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In this paper, development of the model of collective interaction in the system “buses-passengers” represented in [1] is discussed. The point is that route busing depends on the travel time and alighting at bus stops; these times depend on boarding and passengers crowding at bus stops. In its turn, the number of passengers in the passenger compartment and at bus stops depends on the buses motion on the route.General equations and hypotheses describing the system behavior are formulated. A case of buses motion according to the schedule. Equations for deviations of system parameters from the values specified by the schedule are obtained and studied in the linear approximation. 相似文献
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The crowding approach to niching in genetic algorithms 总被引:1,自引:0,他引:1
A wide range of niching techniques have been investigated in evolutionary and genetic algorithms. In this article, we focus on niching using crowding techniques in the context of what we call local tournament algorithms. In addition to deterministic and probabilistic crowding, the family of local tournament algorithms includes the Metropolis algorithm, simulated annealing, restricted tournament selection, and parallel recombinative simulated annealing. We describe an algorithmic and analytical framework which is applicable to a wide range of crowding algorithms. As an example of utilizing this framework, we present and analyze the probabilistic crowding niching algorithm. Like the closely related deterministic crowding approach, probabilistic crowding is fast, simple, and requires no parameters beyond those of classical genetic algorithms. In probabilistic crowding, subpopulations are maintained reliably, and we show that it is possible to analyze and predict how this maintenance takes place. We also provide novel results for deterministic crowding, show how different crowding replacement rules can be combined in portfolios, and discuss population sizing. Our analysis is backed up by experiments that further increase the understanding of probabilistic crowding. 相似文献
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《Applied Soft Computing》2008,8(1):88-95
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. 相似文献
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Intelligence modeling for coping strategies to reduce emergency department overcrowding in hospitals
Chien-Lung Chan Hsin-Tsung Huang Huey-Jen You 《Journal of Intelligent Manufacturing》2012,23(6):2307-2318
Emergency department (ED) crowding is a common challenge for hospitals across the globe. The efficiency and effectiveness of ED services can be improved through identifying the causing ED crowding and modeling the prediction of ED crowding. The nature of ED crowding involves a complex dynamics of intertwined processes and workflows among the different departments within a hospital; thus, the problem cannot be tackled by examining ED alone. It is important to build a model which can identify the factors causing ED crowding and validate the coping strategies of hospitals. This study proposes an intelligence model which first introduces the well-know decision tree method to fit an accommodated nonlinear association and obtain intelligent grading rules of ED crowding; Then it integrates the intelligent grading rules and indexes of coping strategies to construct a hierarchical linear model. The results simultaneously solved traditional modeling issue of high correlation among independent variables and un-convergence. It also provides a better illustration of ED crowding phenomena with more accurate model fitting, as well as a clear linkage between coping strategies and the factors causing ED crowding. Furthermore, our proposed model can have a better understanding of problem nature and guild a better bed management for decision makers. It can also detect intelligently whether hospitals have drawn up active or passive bed management strategies to cope with ED crowding. 相似文献
