自适应分区多代理模型交互式遗传算法

为提高交互式遗传算法的性能.提出一种自适应分区多代理模型交互式遗传算法.该算法基于关键维分割进化初期的搜索空间,同时基于进化进程、逼近精度以及用户评价敏感度,自适应地分割进化中后期的搜索空间.在子空间上,采用多类代理模型学习用户对进化个体评价,并用于评价后续进化的部分或全部个体.将该算法应用于服装进化设计系统,实验结果表明,算法在种群多样性、减轻用户疲劳及用户对优化结果满意度等方面均具有优越性.     

利用混沌差分进化算法预测RNA二级结构

RNA二级结构预测在生物信息学中具有重要意义。本文针对RNA二级结构预测，提出了一种混沌差分进化算法。算法对种群进行混沌初始化，利用混沌扰动产生新的个体，缩小搜索空间；根据个体的适应值和种群密度自适应地对个体进行混沌更新，改善了种群的多样性。该算法充分利用了差分进化算法速度快以及混沌的遍历性、随机性和规律性等特点，有效克服了早熟现象，提高了算法的全局搜索能力。实验证明了算法的有效性。     

自适应分区多代理模型交互式遗传算法

为提高交互式遗传算法的性能,提出一种自适应分区多代理模型交互式遗传算法&.该算法基于关键维分割进化初期的搜索空间,同时基于进化进程,逼近精度以及用户评价敏感度,自适应地分割进化中后期的搜索空间.在子空间上,采用多类代理模型学习用户对进化个体评价,并用于评价后续进化的部分或全部个体.将该算法应用于服装进化设计系统,实验结果表明,算法在种群多样性,减轻用户疲劳及用户对优化结果满意度等方面均具有优越性.

     

一种求解数值优化问题的进化规划算法

针对进化规划算法收敛速度慢、容易早熟收敛等问题,提出一种基于探测变异的进化规划算法。该算法通过降维得到多个探测变异量,对个体进行探测变异,使个体始终向适应度好的方向进化,并利用自适应高斯变异标准差伸缩搜索空间,使个体跳出局部最优解。通过3个经典算例对其性能进行测试,实验结果证明该算法收敛速度快,求解质量高,可以解决早熟收敛等问题。     

基于多近似模型的交互式遗传算法

人的疲劳向题是交互式遗传算法的核心问题,它制约了交互式遗传算法在复杂优化问题中的应用.为了解决该问题,本文提出基于多近似模型的交互式遗传算法.该算法首先将搜索空间划分,然后利用传统交互式遗传算法得到的数据,在不同子空间生成不同的近似模型,最后采用该模型近似人对进化个体的评价,从而减少人评价的数量,有效解决人的疲劳问题.算法性能分析及在服装进化设计系统中的应用验证了其有效性.     

引入进化梯度的改进小生境遗传算法

针对基本遗传算法易于早熟及局部寻优能力较差等不足,提出了一种引入进化梯度的改进小生境混合遗传算法(GNGA)。利用进化梯度信息调整个体向更优解进化,并根据进化代数自适应调整实数编码个体的交叉量和变异量,增强了局部寻优能力和解的精度。基于排挤的小生境算法的引入,保持了种群的个体多样性以克服早熟。在Shubert函数上的仿真结果表明,与小生境遗传算法相比该算法能有效提高解的精度及收敛速度,找到更多最优解。     

粒子群分形进化算法

在传统粒子群优化(PSO)算法的基础上,提出粒子群分形进化算法(FEPSO).FEPSO利用分形布朗运动模型中的无规则运动特性模拟优化目标函数未知特性,隐含的趋势变化模拟优化目标函数极值变化的总趋势,从而克服个体过于随机进化和早熟的现象.与传统的PSO算法相比,文中算法中每个粒子包含分形进化阶段.在分形进化阶段,粒子在解的子空间以不同的分形参数进行分形布朗运动方式搜索解空间,并对其分量进行更新.仿真实验结果表明,该算法对大部分标准复合测试函数都具有较强的全局搜索能力,其性能超过国际上最近提出的基于PSO的改进算法.     

