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1.
为了改善人工免疫多目标进化算法的分布性,引入聚集密度以进行Pareto最优解集的更新。其基本思想为:首先计算群体中每个个体的聚集密度,再根据目标函数值和聚集密度定义一个偏序集,然后采用比例选择原则依次从偏序集中选择个体,更新精英集。通过数值实验,用量化指标研究了新算法的收敛性和分布性,结果表明:新算法的收敛性与常规人工免疫多目标进化算法相当,但分布性有了明显提高。  相似文献   

2.
为了改善协同进化多目标优化算法性能,引入了聚集密度对超级个体集合进行更新。其基本思想是:首先计算种群中各个体的聚集密度,再定义一个偏序集,然后根据一定的比例依次从偏序集中选择个体更新。根据数值试验和量化指标测试了新算法的收敛性与分布性。结果表明,新算法在收敛性方面与常规协同进化多目标算法相当,但其分布性获得了一定程度的改善。  相似文献   

3.
多目标进化算法有两个重要研究内容:最优解集的构造和解的分布性。用擂台赛法则构造非支配集具有较高的效率,而聚集密度方法既能从宏观上刻画群体的多样性与分布性,同时也比较好地刻画了个体之间的内在关系。将聚集密度技术引入基于擂台赛法则的多目标进化算法。数值计算表明,这种新的算法既保持了擂台赛法则较高的运行速度,又改善了群体的分布度,提高了种群的多样性,避免了过早收敛于局部最优解的现象。  相似文献   

4.
建立了供水调度模型,利用基于分解的多目标进化算法,首先将供水调度问题分解为若干单目标,然后根据分布估计的思想对各个单目标建立概率模型,通过采样产生新的个体。利用非支配排序法进行选择,得到最优解。实验表明,该算法对求解供水调度优化问题具有较好的多样性和均匀性,并且降低了算法的计算复杂度。  相似文献   

5.
针对现有的量子克隆遗传算法存在算法效率低、收敛速度较慢、易于陷入局部值等缺陷.文章通过引入量子交叉.加快算法收敛速度,使用自适应量子旋转门更新策略,加快最优解的搜索;采用量子灾变策略,避免早熟和进化停滞.由此给出了一种改进的量子克隆遗传算法(NQCGA).仿真结果表明:所提算法的多用户检测器的误码率、收敛速度、抗多址干...  相似文献   

6.
最优解集的构造和解的分布性是多目标进化算法的两个重要研究内容。用擂台赛法则构造非支配集具有较高的效率,而小生境共享技术可以提高种群的多样性。本文将小生境共享技术引入基于擂台赛法则的多目标进化算法,数值实验表明:改进后的算法保持了擂台赛算法运行效率高的特点,而且具有较佳的分布度。  相似文献   

7.
许利军  杨棉绒 《科技通报》2012,28(5):171-174
针对单种群遗传算法在求解QoS组播路由问题中存在的容易早熟、收敛性差等缺陷,提出了一种基于多种群遗传算法的QoS组播路由算法。该算法在初始化过程中采用多种初始化算法生成了不同种群,并设计了多种交叉、变异操作,保证了种群间进化过程的独立性和算法的多样性;增加了种群间协调机制,提高了算法的整体收敛性。仿真结果证明了多种群遗传算法的有效性和优越性。  相似文献   

8.
何学文  张磊 《大众科技》2012,14(3):16-17
对识别后的语音文档进行了向量空间模型的建立,针对得到的高维稀疏矩阵提出了基于局部敏感哈希的语音文档分类算法,算法能够直接在高维稀疏矩阵上进行分类,无需降维。此外,在构建局部敏感哈希函数的时候结合了稳定分布。实验证明,局部敏感哈希算法能够对语音文档进行合理有效的分类,同时获得了较小的时间复杂度。  相似文献   

