共查询到20条相似文献，搜索用时 203 毫秒
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针对车间调度问题计算复杂度较高的特点,将协同进化多目标优化算法应用于车间调度问题。计算实例结果表明,协同进化多目标优化算法应用于车间调度问题不仅可以优化效果,而且能够在一定程度上提高计算效率。 相似文献
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提出了一种新的基于差分进化和粗糙集理论的多目标寻优算法。应用差分进化作为的搜索引擎,尝试将它在单一目标优化中展现出的良好收敛作用转换到多目标优化问题中。在搜索的第二阶段中,为了提高迄今为止已有的非支配解决方案的普遍性,应用到了粗糙集理论。对于专用文献中通常采纳应用标准的测试函数和尺度的检验,本文的混合方法是有效的。 相似文献
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为了改善协同进化多目标优化算法性能,引入了聚集密度对超级个体集合进行更新。其基本思想是：首先计算种群中各个体的聚集密度,再定义一个偏序集,然后根据一定的比例依次从偏序集中选择个体更新。根据数值试验和量化指标测试了新算法的收敛性与分布性。结果表明,新算法在收敛性方面与常规协同进化多目标算法相当,但其分布性获得了一定程度的改善。 相似文献
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建立了动态车辆路径优化问题的数学模型,提出了一种基于聚集密度的人工免疫多目标进化算法。该算法首先计算群体中每个个体的聚集密度,再根据目标函数值和聚集密度定义一个偏序集,然后采用比例选择原则依次从偏序集中选择个体,更新精英集。实验结果表明,该算法是解决动态车辆路径问题的有效方法。 相似文献
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提出了基于量子进化算法的人体跟踪方法。量子进化算法借鉴了量子计算的思想,具有较强的寻优能力和较快的运算速度,在基于量子进化算法的人体跟踪中,将跟踪置于函数优化框架内,视跟踪为在模型可行域内求解与图像观测特征具有最优匹配的模型的函数优化问题,并对此目标函数使用量子进化算法寻优。模拟场景实验表明,与基于粒子滤波的人体跟踪算法相比,基于量子进化算法的人体跟踪具有较高的跟踪精度和较快的运算速度。 相似文献
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建立了供水调度模型,利用基于分解的多目标进化算法,首先将供水调度问题分解为若干单目标,然后根据分布估计的思想对各个单目标建立概率模型,通过采样产生新的个体。利用非支配排序法进行选择,得到最优解。实验表明,该算法对求解供水调度优化问题具有较好的多样性和均匀性,并且降低了算法的计算复杂度。 相似文献
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多目标进化算法在许多领域有广泛的应用,大部分文献都只针对二维与三维的测试问题,而应用到高维的情况比较少。随着人们开始越来越多地关注高维目标优化问题,目标减少成为研究的热点。从决策者角度考虑冗余目标问题,提出了基于最小二乘法的目标减小算法。该方法将每个目标函数分段拟合为多条直线,然后两两比较各斜率向量确定最冗余目标对,进而确定冗余目标。 相似文献
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PID控制参数优化是工业自动控制领域的研究热点,传统的PID控制通过人工试凑法获取控制系数,控制实时性和精度较低。为了解决这些问题,本文提出了进化计算的PID控制参数快速整定算法,通过对PID控制参数的粒子化映射,将PID控制参数优化转化为动态系统的全局寻优问题,利用粒子的进化迭代计算能力,搜索符合目标函数的控制参数。实验仿真利用温度控系统进行算法验证,仿真证明该算法具有很强的鲁棒性,可以快速达到系统整定。 相似文献
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多目标优化问题是一类很普遍的问题。演化算法是一种通过模拟自然界的生物演化过程搜索最优解的方法,用于求解多目标优化问题有其独特的优势。系统介绍了多目标演化算法特点、需要解决的关键问题、算法框架、算法实现及应用趋势。 相似文献
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In recent years, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Ease of use, broad applicability, and global perspective may be considered as the primary reason for their success. The honey-bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is presented and tested with a nonlinear, continuous constrained problem with continuous decision and state variables to demonstrate the efficiency of the algorithm in handling the single reservoir operation optimization problems. It is shown that the performance of the model is quite comparable with the results of the well-developed traditional linear programming (LP) solvers such as LINGO 8.0. Results obtained are quite promising and compare well with the final results of the other approach. 相似文献
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《Information processing & management》2020,57(5):102255
Collaborative frequent itemset mining involves analyzing the data shared from multiple business entities to find interesting patterns from it. However, this comes at the cost of high privacy risk. Because some of these patterns may contain business-sensitive information and hence are denoted as sensitive patterns. The revelation of such patterns can disclose confidential information. Privacy-preserving data mining (PPDM) includes various sensitive pattern hiding (SPH) techniques, which ensures that sensitive patterns do not get revealed when data mining models are applied on shared datasets. In the process of hiding sensitive patterns, some of the non-sensitive patterns also become infrequent. SPH techniques thus affect the results of data mining models. Maintaining a balance between data privacy and data utility is an NP-hard problem because it requires the selection of sensitive items for deletion and also the selection of transactions containing these items such that side effects of deletion are minimal. There are various algorithms proposed by researchers that use evolutionary approaches such as genetic algorithm(GA), particle swarm optimization (PSO) and ant colony optimization (ACO). These evolutionary SPH algorithms mask sensitive patterns through the deletion of sensitive transactions. Failure in the sensitive patterns masking and loss of data have been the biggest challenges for such algorithms. The performance of evolutionary algorithms further gets degraded when applied on dense datasets. In this research paper, victim item deletion based PSO inspired evolutionary algorithm named VIDPSO is proposed to sanitize the dense datasets. In the proposed algorithm, each particle of the population consists of n number of sub-particles derived from pre-calculated victim items. The proposed algorithm has a high exploration capability to search the solution space for selecting optimal transactions. Experiments conducted on real and synthetic dense datasets depict that VIDPSO algorithm performs better vis-a-vis GA, PSO and ACO based SPH algorithms in terms of hiding failure with minimal loss of data. 相似文献
