共查询到19条相似文献,搜索用时 277 毫秒
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将类比推理技术引入状态空间的搜索,提出了一种基于类比的启发式搜索方法AHS。该方法利用相似的过去问题的求解案例指导新问题的求解,提高了求解问题的效率。在简介基于类比的启发式搜索方法的基础上,重点讨论了实现这种方法需要解决的主要问题;然后针对状态空间的搜索,建立了一个类比求解模型ASM。论述了该求解模型的推理方法和过程;最后通过实验,验证了ASM模型的有效性。 相似文献
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MAS系统的问题求解能力分析 总被引:2,自引:0,他引:2
本文用状态空间搜索模型分析了多Agent系统(MAS)的问题求解能力,认为MAS系统中Agent之间知识的组合应用和对问题搜索方向的交互和决策是影响MAS系统问题求解能力的主要原因,在状态空间搜索模型下可以将Agent间知识的组合应用表达为不同Agent的搜索路径的组合,而Agent对搜索方向的判断是基于启发式信息做出的,从而为形式化分析MAS系统的性能建立了通用的模型.本文以A*算法为例探讨了可采纳算法下多Agent合作求解效果与Agent的知识和启发信息之间的关系,指出只有在一定条件下MAS系统才会获得更好的解题能力.本文还对非可采纳算法下MAS系统性能分析方法提出了初步看法. 相似文献
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增量搜索是一种利用先前的搜索信息提高本次搜索效率的方法,通常可以用来解决动态环境下的重规划问题.在人工智能领域,一些实时系统常常需要根据外界环境的变化不断修正自身,这样就会产生一系列变化较小的相似问题,此时应用增量搜索将会非常有效.另外,基于BDD(binary decision diagram)的启发式搜索,结合了基于BDD的搜索和启发式搜索这两种方法的优点.它既用BDD这一紧凑的数据结构来表示系统的状态空间,又通过使用启发信息来进一步压缩搜索树的大小.在介绍基于BDD的启发式搜索和增量搜索之后,结合这两种方法给出了基于BDD的增量启发式搜索算法--BDDRPA*.大量的实验结果表明,BDDRPA*算法是非常有效的,它可以被广泛地应用到智能规划、移动机器人问题等领域中. 相似文献
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分布式人工蜂群免疫算法求解函数优化问题 总被引:1,自引:0,他引:1
为了克服人工蜂群算法由于开发能力较弱而导致收敛速度慢、搜索精度不高等缺点,结合子蜂群思想和免疫克隆选择算法,提出一种基于分布式精英进化模型的人工蜂群免疫算法。首先对外层子蜂群进行启发式快速人工蜂群操作以提高收敛速度;然后对内层精英蜂群进行免疫克隆选择操作,进一步提高了算法的收敛精度和全局搜索能力。仿真结果表明了该算法在求解函数优化问题上的有效性和优越性。 相似文献
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为了寻找人类智能在启发式搜索上的优势,帮助建立基于认知的网络搜索模型,提出了基于视觉感知和目标递归的综合策略。采用功能技术fMRI结合ACT-R认知系统仿真的方法。以简化四方趣题为范式,通过fMRI实验获取信息加工同步的脑激活模式。分析了问题求解过程提出视觉感知策略和目标递归策略混合策略假设,并建立产生式系统。ACT-R认知系统模拟启发式搜索中综合策略应用过程,仿真实验结果表明,反应时偏差0.3s,脑功能区BOLD效应拟合值0.95。研究结果表明,综合策略应用能实现信息有效选择、缩短加工路径、减轻记忆负担和加速搜索过程,是人在启发式搜索上区别于机器的优势。 相似文献
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提出了一种结合增量与启发式搜索的多目标问题处理方法,设计并实现了一个基于路径扩展方法的多目标增量启发式搜索系统.当问题搜索图中边的权重发生改变或添加删除节点时,该系统通过对搜索现场进行实时的更新,部分利用先前搜索保留的信息,从更新后的状态开始求解新的问题,从而提高了重搜索的效率.对gridworld标准测试样例进行了大量的系统测试,实验结果表明:结合增量与启发式搜索的处理方法能够有效地解决状态格局不断变化的一系列相似的多目标最短路径问题. 相似文献
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多目标不等面积设施布局问题(UA-FLP)是将一些不等面积设施放置在车间内进行布局,要求优化多个目标并满足一定的限制条件。以物料搬运成本最小和非物流关系强度最大来建立生产车间的多目标优化模型,并提出一种启发式算法进行求解。算法采用启发式布局更新策略更新构型,通过结合基于自适应步长梯度法的局部搜索机制和启发式设施变形策略来处理设施之间的干涉性约束。为了得到问题的Pareto最优解集,提出了基于Pareto优化的局部搜索和基于小生境技术的全局优化方法。通过两个典型算例对算法性能进行测试,实验结果表明,所提出的启发式算法是求解多目标UA-FLP的有效方法。 相似文献
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基于改进势场蚁群算法的机器人路径规划 总被引:1,自引:0,他引:1
提出一种全局静态环境下移动机器人路径规划的改进势场蚁群算法.该算法采用人工势场法求得的初始路径和机器人与下一个节点之间的距离综合构造启发信息,并引入启发信息递减系数,避免了传统蚁群算法由于启发信息误导所致的局部最优问题;依据零点定理, 提出初始信息素不均衡分配原则,不同的栅格位置赋予不同的初始信息素,降低蚁群搜索的盲目性,提高算法的搜索效率;设定迭代阈值,自适应调节信息素挥发系数,使得该算法具有较高的全局搜索能力,避免出现停滞现象.仿真结果验证了所提出算法的可行性和有效性. 相似文献
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Li-Ning Xing Ying-Wu Chen Peng Wang Qing-Song Zhao Jian Xiong 《Applied Soft Computing》2010,10(3):888-896
A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules. 相似文献
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《Computers & Operations Research》2005,32(2):343-358
This paper proposes a new heuristic algorithm for the optimization of a performance measure of a simulation model constrained under a discrete decision space. It is a simulated annealing-based simulation optimization method developed to improve the performance of simulated annealing for discrete variable simulation optimization. This is accomplished by basing portions of the search procedure on inferred statistical knowledge of the system instead of using a strict random search. The proposed method is an asynchronous team-type heuristic that adapts techniques from response surface methodology and simulated annealing.Testing of this method is performed on a detailed simulation model of a semi-conductor manufacturing process consisting of over 40 work-stations with a cost minimization objective. The proposed method is able to obtain superior or equivalent solutions to an established simulated annealing method during each run of the testing experiment. 相似文献