9.
针对标准免疫克隆算法在求解TSP问题的过程中还存在收敛性不好、效率低下等问题。本文设计了一种以非线性混沌优化免疫克隆算法为基础的TSP问题求解模型,最先运用混沌变量完成抗体编码,利用混沌机制等产生克隆初始种群,然后对后代进行克隆,并将混沌算法引入到免疫克隆变异中,在进化中将混沌变量映射到实际优化问题中计算抗体的亲和度,之后再优化选择算子,为种群的多样性提供保证。实验仿真结果表明,本文提出的改进IA算法在执行时间和迭代次数上都优于传统免疫算法,在收敛问题上,比传统免疫算法更具有优势。  相似文献   

10.
马占春  宁小美 《科技通报》2012,28(10):155-157
针对EA(evolutionary algorithm)在机器人路径规划中局部收敛和收敛速度慢的缺点,结合云模型的优良特性,提出了基于云模型的路径规划算法.本算法采用正态云算子在路径池中进行进化和变异.进化过程中出现跨代精英路径时说明靠近了较优路径,就可以缩小进化范围,同时还利用了往次进化过程中的优化结果,来保证最终结果的准确性.仿真实验证明,本算法不但提升了进化速度,同时提高了路径的可靠性.  相似文献   

11.
Aligning time series of different sampling rates is an important but challenging task. Current commonly used dynamic time warping methods usually suffer from pathological temporal singularity problem. In order to overcome this, we transform the alignment task to a spatial-temporal multi-objective optimization (MOO) problem. Existing MOO algorithms are always inefficient in finding Pareto optimal alignment solutions due to their insufficiency in maintaining convergence and diversity among the obtained Pareto solutions. In light of this, we propose a novel and efficient MOO algorithm Cell-MOWOA which integrates Cellular automata with the rising Whale Optimization Algorithm to find Pareto optimal alignment solutions. Innovative multi-variate non-linear cell state evolutionary rules are designed within Pareto solution external archive to improve the convergence and diversity of the Pareto solutions, and novel whale population updating mechanism is designed to accelerate the convergence to the Pareto front. Besides, new integer whale updating mechanism is presented to transform real-number whale solutions to integer whale solutions. Experimental results on 85 gold-standard UCR time series datasets showed that Cell-MOWOA outperformed six state-of-the-art baselines by 24.53% in average in increasing alignment accuracy and 42.66% in average in reducing singularity. Besides, it achieved outstanding runtime efficiency, especially on long time series datasets.  相似文献   

12.
As a recent swarm intelligence optimization algorithm, sparrow search algorithm (SSA) is widely adopted in many real-world problems. However, the solutions to the limitations of SSA (such as low accuracy of convergence and tendency of trapping into local optimum) are still not available. To address these issues, we propose an enhanced multi-strategies sparrow search algorithm (EMSSA) based on three strategies specifically addressing the limitations of SSA: 1) in the uniformity-diversification orientation strategy, we propose an adaptive-tent chaos theory to allow more diversity and greater randomness in the initial population; 2) in the hazard-aware transfer strategy, we construct a weighted sine and cosine algorithm based on the growth function to avoid trapping into the state of local optima stagnation; 3) in the dynamic evolutionary strategy, we design the similar perturbation function and introduce the triangle similarity theory to improve the exploration capability. The performance of EMSSA in solving the continuous optimization problems about the 23 benchmark functions, CEC2014, and CEC2017 problems is much improved than that of SSA and other state-of-the-art algorithms. Furthermore, the results of the density peak clustering optimization show that the EMSSA outperforms SSA.  相似文献   

13.
郑丹文 《科教文汇》2011,(31):178-179
本文围绕如何有效做好高校党委常委会议的文书归档工作,提出了一些具有可操作性和针对性的做法,如归档工作要落实专人负责并明确职责,要确定归档内容和保管期限,专兼职档案人员要分工合作有序进行归档;事后要明确查档规定等。  相似文献   