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害虫防治风险型决策的一类方法 总被引：3，自引：0，他引：3
Ｂａｙｅｓ期望损失决策是应用广泛的害虫型决策技术，与Ｂａｔｙｅｓ决策进行比较分析后认为，多目标决策算法，如ＴＯＰＳＩＳ法和ＥＬＥＣＴＲＥ法等，可作为害虫防治风险型决策的一类新方法，从而可以丰富风险型决策的理论内容。 相似文献
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以小麦产量,天敌量及药剂相对稀释量为目标,对麦二叉蚜,天敌及农药系统进行多目标优化分析.结果表明,渭北旱塬于灌浆期防治麦二叉蚜的最优目标值是小麦增产率为7.59％,天敌累积量为113.3386头日,药剂相对稀释量为124.4117.给出了若干药剂防治麦二叉蚜的最佳浓度.移动理想点法可作为害虫综合治理的决策优化工具。 相似文献
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《Information processing & management》2022,59(5):103005
In real-life applications, resources in construction projects are always limited. It is of great practical importance to shorten the project duration by using intelligent models (i.e., evolutionary computations such as genetic algorithm (GA) and particle swarm optimization (PSO) to make the construction process reasonable considering the limited resources. However, in the general EC-based model, for example, PSO easily falls into a local optimum when solving the problem of limited resources and the shortest period in scheduling a large network. This paper proposes two PSO-based models, which are resource-constrained adaptive particle swarm optimization (RC-APSO) and an input-adaptive particle swarm optimization (iRC-APSO) to respectively solve the static and dynamic situations of resource-constraint problems. The RC-APSO uses adaptive heuristic particle swarm optimization (AHPSO) to solve the limited resource and shortest duration problem based on the analysis of the constraints of process resources, time limits, and logic. The iRC-APSO method is a combination of AHPSO and network scheduling and is used to solve the proposed dynamic resource minimum duration problem model. From the experimental results, the probability of obtaining the shortest duration of the RC-APSO is higher than that of the genetic PSO and GA models, and the accuracy and stability of the algorithm are significantly improved compared with the other two algorithms, providing a new method for solving the resource-constrained shortest duration problem. In addition, the computational results show that iRC-APSO can obtain the shortest time constraint and the design scheme after each delay, which is more valuable than the static problem for practical project planning. 相似文献
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介绍了一种求解复杂组合优化问题的新型的模拟进化算法——蚁群算法。阐述了该算法的基本原理、模型以及实现过程,并且介绍了蚁群算法在TSP问题、二次分配问题、车间作业调度问题、大规模集成电路综合布线以及车辆路径问题等组合优化问题中的应用思路。 相似文献
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Pin-Lin Liu 《Journal of The Franklin Institute》2009,346(10):958-968
This paper investigates the exponential stability problem for uncertain time-varying delay systems. Based on the Lyapunov-Krasovskii functional method, delay-dependent stability criteria have been derived in terms of a matrix inequality (LMI) which can be easily solved using efficient convex optimization algorithms. These results are shown to be less conservative than those reported in the literature. Four numerical examples are proposed to illustrate the effectiveness of our results. 相似文献
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This article presents a novel tuning design of Proportional-Integral-Derivative (PID) controller in the Automatic Voltage Regulator (AVR) system by using Cuckoo Search (CS) algorithm with a new time domain performance criterion. This performance criterion was chosen to minimize the maximum overshoot, rise time, settling time and steady state error of the terminal voltage. In order to compare CS with other evolutionary algorithms, the proposed objective function was used in Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for PID design of the AVR system. The performance of the proposed CS based PID controller was compared to the PID controllers tuned by the different evolutionary algorithms using various objective functions proposed in the literature. Dynamic response and a frequency response of the proposed CS based PID controller were examined in detail. Moreover, the disturbance rejection and robustness performance of the tuned controller against parametric uncertainties were obtained, separately. Energy consumptions of the proposed PID controller and the PID controllers tuned by the PSO and ABC algorithms were analyzed thoroughly. Extensive simulation results demonstrate that the CS based PID controller has better control performance in comparison with other PID controllers tuned by the PSO and ABC algorithms. Furthermore, the proposed objective function remarkably improves the PID tuning optimization technique. 相似文献