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In recent years, finite element simulation has been increasingly combined with optimization techniques and applied to optimization
of various metal-forming processes. The robustness and efficiency of process optimization are critical factors to obtain ideal
results, especially for those complicated metal-forming processes. Gradient-based optimization algorithms are subject to mathematical
restrictions of discontinuous searching space, while nongradient optimization algorithms often lead to excessive computation
time. This paper presents a novel intelligent optimization approach that integrates machine learning and optimization techniques.
An intelligent gradient-based optimization scheme and an intelligent response surface methodology are proposed, respectively.
By machine learning based on the rough set algorithm, initial total design space can be reduced to self-continuous hypercubes
as effective searching spaces. Then optimization algorithms can be implemented more effectively to find optimal design results.
An extrusion forging process and a U channel roll forming process are studied as application samples and the effectiveness
of the proposed approach is verified. 相似文献
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The multiprocessor scheduling problem is one of the classic examples of NP-hard combinatorial optimization problems. Several
polynomial time optimization algorithms have been proposed for approximating the multiprocessor scheduling problem. In this
paper, we suggest a geneticizedknowledge genetic algorithm (gkGA) as an efficient heuristic approach for solving the multiprocessor
scheduling and other combinatorial optimization problems. The basic idea behind the gkGA approach is that knowledge of the
heuristics to be used in the GA is also geneticized alongiside the genetic chromosomes. We start by providing four conversion
schemes based on heuristics for converting chromosomes into priority lists. Through experimental evaluation, we observe that
the performance of our GA based on each of these schemes is instance-dependent. However, if we simultaneously incorporate
these schemes into our GA through the gkGA approach, simulation results show that the approach is not problem-dependent, and
that the approach outperforms that of the previous GA. We also show the effectiveness of the gkGA approach compared with other
conventional schemes through experimental evaluation.
This work was presented, in part, at the Second International Symposium on Artifiical Life and Robotics, Oita, Japan, February
18–20, 1997 相似文献
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参数优化是ε-支持向量回归机研究领域的重要问题,其本质是一个优化搜索的过程。基于差异演化算法在求解优化问题上的有效性,提出了以差异演化算法寻优技巧的ε 支持向量回归机参数优化方法。将该算法应用于受噪声影响的标准函数,与采用遗传算法、蚁群算法、粒子群算法对支持向量机进行优化的仿真实验结果对比表明由DE算法所确定的ε 支持向量回归机具有较好的预测性能。 相似文献
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本文针对大型工业过程的特点,提出了一种兼备常规动态模型、模糊关系模型和知识基模
型的混合式知识表达模型,并以此为基础,开发了一类启发式优化控制策略,设计了相应的实
时优化控制系统.通过实际工业过程的验证,表明这类启发式优化控制策略是可行的和有效的. 相似文献