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适应值共享拥挤遗传算法 总被引:5,自引:0,他引:5
保持遗传算法在演化过程中的种群多样性,是将遗传算法成功应用于解决多峰优化问题和多目标优化问题的关键。适应值共享遗传算法和拥护遗传算法分别从不同角度改善了遗传算法的搜索能力,是寻找多个最优解的常用算法。将这两种算法的优点加以结合,提出适应值共享拥护遗传算法。数值测试结果表明,该算法比标准适应值共享遗传算法和确定性拥挤遗传算法具有更强的搜索能力。 相似文献
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采用循环拥挤排序策略的改进 NSGA-II算法 总被引:2,自引:0,他引:2
采用循环拥挤排序策略,形成改进的NSGA-Ⅱ算法.循环拥挤排序策略首先计算同一级非支配解的拥挤距离,删除其中拥挤距离最小的解;然后重新计算剩余解之间的拥挤距离,再次删除其中拥挤距离最小的解.以次类推,直到选出指定数量支配解为止.与单次拥挤距离排序相比,循环拥挤距离排序得到的解具有更好的多样性.ZDT1~ZDT4四个基准函数测试结果表明,改进的NSGA-Ⅱ比NSGA-Ⅱ具有更好的收敛性和多样性. 相似文献
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针对优化多模函数时单纯使用共享和排挤机制的遗传算法所存在的缺陷,提出了基于适应值共享的多生境排挤遗传算法。基本思想是:按照共享的思想在对个体的适应值进行调整的同时,将排挤选择和相似个体中适应度最差个体被替换的策略分别应用于选择算子和群体的进化中。理论分析和数值实验表明,该算法很好地维持了种群多样性,对于各类多峰函数具有较强的搜索能力。 相似文献
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一种改进的基于pareto解的多目标粒子群算法 总被引:1,自引:0,他引:1
研究一种改进的多目标粒子群优化算法,算法采用精英归档策略,利用粒子的个体最优定位,通过Pareto支配关系更新全体粒子最优位置,由档案库中动态提供。根据Pareto支配关系来更新粒子的个体最优位置。使用非劣解目标的密度距离度量非劣解前端的均匀性,通过删除密度距离小的非劣解提高非劣解前端的均匀性。从归档中根据粒子的密度距离大小依照概率选取作为粒子的全局最优位置,以保持解的多样性。标准函数的仿真实验结果表明,所提算法能够获得大量且较均匀的非劣解,快速地收敛于Pareto最优解前端。 相似文献
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提出了一种利用Pareto支配来求解多目标优化问题的自适应和声搜索算法(MOSAHS)。该算法利用外部种群来保存非支配解,为了保持非支配解的多样性,提出了一种基于拥挤度的删除策略,这个策略能较好地度量个体的拥挤程度。用5个标准测试函数对其进行测试,并与其他多目标优化算法相比较。实验结果表明,与其他的算法相比,提出的算法在逼近性和均匀性两方面都有很好的表现,是一种有效的多目标和声搜索算法。 相似文献
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Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure 总被引:2,自引:1,他引:1
Yao-Nan Wang Liang-Hong Wu Xiao-Fang Yuan 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2010,14(3):193-209
A self-adaptive differential evolution algorithm incorporate Pareto dominance to solve multi-objective optimization problems
is presented. The proposed approach adopts an external elitist archive to retain non-dominated solutions found during the
evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic
is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. The experiments
were performed using eighteen benchmark test functions. The experiment results show that, compared with three other multi-objective
optimization evolutionary algorithms, the proposed MOSADE is able to find better spread of solutions with better convergence
to the Pareto front and preserve the diversity of Pareto optimal solutions more efficiently. 相似文献
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论文时PCI和ISA总线的原理以及接口转换的关键技术进行了阐述,提出了通过FPGA采实现PCI到ISA总线转换设计的方法。重点介绍了在PCI总线和PCI到ISA时序转换的逻辑电路中状态机的设计方法。最后分析了PCI到ISA总线转换的核心仿真时序图。 相似文献
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基于拥挤距离的动态粒子群多目标优化算法 总被引:1,自引:0,他引:1
提出了一种改进的基于拥挤距离的动态粒子群多目标优化算法。为提高粒子的全局搜索能力,提出了新的动态变化惯性权重和加速因子的方法。引进了拥挤距离排序方法维护外部精英集和更新全局最优值。为保持非劣解的多样性,采用了小概率变异机制,并根据种群的大小选择不同的变异概率。最后,把算法应用到5个典型的多目标测试函数并与其他算法进行比较。实验结果表明,该算法所得的Pareto解集有很好的收敛性和多样性。 相似文献
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在VXI总线系统中,为了能够获得大量实时数据,需要利用CAN总线快速可靠地传输数据的性能.设计一种混合VXI总线和CAN总线的系统。在这种混合系统中要实现CAN总线上各个节点采集并传输实时数据.采用VXI—CAN这样一个消息基模块完成数据到VXI总线上的传输。通过这种VXI总线和CAN总线互相通讯获得实时数据的基本原理和方法.实现了实时数据的采集。这种数据传输的实现为VXI总线系统的设计拓宽了新的思路。 相似文献
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基于遗传蚁群算法的舰艇编队防空火力分配 总被引:1,自引:1,他引:0
提高舰艇编队的防空火力分配效率是海上防空中一件紧迫的任务.火力分配问题是NP难问题,经典的求解算法存在指数级的时间复杂度,启发性智能算法又易于陷入局部最优.提出一种基于拥挤替换思想的遗传蚁群算法用于解决水面舰艇编队防空火力分配问题,遗传算法阶段采用拥挤替换和时变性变异算子设计,以维持较好的种群多样性,蚁群算法阶段,由于有较好的初始信息素分布,在进一步求精解的时候能够避免陷入局部最优.仿真结果表明:新算法与其它算法相比,在优化性能和时间性能方面都有了较大的改善,并且分配问题规模越大,优势越明显,能较好地解决舰艇编队防空火力分配问题. 相似文献
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介绍了PCI总线的通信基础———反射波 ,揭示了PCI总线和ISA、STD总线在开发上差异巨大的内在机理 ,给出了PCI总线电气规范 ,为ISA总线的技术开发人员转到PCI总线的开发打下了理论基础 相似文献
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论文提出了一种基于拥挤度和动态惯性权重聚合的多目标粒子群优化算法,该算法采用Pareto支配关系来更新粒子的个体最优值,用外部存档策略保存搜索过程中发现的非支配解;采用适应值拥挤度裁剪归档中的非支配解,并从归档中的稀松区域随机选取精英作为粒子的全局最优位置,以保持解的多样性;采用动态惯性权重聚合的方法以使算法尽可能地逼近各目标的最优解。仿真结果表明,该算法性能较好,能很好地求解多目标优化问题。 相似文献
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