共查询到20条相似文献,搜索用时 140 毫秒
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基于空间划分的细粒度并行演化算法 总被引:1,自引:1,他引:0
引入(μ+1)选择策略,提出在群体形成的最小凸集中随机均匀地生成新个体的空间划分选择策略,并将其引入细粒度并行演化模型中,提出了应用于此模型的新算法。给出了并行动算求解的仿真实例,并分析了新算法在防止早熟收敛方面的特性。 相似文献
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空间中存在着大量的流星体和空间碎片,这种数量持续增长的空间物体无疑将对未来太空任务安全带来巨大挑战和负面影响.本文回顾了流星体和空间碎片模型的发展历史,详细阐述了各模型的主要特性与适用范围,重点讲解了碎片从解体事件发生到未来空间碎片总量演变过程中不同模型的合理运用,梳理了空间事件与模型之间的关系,对比了不同模型的优缺点并给出了选用建议,最后对未来我国自有太空体系模型的构建提出了展望. 相似文献
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基于MapObjects的空间拓扑关系的建立 总被引:4,自引:0,他引:4
MO(MapObjects)是广泛应用于GIS软件开发的组件,但是它的数据模型的局限性决定了它不能够独立支持空间拓扑关系。而空间拓扑关系是GIS中空间分析的基础,从而限制了它在空间分析方面的应用。该文针对MO不能独立支持空间拓扑关系的问题,分析了空间拓扑关系和MO的空间数据模型,设计了用于存储拓扑关系数据的数据库,给出了建立空间拓扑关系的过程,提出并实现了一种基于MO的空间拓扑关系的建立方法,扩展了MO的空间分析功能,拓展了MO的应用范围。 相似文献
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由于空间问题固有的复杂性和不确定性,空间关系的描述普遍采用定性的方法。方向关系是一类重要的空间关系,它在空间数据建模、空间查询、空间分析、空间推理等过程中起着重要的作用。本文以投影模式的方向关系模型为基础,给出了利用字符串表示方法进行定性方向关系判定的方法及规则,并提出了一种简便有效的方向关系编码方法。 相似文献
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在很多有效的聚类算法中,DBSCAN算法对于聚类空间数据有着非常好的性能,依赖于基于密度的聚类定义,DBSCAN可以发现任意形状的聚类,而且执行效率很高。但是,DBSCAN没有考虑非空间属性,而非空间属性对聚类的结果也起着十分重要的作用。在DBscAN的基础上,参考DBRS的概念,进一步考虑了非空间属性的数据类型,从而提出了可以处理空间和非空间数据的新的聚类方法,并给出了主要的算法。 相似文献
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基于试题空间的学习诊断方法 总被引:2,自引:0,他引:2
在智能教学系统中,知识空间理论提供了一种描述知识结构的方法,常被用于进行对学生学习状态的诊断。本文对已有的知识空间理论进行了改进,提出了试题空间的概念,给出了基于最短路径的最优诊断原理,以此为基础,给出了一种新的更为有效的学习诊断方法.通过基于ID3算法的决策树生成方法对之进行了算法实现。研究表明,这种方法能够提高诊断的效率。 相似文献
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To deal with large-scale problems that often occur in industry, the authors propose design space optimization with design
space adjustment and refinement. In topology optimization, a design space is specified by the number of design variables,
and their layout or configuration. The proposed procedure has two efficient algorithms for adjusting and refining design space.
