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1.
可靠性优化的蚁群算法   总被引:7,自引:0,他引:7  
建立了可靠性冗余优化模型,分析了各种优化方法的优缺点。采用模拟退火算法、遗传算法和蚁群算法分别解决了此问题,并通过实例,结果表明蚁群算法比较有效。  相似文献
2.
遗传算法在系统可靠性优化中的应用   总被引:7,自引:0,他引:7  
张铁柱  滕春贤  韩志刚 《控制与决策》2002,17(3):378-380,384
研究性等价、体积和重量约束条件下,多级串联系统和桥式网络系统可靠性优化问题.使用遗传算法对该问题进行求解,利用基于排名的选择方法和最优保存策略,改善了遗传算法的收敛性能。计算机仿真实验结果表明,用遗传算法求解该问题是有效的。  相似文献
3.
基于遗传算法的信息系统可靠性优化设计   总被引:1,自引:1,他引:0  
设备冗余是信息系统进行可靠性优化设计的常用策略之一,其主要问题在于冗余设备的选择和配置,以达到满足一定可靠性要求下实现成本最小化的目的。这是一类结构复杂的规划问题,很难采用传统的数值算法进行求解,遗传算法提供了有效的解决方法。首先运用信息系统Petri网模型的层次结构分析结果,给出区分结点重要度的系统可靠性度量公式。在此基础上提出优化模型,给出遗传算法求解优化问题的步骤,并通过实例证明了方法的有效性及实用性。  相似文献
4.
In most of the real world design or decision making problems involving reliability optimization, there are simultaneous optimization of multiple objectives such as the maximization of system reliability and the minimization of system cost, weight and volume. In this paper, our goal is to solve the constrained multi-objective reliability optimization problem of a system with interval valued reliability of each component by maximizing the system reliability and minimizing the system cost under several constraints. For this purpose, four different multi-objective optimization problems have been formulated with the help of interval mathematics and our newly proposed order relations of interval valued numbers. Then these optimization problems have been solved by advanced genetic algorithm and the concept of Pareto optimality. Finally, to illustrate and also to compare the results, a numerical example has been solved.  相似文献
5.
基于粒子群优化算法的系统可靠性优化   总被引:1,自引:0,他引:1  
系统可靠性优化问题是典型的NP难题,建立了可靠性冗余优化模型,采用粒子群优化算法对其进行求解。通过对其它文献中仿真实例的计算和结果对比,表明了算法对求解可靠性优化问题的可行性和有效性。  相似文献
6.
人工免疫粒子群算法在系统可靠性优化中的应用   总被引:1,自引:0,他引:1       下载免费PDF全文
为了优化舰载装备系统在其设计初期的可靠性, 根据模糊优选理论, 建立了基于正负理想方案的可靠性分配的多指标模糊优化模型. 针对基本粒子群(PSO)算法易陷入早熟状态以及群体缺乏多样性等不足之处, 将人工免疫系统(AIS)原理与改进的粒子群算法有机结合, 并对粒子的飞行速度进行控制, 提出一种基于人工免疫的粒子群算法(AI-PSO). 将该算法应用于系统可靠性优化求解中, 仿真试验结果表明, 相比其他算法而言, 该算法具有较强的 全局搜索能力, 其优化结果更为合理.  相似文献
7.
Network design problem is a well-known NP-hard problem which involves the selection of a subset of possible links or a network topology in order to minimize the network cost subjected to the reliability constraint. To overcome the problem, this paper proposes a new efficiency algorithm based on the conventional ant colony optimization (ACO) to solve the communication network design when considering both economics and reliability. The proposed method is called improved ant colony optimizations (IACO) which introduces two addition techniques in order to improve the search process, i.e. neighborhood search and re-initialization process. To show its efficiency, IACO is applied to test with three different topology network systems and its results are compared with those obtained results from the conventional approaches, i.e. genetic algorithm (GA), tabu search algorithm (TSA) and ACO. Simulation results, obtained these test problems with various constraints, shown that the proposed approach is superior to the conventional algorithms both solution quality and computational time.  相似文献
8.
An optimal redundancy problem is considered as a stochastic optimization problem. The mean lifetime of a network is maximized by the stochastic branch and bound algorithm. To obtain (stochastic) estimates of branches, use is made of stochastic tangent minorants and majorants of the objective functional, interchange relaxation (permutation of maximization and expectation operations), and multiple solution of auxiliary dynamic programming problems. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 129–141, May–June 2008.  相似文献
9.
A genetic algorithm (GA) is used to solve the redundancy allocation problem when the objective is to maximize a lower percentile of the system time-to-failure distribution and the available components have random Weibull scale parameters. The GA searches the prospective solution space using an adaptive penalty to consider both feasible and infeasible solutions until converging to a feasible recommended system design. The objective function is intractable and a bi-section search is required as a function evaluator. Previously, this problem has most often been formulated to maximize system reliability instead of a lower-bound on system time-to-failure. Most system designers and users are risk-averse, and maximization of a lower percentile of the system time-to-failure distribution is a more conservative strategy (i.e. less risky) compared to maximization of the mean or median of the time-to-failure distribution. The only previous research to consider a lower percentile of system time-to-failure, also required that all component Weibull parameters are known. Those findings have been extended to address problems where the Weibull shape parameter is known, or can be accurately estimated, but the scale parameter is a random variable. Results from over 90 examples indicate that the preferred system design is sensitive to the user's perceived risk.  相似文献
10.
将实验设计、响应表面法和蒙特卡罗模拟技术相结合,提出了基于产品质量工程的6σ概率优化设计方法,实现了对汽车构件耐撞性的优化设计,提高了设计变量的可靠性.数字算例表明,该方法具有较高的精度和较强的工程实用性.  相似文献
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