混合量子差分进化算法及应用

量子进化算法基于量子旋转门更新量子比特状态影响了算法搜索性能．提出一种差分进化（DE）与和声搜索（Hs）相结合更新量子比特状态的混合量子差分进化算法（HQDE）．该方法采用实数量子角形式编码染色体,设计一种由差分进化计算更新量子位状态的量子差分进化算法（QDE）和一种由和声搜索更新量子位状态的量子和声搜索（QHS）,并相互机制融合,采用两种不同进化策略共同作用产生种群新量子个体以克服常规算法中早熟及收敛速度慢等缺陷;在此基础上,算法还引入量子非门算子对当前最劣个体以一定概率选中的量子比特位进行变异操作增强算法跳出局部最优解能力．理论分析证明该算法收敛于全局最优解．0／1背包问题及旅行商问题实例测试结果验证了该方法有效性．     

基于微粒群算法与模拟退火算法的协同进化方法

提出了一种基于模拟退火与微粒群算法的协同进化方法,利用了微粒群算法的易实现性、局部快速收敛性以及模拟退火算法的全局收敛性.通过两种算法的协同搜索,可以有效克服微粒群算法的早熟收敛.仿真结果表明,本文的协同进化方法不仅具有较好的全局收敛性能,而且具有较快的收敛速度.文章从理论上证明了该方法以概率1收敛于全局最优解.     

基于双种群的进化策略算法

针对进化策略算法收敛速度较慢、容易早熟的问题,提出一种新的基于双种群的改进进化策略算法。将种群划分为规模较小的精英子群和规模较大的普通子群。精英子群用于存放种群中最优秀的个体,普通子群用于存放种群中的普通个体。对不同的子群采用不同的变异策略,使种群在解空间具有尽可能分散的全局搜索能力的同时在局部具有尽可能精细的局部搜索能力。通过理论分析证明了算法的正确性,对几个典型的函数应用该算法进行模拟进化实验,也取得了良好的效果。     

移动Agent多任务调度算法

提出一种可覆盖全部解空间的移动agent多任务分配与调度混合遗传算法。给出问题模型及染色体表示方法,采用禁忌表加随机算法生成初始种群,设计新的交叉机制保证交叉进化解的合法性。为促进算法的收敛,变异个体使用禁忌及任务均衡启发变异算子。还采用保持解的不降性的最佳个体保留策略。2种任务节点、3种通信代价、3种主机节点共18组图的仿真结果表明该算法进化的最优解较标准遗传算法有37.1%的平均改进量。     

Constrained motion planning for robot manipulators using local geometric information

This paper proposes a new motion planning algorithm for robot manipulator systems with path constraints. The constraint function of a manipulator determines the subspace of its joint space, and a proposed sampling-based algorithm can find a path that connects valid samples in the subspace. These valid samples can be obtained by projecting the samples onto the subspace defined by the constraint function. However, these iteratively generated samples easily fall into local optima, which degrades the search performance. The proposed algorithm uses the local geometric information and expands the search tree adaptively to avoid the local convergence problem. It increases the greediness of the search tree when it expands toward an unexplored area, which produces the benefit of reducing computational time. In order to demonstrate the performance of the algorithm, it is applied to two example problems: a maze problem using PUMA 560 under predefined constraints and a closed-chain problem using two Selective Compliance Assembly Robot Arms. The results are compared with those obtained with an existing algorithm to show the improvement in performance.     

An improved artificial bee colony algorithm based on the strategy of global reconnaissance

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence and low solution precision. Moreover, it can easily become stuck at local optima. The scout bees start to search for food sources randomly and then they share nectar information with other bees. Thus, this paper proposes a global reconnaissance foraging swarm optimization algorithm that mimics the intelligent foraging behavior of scouts in nature. First, under the new scouting search strategies, the scouts conduct global reconnaissance around the assigned subspace, which is effective to avoid premature convergence and local optima. Second, the scouts guide other bees to search in the neighborhood by applying heuristic information about global reconnaissance. The cooperation between the honeybees will contribute to the improvement of optimization performance and solution precision. Finally, the prediction and selection mechanism is adopted to further modify the search strategies of the employed bees and onlookers. Therefore, the search performance in the neighborhood of the local optimal solution is enhanced. The experimental results conducted on 52 typical test functions show that the proposed algorithm is more effective in avoiding premature convergence and improving solution precision compared with some other ABCs and several state-of-the-art algorithms. Moreover, this algorithm is suitable for optimizing high-dimensional space optimization problems, with very satisfactory outcomes.     

