基于Spark的并行化头脑风暴优化算法及复杂多峰函数优化

头脑风暴优化BSO算法是一种新型的群体智能优化算法,启发于众人集思广益求解问题的模式,适合求解复杂多峰函数优化问题。但是,BSO求解多峰极值时需进行重复的迭代运算,面对大规模数据集时会出现计算效率与求解精度过低的现象。为解决上述问题,设计并实现了一种基于Spark的并行化头脑风暴优化算法,通过将BSO算法中计算复杂度最高的聚类与新解产生过程并行化,以提高算法的加速比与计算效率。特别地,基于并行化思想,将种群划分为多个子群进行协同演化,每个子群独立产生新解来保持种群多样性,提高算法的收敛速度。最后,利用并行化BSO算法求解多峰函数。实验表明,在并行节点的总核心数为10的情况下,并行化BSO算法计算时间节省一半,计算精度和串行BSO算法基本持平,收敛速度明显提高,实验结果说明了并行化BSO的有效性。     

Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization

This paper describes the latest version of a bi-objective multipopulation genetic algorithm (BMPGA) aiming to locate all global and local optima on a real-valued differentiable multimodal landscape. The performance of BMPGA is compared against four multimodal GAs on five multimodal functions. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multipopulation and clustering scheme, which focuses selection within the various sub-populations and results in effective identification and retention of the optima of the target functions as well as improved exploitation within promising areas. The results of the empirical comparison provide clear evidence that supports the conclusion that BMPGA is better than the other GAs in terms of overall effectiveness, applicability, and reliability. The practical value of BMPGA has already been demonstrated in applications to multiple ellipses and elliptic objects detection in microscopic imagery.      

Relationship between Creativity, Imitation, and Cultural Diversity

There are both benefits and drawbacks to cultural diversity. It can lead to friction and exacerbate differences. However, as with biological diversity, cultural diversity is valuable in times of upheaval; if a previously effective solution no longer works, it is good to have alternatives available. What factors give rise to cultural diversity? This paper describes a preliminary investigation of this question using a computational model of cultural evolution. The model is composed of neural network based agents that evolve fitter ideas for actions by (1) inventing new ideas through modification of existing ones, and (2) imitating neighbors'' ideas. Numerical simulations indicate that the diversity of ideas in a population is positively correlated with both the proportion of creators to imitators in the population, and the rate at which creators create. This is the case for both minimum and peak diversity of actions over the duration of a run.     

From observation to simulation: generating culture-specific behavior for interactive systems

In this article we present a parameterized model for generating multimodal behavior based on cultural heuristics. To this end, a multimodal corpus analysis of human interactions in two cultures serves as the empirical basis for the modeling endeavor. Integrating the results from this empirical study with a well-established theory of cultural dimensions, it becomes feasible to generate culture-specific multimodal behavior in embodied agents by giving evidence for the cultural background of the agent. Two sample applications are presented that make use of the model and are designed to be applied in the area of coaching intercultural communication.     

基于K- 均值聚类的动态多种群粒子群算法及其应用

针对粒子群算法在求解复杂的多峰问题时极易陷入局部最优解的问题,提出一种基于K-均值聚类的动态多种群粒子群算法（KDMSPSO）.在该算法中,利用K-均值聚类算法将种群分成若干个子群（聚类）;为了增强子群间的信息交流,对子群进行动态重组;在每个子群中,粒子的速度由它所在子群的中心粒子和该粒子所有邻居的信息共同调整.在基准函数测试和实际应用中,其结果显示KDMSPSO算法相比其他PSO算法具有一定的优势.     

交互式多目标文化算法优化多模态混合指标

多模态混合指标优化是一类难以求解的多目标优化问题。针对该问题,借鉴文化算法的双层结构,构建了一种能融合历史知识、标准化知识和领域知识的交互式文化算法。该算法以指标均衡性构建信度空间样本库。知识提取函数根据样本库内个体在决策空间和目标空间的特殊拥挤距离选取多模态解。将选取的多模态解作为聚类中心推荐给用户评价。根据种群的指标均衡性,知识引导自适应交叉和变异概率,扩大种群多样性。采用指标均衡性引导形势知识更新。基于个体表现型相似性估计大规模种群隐式性能指标。提出新的多模态解评价测度。将算法应用于室内布局优化问题,与代表性方法比较,验证所提算法的有效性和可用性。     

区间数型多式联运路线优化问题的混合遗传算法*

多式联运路线优化问题直接关系到货物运输的费用、时间和运输质量。首先分析了多式联运路线优化问题的数学模型及虚拟运输网络图;其次,将区间数排序的思想引入适应度函数的设计中,提出了一种求解区间数型多式联运路线优化问题的混合型遗传算法,给出了染色体编码、遗传算子设计、约束判断与调整及群体多样性控制的方法;最后用示例对算法的有效性进行了验证,算法的提出可为多式联运经营者的决策提供数据参考。     