14.
Personalized recommender systems have been extensively studied in human-centered intelligent systems. Existing recommendation techniques have achieved comparable performance in predictive accuracy; however, the trade-off between recommendation accuracy and diversity poses new challenges, as diversification may lead to accuracy loss, whereas it can solve the over-fitting problem and enhance the user experience. In this study, we propose a heuristic optimization-based recommendation model that jointly optimizes accuracy and diversity performance by obtaining a set of optimized solutions. To establish the best accuracy-diversity balance, a novel trajectory-reinforcement-based bacterial colony optimization algorithm was developed. The improved bacterial colony optimization algorithm was comprehensively evaluated by comparing it with eight popular and state-of-the-art algorithms on ten benchmark testing problems with different degrees of complexity. Furthermore, an optimization-based recommendation model was applied to a real-world recommendation dataset. The results demonstrate that the improved bacterial colony optimization algorithm achieves the best overall performance for benchmark problems in terms of convergence and diversity. In the real-world recommendation task, the proposed approach improved the diversity performance by 1.62% to 8.62% while maintaining superior (1.88% to 40.32%) accuracy performance. Additionally, the proposed personalized recommendation model can provide a set of nondominated solutions instead of a single solution to accommodate the ever-changing preferences of users and service providers. Therefore, this work demonstrates the excellence of an optimization-based recommendation approach for solving the accuracy-diversity trade-off.  相似文献   

15.
陈洪美 《科教文汇》2011,(26):187-188
当前教师职业档案存在目的功利、外延泛化、标准芜杂、多头管理等方面的问题。本文着眼于此类档案服务于教师自我生长发展和学校制度文化建设的价值追求,对此类档案的发展趋势进行了利弊分析,并从观念更新、建章立制和规范管理等几个方面提出相应策略。  相似文献   

16.
叙述了以Documentum平台为基础的档案管理系统功能和设计思路,重点阐述了某企业档案管理系统中软、硬件的实现方案,并列举了系统中文件归档及借阅审批等重要功能。  相似文献   

17.
A matrix-based framework for the modeling, analysis and dynamics of Bayesian games are presented using the semi-tensor product of matrices. Static Bayesian games are considered first. A new conversion of Bayesian games is proposed, which is called an action-type conversion. Matrix expressions are obtained for Harsanyi, Selten, and action-type conversions, respectively. Certain properties are obtained, including two kinds of Bayesian Nash equilibria. Then the verification of Bayesian potential games is considered, which is proved to test the solvability of corresponding linear equations equivalently. Finally, the dynamics of evolutionary Bayesian games are considered. Two learning rules for Bayesian potential games are proposed, which are type-based myopic best response adjustment and logit response rule, respectively. Markovian dynamic equations are obtained for the proposed strategy updating rules and convergence is proved.  相似文献   

18.
张苏荣  王文平 《软科学》2011,25(1):24-27,36
运用演化博弈论的方法,建立企业间合作演化博弈模型,研究知识更新对企业合作策略选择的影响。模型分析表明,企业均选择合作策略的概率与知识更新的收益系数及相互间的信任水平正相关,与知识更新的合作成本系数和风险系数负相关。在此基础上,提出了激励企业通过知识网络开展合作的策略建议。  相似文献   

19.
In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.  相似文献   

20.
In this paper, the identification of the Wiener–Hammerstein systems with unknown orders linear subsystems and backlash is investigated by using the modified multi-innovation stochastic gradient identification algorithm. In this scheme, in order to facilitate subsequent parameter identification, the orders of linear subsystems are firstly determined by using the determinant ratio approach. To address the multi-innovation length problem in the conventional multi-innovation least squares algorithm, the innovation updating is decomposed into sub-innovations updating through the usage of multi-step updating technique. In the identification procedure, by reframing two auxiliary models, the unknown internal variables are replaced by using the outputs of the corresponding auxiliary model. Furthermore, the convergence analysis of the proposed algorithm has shown that the parameter estimation error can converge to zero. Simulation examples are provided to validate the efficiency of the proposed algorithm.  相似文献   

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