共查询到20条相似文献,搜索用时 62 毫秒
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基于数据仓库的多目标优化遗传算法为解决多目标优化问题提供了有效的途径。其基本思想是:为求Pareto最优解的多目标优化遗传算法建立一个数据仓库,将进化过程中所产生的每一代Pareto最优解放入数据仓库中,在每一代先对数据仓库中的所有个体进行求Pareto最优解运算,淘汰掉劣解,再进行个体间的欧氏距离运算,将小于指定值的其中一个个体作为劣解处理。大量的计算机仿真计算表明,这种算法不仅能够有效地避免交叉或变异操作对Pareto最优解产生的破坏。而且进化速度极快,算法稳定,一般只需20-40代的运算.即可得到分布广泛的Pareto最优解。 相似文献
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在多目标优化遗传算法中,将整个种群按目标函数值划分成若干子种群,在各子种群内μ个父代经遗传操作产生λ个后代;然后将各子种群的所有父代和后代个体收集起来进行种群排序适应度共享,选取较好的个体组成下一代种群。相邻的非劣解容易分在同一子种群有利于提高搜索效率;各子种群间的遗传操作可采用并行处理;各子种群的所有
有个体收集起来进行适应度共享有利于维持种群的多样性。最后给出了计算实例。 相似文献
有个体收集起来进行适应度共享有利于维持种群的多样性。最后给出了计算实例。 相似文献
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基于遗传算法的多目标0-1背包问题优化模型 总被引:1,自引:1,他引:1
多目标0-1背包问题是一个NP-complete的多目标优化问题,基于群体搜索机制的遗传算法非常适合多目标优化问题的求解。在著名的多目标优化遗传算法NSGA-II中,引入邻域搜索机制,并将其应用于多目标0-1背包问题的求解。数值实验表明,引入邻域搜索机制的NSGA-II算法在求解多目标0-1背包问题时表现出更好的性能。 相似文献
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基于最佳进化方向的多目标遗传算法 总被引:4,自引:0,他引:4
该文模拟自然界中生物总是向着有利于自己的方向进化,即朝生物利益最大化的方向进化这一现象,给出了一种新的设计适应度函数的方法,并且结合多目标优化的Pareto最优解的概念,提出了求解多目标优化问题的一种新的算法———基于最佳基因的多目标遗传算法。数值实验表明,该算法不仅操作简单、鲁棒性强、速度快、且能够获得数量多而且广泛的Pareto最优解。 相似文献
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针对排序选择法中广泛采用的线性选择方法的缺陷,提出了一种非线性选择方法。这种选择方法既充分体现了非劣解集对劣解集的优先选择权,又考虑到了非劣解集和劣解集中个体的平等性。理论分析和仿真计算表明,这种新的排序选择法不仅能得到分布广泛的Pareto最优解,而且进化速度极快,一般只需30-50代。 相似文献
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针对无线多用户正交频分复用(OFDM)系统中功率分配问题,提出一种基于效用函数最大化框架的资源分配算法.在实际网络环境中,此类最优化算法为非凸的,利用经典最优化方法很难解决.为此,将智能优化中的粒子群方法应用到非凸优化算法设计中,并针对粒子群优化容易陷入局部极值点的问题,将Logistic混沌搜索嵌入PSO算法中,提出混沌粒子群算法.与同类算法相比,所提出算法不仅有效解决了非凸性问题,而且可以使系统具有更好的性能. 相似文献
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在多智能体系统中, 分布式资源分配问题是近年来研究热点之一. 分布式资源分配问题旨在通过智能体间信息交互实现资源最优配置. 其中智能体局部约束给算法设计带来巨大挑战. 首先, 针对一阶多智能体系统, 提出基于自适应精确罚函数的分布式资源分配算法, 其中各智能体利用距离函数实现局部约束求解. 此外, 自适应设计思想旨在避免算法对全局先验知识获取. 其次, 利用跟踪技术实现二阶多智能体系统算法设计. 并利用凸函数和非光滑分析法给出严谨的收敛性分析. 最后, 仿真结果验证了本文所设计优化算法对强凸分布式资源分配问题的有效性. 相似文献
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Wim H. Hesselink 《Acta Informatica》2013,50(5-6):297-329
Resource allocation is the problem that a process may enter a critical section CS of its code only when its resource requirements are not in conflict with those of other processes in their critical sections. For each execution of CS, these requirements are given anew. In the resource requirements, levels can be distinguished, such as e.g. read access or write access. We allow unboundedly many processes that communicate by reliable asynchronous messages and have finite memory. A simple starvation-free solution is presented. Processes only wait for one another when they have conflicting resource requirements. The correctness of the solution is argued with invariants and temporal logic. It has been verified with the proof assistant PVS. 相似文献
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Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments 总被引:4,自引:0,他引:4
In a distributed manufacturing environment, factories possessing various machines and tools at different geographical locations are often combined to achieve the highest production efficiency. When jobs requiring several operations are received, feasible process plans are produced by those factories available. These process plans may vary due to different resource constraints. Therefore, obtaining an optimal or near-optimal process plan becomes important. This paper presents a genetic algorithm (GA), which, according to prescribed criteria such as minimizing processing time, could swiftly search for the optimal process plan for a single manufacturing system as well as distributed manufacturing systems. By applying the GA, the computer-aided process planning (CAPP) system can generate optimal or near-optimal process plans based on the criterion chosen. Case studies are included to demonstrate the feasibility and robustness of the approach. The main contribution of this work lies with the application of GA to CAPP in both a single and distributed manufacturing system. It is shown from the case study that the approach is comparative or better than the conventional single-factory CAPP. 相似文献