融合分区和局部搜索的多模态多目标优化

为解决多模态多目标优化中种群多样性维持难和所得等价解数量不足问题,基于分区搜索和局部搜索,本研究提出一种融合分区和局部搜索的多模态多目标粒子群算法(multimodal multi-objective particle swarm optimization combing zoning search and local search,ZLS-SMPSO-MM)。在所提算法中,整个搜索空间被分割成多个子空间以维持种群多样性和降低搜索难度;然后,使用已有的自组织多模态多目标粒子群算法在每个子空间搜索等价解和挖掘邻域信息,并利用局部搜索能力较强的协方差矩阵自适应算法对有潜力的区域进行精细搜索。通过14个多模态多目标优化问题测试,并与其他5种知名算法进行比较;实验结果表明ZLS-SMPSO-MM在决策空间能够找到更多的等价解,且整体性能要好于所比较算法。     

基于文化算法的混合聚类方法

文化算法是一种新的进化计算方法,文化进化过程除了具有传统的进化计算模型的群体空间外,还增加了一个知识空间和支持这两个空间通信的机制。以文化算法为框架,采用K-均值模型为聚类模型,针对聚类问题设计适用于该问题的知识空间、群体空间、接受函数和影响函数,提出一种混合聚类算法KCAGA。实验证明,该算法对解决聚类问题初始化敏感以及容易陷入局部优化取得很好的效果,适用于聚类问题的解决。     

面向多峰函数优化的PASFLI算法及应用

针对基本混合蛙跳算法在高维多峰函数优化时早熟及难以找到所有全局极值的问题,提出了一种具有混合智能的多态子种群自适应混合蛙跳免疫算法,证明了算法以概率1收敛于全局最优解。该算法采用双层进化模式,融合了混合蛙跳、免疫克隆选择技术。在低层混合蛙跳操作中,加入了多态自适应子种群机制,提高了子种群多样性,有效抑制了早熟现象;在算法进化后期,提出了全局极值筛选策略,将子种群极值点提升到高层免疫克隆选择操作,进一步提高了全局寻优能力。通过复杂多峰函数仿真实验,表明该算法能够快速有效地给出全部全局最优解。     

一种多模态单亲遗传算法

遗传算法在处理复杂的、多模态优化问题时常不十分有效,很难同时搜索多个峰点.这主要是由全局选择机制和交叉算子引起的.针对上述不足,本文提出了一种多模态单亲遗传算法,目标不是发现一个最优解而是多个最优或次优解的集合.主要是对交叉算子和选择机制作了改进,群体中个体能较好地保留自己的遗传特性,大大增强了种群个体的分散性.该方法不仅易实现并行或分布计算,且群体规模可以任意选取.仿真结果验证了算法的有效性.     

Monitoring of particle swarm optimization

In this paper, several diversity measurements will be discussed and defined. As in other evolutionary algorithms, first the population position diversity will be discussed followed by the discussion and definition of population velocity diversity which is different from that in other evolutionary algorithms since only PSO has the velocity parameter. Furthermore, a diversity measurement called cognitive diversity is discussed and defined, which can reveal clustering information about where the current population of particles intends to move towards. The diversity of the current population of particles and the cognitive diversity together tell what the convergence/divergence stage the current population of particles is at and which stage it moves towards.     

基于动态双子种群的差分进化K中心点聚类算法

随着海量大数据的出现,聚类算法需要新型计算模式来提高计算速度与运行效率。本文提出一种基于动态双子种群的差分进化K中心点聚类算法DGP-DE-K-mediods(Dynamic Gemini Population based DE-K-mediods)。DGP-DE-K-mediods利用动态双子种群方法,解决聚类算法在维持种群密度的时候避免陷入局部最优的问题;采用差分进化(Differential Evolution, DE)算法来提高全局最优能力的强健性;基于Hadoop云平台来并行处理DGP-DE-K-mediods,加快算法的运行速度和效率;描述基于MapReduce的并行聚类算法的编程过程;DGP-DE-K-mediods利用UIC的大数据分类的案例数据和网络入侵检测这种大数据应用来仿真算法的效果。实验结果表明,与已有的聚类算法相比,DGP-DE-K-mediods在检测精度、运行时间上有明显的优势。     

基于种群关系的多种群粒子群协同优化算法

传统粒子群优化算法容易陷入局部最优解,搜索效率不高,针对此问题,提出了一种基于种群关系和斥力因子的多种群粒子群优化算法SRB-PSO (Swarm-Relation-Based PSO).根据当前搜索结果定义种群之间统治、对等和被统治3种关系,通过引入斥力因子来保证种群间搜索的多样性,并通过统治和被统治关系提高算法的搜索效率,从而在改善算法的全局搜索性能的同时提高解的质量.将算法与其他几种主流粒子群优化改进算法在标准测试集上进行对比,实验结果证明了SRB-PSO算法能较好地保持粒子多样性,全局搜索能力强,在解决多峰函数时的性能优于其他几种主流粒子群优化改进算法.     

