在非线性关联的复杂多目标优化问题,优势更为突出.