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In this article, we present a distributed resource and power allocation scheme for muRip]e-resource wireless cellular networks. The global optimization of multi-cell multi-link resource allocation problem is known to be NP-hard in the general case. We use Gibbs sampling based algorithms to perform a distributed optimization that would lead to the global optimum of the problem. The objective of this article is to show how to use the Gibbs sampling (GS) algorithm and its variant the Metropolis-Hastings (MH) algorithm. We also propose an enhanced method of the MH algorithm, based on a priori known target state distribution, which improves the convergence speed without increasing the complexity. Also, we study different temperature cooling strategies and investigate their impact on the network optimization and convergence speed. Simulation results have also shown the effectiveness of the proposed methods. 相似文献
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Tushar Goel Nielen Stander Yih-Yih Lin 《Structural and Multidisciplinary Optimization》2010,41(3):421-432
This study pertains to practical use of the GA for industrial applications where only a limited number of simulations can
be afforded. Specifically, an attempt is made to find an efficient allocation of the total simulation budget (population size
and number of generations) for constrained multi-objective optimization. A study is conducted to seek improvements while restricting
the number of simulations to 1,000. Parallelization is exploited using concurrent simulations for each GA generation on a
HP quad-core cluster, and resulted in a significant time savings. Furthermore, the efficient distribution of computational
effort to achieve the greatest improvement in performance was explored. Two analytical examples as well as an automotive crashworthiness
simulation of a finite element model with 58,000 elements were used as test examples. Various population sizes and numbers
of generations were tried while limiting the total number of simulations to 1,000. The optimization performance was compared
with Monte-Carlo and space filling sampling methods. It was observed that using the GA, many feasible and trade-off solutions
could be found. It is shown that allowing a large number of generations is beneficial to get good trade-off solutions. For
the vehicle design, significant improvements in the performance were observed. This example also suggests that, for problems
with a small feasible region, the number of feasible solutions can be significantly increased in the first few generations
involving about 200 simulations. 相似文献
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一种基于混沌搜索的自适应入侵遗传算法 总被引:2,自引:0,他引:2
将生物系统中“入侵”的概念引入遗传算法,提出了一种基于混沌搜索的自适应入侵遗传算法。该算法动态地引入入侵种群,并采用混沌搜索产生入侵个体。入侵种群的扩散使优良基因得以在个体间传播,优化了种群的基因构成,能够促使种群跳出局部最小,并向全局最优的方向进化,从而有效地避免了遗传算法的早熟现象。将该算法用于函数优化及解决模式分类问题的神经网络参数训练,实验结果表明,该算法具有较快的收敛速度和较强的寻优能力。 相似文献
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研究一类带有不等式约束为凸函数的多智能体系统分布式资源分配问题.在资源分配问题中,各智能体拥有仅自身可知的局部成本函数和局部凸不等式约束.分布式资源分配旨在如何利用智能体间的信息交互设计一种分布式优化算法,完成定量资源分配的同时还保证最小化全局成本函数.针对该问题,基于卡罗需-库恩-塔克条件和比例积分控制思想,首先提出一种自适应分布式优化算法,其中凸不等式约束的对偶变量可实现自适应获取;然后,为了降低系统的通信资源消耗,设计一种动态事件触发控制策略以实现离散时间通信的分布式资源分配算法;最后,通过数值仿真验证所设计算法的有效性. 相似文献
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基于改进遗传算法的多天线地面站硬件资源分配方法 总被引:1,自引:0,他引:1
多天线卫星地面站硬件设备资源分配问题是一个基于约束满足的复杂资源组合优化问题。在考虑任务执行时间、地面站可见时间窗口、地面站设备接收能力和设备链路约束的情况下,对多天线地面站硬件资源分配问题建立了高可用模型。以加权任务执行总时间为目标,以经典遗传算法为基础,根据问题特点改进了相关遗传算子,在进行遗传变异的过程中,通过深度优先搜索算法确定单个染色体对应的最佳资源分配方案,同时利用启发式信息优化搜索过程。最后通过高可用算例仿真表明,所建模型和算法是合理有效的。 相似文献