一种求解高维多模态复杂问题的差分文化算法

针对在求解高维多峰值复杂问题时种群容易陷入局部搜索、求解精度低的问题,提出了一种基于自适应差分进化算法和小生境高斯分布估计的文化算法。将差分进化算法用于种群空间的优化,利用动态小生境识别算法在种群空间中识别小生境群体。信度空间利用高斯分布估计算法在小生境内进行局部优化,并将小生境特征存入进化知识库,进化知识库进一步引导种群空间,有效地保证了种群的多样性,避免了局部的重复搜索。最后,通过仿真实验测试表明,算法具有收敛速度快、求解精度高、稳定性高和全局搜索能力强等优势。     

Crowding clustering genetic algorithm for multimodal function optimization

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches and clustering operation to eliminate genetic drift. Numerical experiments on standard test functions indicate that crowding clustering genetic algorithm is superior to both standard crowding and deterministic crowding in quantity, quality and precision of multi-optimum search. The proposed algorithm is applied to the practical optimal design of varied-line-spacing holographic grating and achieves satisfactory results.     

一种面向多模函数改进的果蝇优化算法

为将果蝇优化算法有效应用在多模函数优化问题中,设计了一种优化多模函数的果蝇优化算法—基于佳点集和小生境技术的混合果蝇优化算法。首先引入数论中的佳点集概念构造初始种群,使其较均匀地分布在可行域中并且产生的模式多样性比随机分布更好,提高了算法的搜索能力及效率和稳定性;其次用小生境技术改进算法的搜索模式,更好地维持了种群的多样性使种群能快速定位较多的峰;再通过小生境熵来量化群体的多样性并选择进化方向,当小生境熵低于设定的阈值时,结合佳点搜索产生新群体给以扰动,以维持种群的多样性,否则对各个峰进行精细搜索。对七个测试函数分别进行两类仿真,结果表明,该算法不仅能够高效且高精度地找到全局极值而且能够以较高的精度定位到所有全局极值和多个次优极值,显示了较强的多峰搜索能力。     

移动电子商务情境下客户细分优化算法的研究

由于传统聚类算法的收敛过早、精度较低等缺点无法满足移动电子商务情境下的客户多样性、动态性、复杂性等特点,在研究典型客户细分领域聚类算法的基础上,提出一种结合不同聚类算法优点的混合聚类算法M-Cluster。针对移动电子商务情境下学生群体的消费模式和群集现象,构建出基于M-Cluster算法的融合LTV和RFM模型优点的CPM模型用于评价和细分客户群。     

多模态多目标差分进化算法求解非线性方程组

针对当前算法在求解非线性方程组时面临解的个数不完整、精确度不高、收敛速度慢等问题进行了研究,提出一种多模态多目标差分进化算法。首先将非线性方程组转换为多模态多目标优化问题,初始化一个随机种群并对种群中全部个体进行评价;然后通过非支配解排序和决策空间拥挤距离选择机制,挑选种群中的一半优质个体进行变异;接着在变异过程中采用一种新的变异策略和边界处理方法以增加解的多样性;最后通过交叉和选择机制使优质个体进行进化,直到搜索到全部最优解。在所选测试函数集和工程实例上的实验结果表明,该算法能有效地搜索到非线性方程组的解,并通过与当前四个算法进行比较,该算法在解的数量和成功率上具有优越性。     

Graph-based multimodal clustering for social multimedia

Real world datasets often consist of data expressed through multiple modalities. Clustering such datasets is in most cases a challenging task as the involved modalities are often heterogeneous. In this paper we propose a graph-based multimodal clustering approach. The proposed approach utilizes an example relevant clustering in order to learn a model of the “same cluster” relationship between a pair of items. This model is subsequently used in order to organize the items of the collection to be clustered in a graph, where the nodes represent the items and a link between a pair of nodes exists if the model predicted that the corresponding pair of items belong to the same cluster. Eventually, a graph clustering algorithm is applied on the graph in order to produce the final clustering. The proposed approach is applied on two problems that are typically treated using clustering techniques; in particular, it is applied on the problem of detecting social events and to the problem of discovering different landmark views in collections of social multimedia.