一种改进的禁忌搜索法在函数优化问题中的应用

禁忌搜索法对初始解、邻域个数及禁忌列表的大小等参数有比较严格的要求,这些参数直接影响着算法的优化能力。文章提出了一种改进的禁忌搜索法,它用有效空间来压缩搜索范围,这样可以提高搜索效率和全局搜索能力。用短期和长期禁忌列表存储器来保证算法能搜索到全局空间的每一点,并且不重复搜索。经过验算和分析,证明它是一种较好的全局启发式搜索法。     

On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers

In kernel-based nonlinear subspace (KNS) methods, the subspace dimensions have a strong influence on the performance of the subspace classifier. In order to get a high classification accuracy, a large dimension is generally required. However, if the chosen subspace dimension is too large, it leads to a low performance due to the overlapping of the resultant subspaces and, if it is too small, it increases the classification error due to the poor resulting approximation. The most common approach is of an ad hoc nature, which selects the dimensions based on the so-called cumulative proportion computed from the kernel matrix for each class. We propose a new method of systematically and efficiently selecting optimal or near-optimal subspace dimensions for KNS classifiers using a search strategy and a heuristic function termed the overlapping criterion. The rationale for this function has been motivated in the body of the paper. The task of selecting optimal subspace dimensions is reduced to find the best ones from a given problem-domain solution space using this criterion as a heuristic function. Thus, the search space can be pruned to very efficiently find the best solution. Our experimental results demonstrate that the proposed mechanism selects the dimensions efficiently without sacrificing the classification accuracy.     

基于搜索空间划分的概念生成算法

概念格作为形式概念分析理论中的核心数据结构,在机器学习、数据挖掘和知识发现、信息检索等领域得到了广泛的应用.概念格的构造在其应用过程中是一个主要问题.提出了一种基于搜索空间划分的概念生成算法SSPCG(search space partition based concepts generation),它将属性集合的幂集看作初始闭包搜索空间,迭代地将每个搜索空间划分为一些子搜索空间,并引入了子搜索空间的有效性判断,只搜索那些能生成正规闭包的子搜索空间,有效地提高了搜索效率;同时,在计算闭包过程中保存一些必要的中间结果,用来提高闭包运算速度.由于所有子搜索空间是独立的,所以该算法可以很容易地扩展为并行算法.在随机生成的数据集和真实数据集上进行的实验测试表明,本算法的时间性能要优于Ganter提出的NextClosure算法.     

A space search optimization algorithm with accelerated convergence strategies

Evolutionary algorithms (EAs), which have been widely used to solve various scientific and engineering optimization problems, are essentially stochastic search algorithms operating in the overall solution space. However, such random search mechanism may lead to some disadvantages such as a long computing time and premature convergence. In this study, we propose a space search optimization algorithm (SSOA) with accelerated convergence strategies to alleviate the drawbacks of the purely random search mechanism. The overall framework of the SSOA involves three main search mechanisms: local space search, global space search, and opposition-based search. The local space search that aims to form new solutions approaching the local optimum is realized based on the concept of augmented simplex method, which exhibits significant search abilities realized in some local space. The global space search is completed by Cauchy searching, where the approach itself is based on the Cauchy mutation. This operation can help the method avoid of being trapped in local optima and in this way alleviate premature convergence. An opposition-based search is exploited to accelerate the convergence of space search. This operator can effectively reduce a substantial computational overhead encountered in evolutionary algorithms (EAs). With the use of them SSOA realizes an effective search process. To evaluate the performance of the method, the proposed SSOA is contrasted with a method of differential evolution (DE), which is a well-known space concept-based evolutionary algorithm. When tested against benchmark functions, the SSOA exhibits a competitive performance vis-a-vis performance of some other competitive schemes of differential evolution in terms of accuracy and speed of convergence, especially in case of high-dimensional continuous optimization problems.     

基于最近邻子空间搜索的两类文本分类方法

在文本分类中,最近邻搜索算法具有思想简单、准确率高等优点,但通常在分类过程中的计算量较大。为克服这一不足,提出了一种基于最近邻子空间搜索的两类文本分类方法。首先提取每一类样本向量组的特征子空间,并通过映射将子空间变换为高维空间中的点,然后把最近邻子空间搜索转化为最近邻搜索完成分类过程。在Reuters-21578数据集上的实验表明,该方法能够有效提高文本分类的性能,具有较高的准确率、召回率和F1值。     

一种混合粒子群算法及其在Job Shop问题中的应用

粒子群算法是一种新颖的演化计算技术,具有思想简单、容易实现的优点,被广泛应用于连续空间的优化。结合遗传算法的思想提出一种新的进化方式并用于Job Shop离散空间优化,进一步结合粒子群算法的群体多样性和禁忌搜索算法的集中搜索性提出一种粒子群算法和禁忌搜索算法的混合策略。用Job Shop问题作为测试基准,仿真试验显示混合粒子群算法是可行和有效的。