First, the design space can be adjusted in terms of design space expansion and reduction. This capability is evolutionary
because the design domain expands or reduces wherever necessary. Second, the design space can be refined uniformly or selectively
wherever and whenever necessary, ensuring a target resolution with fewer elements, especially for selective refinement. Accordingly,
the proposed procedure can handle large-scale problems by solving a sequence of smaller problems. Two examples show the efficiency
of the proposed approach. 相似文献
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一种具有双重进化空间的扩展粒子群优化算法 总被引:1,自引:0,他引:1
为了使粒子群优化(PSO)适于求解更多类问题,提出一种由动力空间和制导空间共同进化的改进粒子群优化算法-具有双重进化空间的扩展粒子群优化算法(简记EPSO).在EPSO中,在演化转换映射的作用下,首先将动力空间中对粒子辅助位置的进化转换为制导空间中对主导位置的进化,然后基于对主导位置的择优选择操作实现算法的进化过程.EPSO克服了PSO仅适于求解连续域最优化问题的缺陷,也非常适于求解离散组合优化问题.对于随机3-SAT问题、背包问题和TSP问题,通过与PSO、ACO和GA等算法的计算对比表明:EPSO是一种继承了PSO优点的高效、扩展演化算法. 相似文献
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针对复杂网络数据量大,与知识存在与/或关系及不易管理等特性,探讨和研究了复杂网络与知识网络之间的关系和演化过程.采用粒商空间理论构建了复杂网络与知识网络协同进化模型,提出了基于粒计算的复杂网络协同进化算法,该方法将双库融合机制及变区域策略应用到协同进化中,较好地解决了复杂网络与知识网互相作用、协同演化等问题.通过实验与比较,验证了此方法的有效性和可行性. 相似文献
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目前,多目标进化算法在众多领域具有极高的应用价值,是优化领域的研究热点之一.分析已有多目标进化算法在保持种群多样性方面的不足并提出一种基于解空间划分的自适应多目标进化算法(space division basedadaptive multiobjective evolutionary algorithm,简称SDA-MOEA)来解决多目标优化问题.该方法首先将多目标优化问题的解空间划分为大量子空间,在算法进化过程中,每个子空间都保留一个非支配解集,以保证种群的多样性.另外,该方法根据每个子空间推进种群前进的距离,自适应地为每个子空间分配进化机会,以提高种群的进化速度.最后,利用3组共14个多目标优化问题检验SDA-MOEA的性能,并将SDA-MOEA与其他5个已有多目标进化算法进行对比分析.实验结果表明:在10个问题上,算法SDA-MOEA显著优于其他对比算法. 相似文献
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Conventional evolutionary algorithms operate in a fixed search space with limiting parameter range, which is often predefined
via a priori knowledge or trial and error in order to ‘guess’ a suitable region comprising the global optimal solution. This
requirement is hard, if not impossible, to fulfil in many real-world optimization problems since there is often no clue of
where the desired solutions are located in these problems. Thus, this paper proposes an inductive–deductive learning approach
for single- and multi-objective evolutionary optimization. The method is capable of directing evolution towards more promising
search regions even if these regions are outside the initial predefined space. For problems where the global optimum is included
in the initial search space, it is capable of shrinking the search space dynamically for better resolution in genetic representation
to facilitate the evolutionary search towards more accurate optimal solutions. Validation results based on benchmark optimization
problems show that the proposed inductive–deductive learning is capable of handling different fitness landscapes as well as
distributing nondominated solutions uniformly along the final trade-offs in multi-objective optimization, even if there exist
many local optima in a high-dimensional search space or the global optimum is outside the predefined search region.
Received 15 January 2001 / Revised 8 June 2001 / Accepted in revised form 24 July 2001 相似文献
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一种协调勘探和开采能力的粒子群算法 总被引:2,自引:0,他引:2
提出一种新的协调勘探和开采能力的粒子群优化算法. 该算法将种群分为随机子群和进化子群, 随机子群增加了算法全局解空间的勘探能力, 在运行过程中通过随机子群进化信息生成解优胜区域指导进化粒子向着最优解子空间逼近. 为了提高算法收敛速度, 算法只在进化子群进入收敛阶段时才对其进行指导, 以防止增加种群多样性导致算法开采能力下降的问题. 将此算法与其他改进粒子群算法进行比较, 实验结果表明, 该算法有较好的全局收敛性, 不仅能有效地克服其他算法易陷入局部极小值的缺点, 而且算法收敛速度和稳定性都有显著提高. 相似文献
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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. 相似文献
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Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts. 相似文献