A tabu search approach to the optimal sequential partitions of directed acyclic graphs

Tabu search is a novel technique for solving combinatorial optimization problems. The process in which the tabu search method seeks to transcend local optimality is based on an evaluation function which chooses the highest-evaluation move in terms of objective function and tabu restrictions. This paper presents a tabu search algorithm for finding a minimum-cost partition of the nodes of a directed acyclic graph into subsets of a given size, subject to the constraint that the precedence relations are satisfied. A standard tabu search approach cannot realize good solutions for this problem, because the problem is a complex multiple partitioning problem in which the number of subsets and the number of nodes in each subset are unsettled. For this problem, we use an appropriate data structure for this method and develop effective neighborhood structure and heuristics. We also assess the effectiveness of the developed algorithm. The results show that this algorithm is effective in obtaining a near-optimal solution to this problem. The running time of the procedure is proportional to the number of nodes in the graph. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 119(4): 42–51, 1997     

基于最优流法和遗传算法的配电网重构

提出了一种基于改进最优流和遗传算法的配电网重构算法.该算法先利用配电网的同胚图将重构问题的全局寻优空间划分为若干子空间,然后利用改进最优流法寻找子空间内的最优解,之后再利用遗传算法搜索全局最优解所在的子空间,从而实现在局部最优解中寻找全局最优解.该算法既通过压缩寻优空间提高了遗传算法的搜索效率,又利用改进最优流法改善了局部寻优能力.算例计算结果表明了文中所提算法的可行性和有效性.     

电力系统相量测量装置最优配置混合算法的研究

以电力系统状态完全可观测和相量测量装置(PMU)配置数目最小为目标,形成了PMU最优配置问题。将遗传算法和禁忌算法有效结合形成禁忌遗传算法,该算法在改进交叉和变异算子的基础上,继承和发展了遗传算法基于多点搜索、鲁棒性强等诸多优点,每当群体有出现早熟而陷入局部最优解的趋势时,利用禁忌搜索增强算法的爬山能力,避免算法早熟而陷入局部最优解,增强算法的全局收敛能力和收敛速度。与遗传算法和禁忌搜索方法相比,禁忌遗传算法具有更好的全局收敛能力和收敛速度。最后采用IEEE14,IEEE30和IEEE57节点系统对算法的有效性进行了验证。     

面向边缘计算的人工鱼群搜索任务调度

如何将计算任务分配到合适的边缘计算资源上进行计算,以满足边缘计算环境下用户的计算需求、提高用户任务请求 的服务质量,是边缘计算中面临的关键问题。 本文提出一种基于人工鱼群搜索的边缘计算任务调度方法(AFETSA)。 将人工 鱼群搜索算法和边缘计算任务调度模型相结合,采用非线性递减函数动态地调整人工鱼的视野范围和步长,以提高启发式任务 调度算法的全局搜索能力,降低任务的计算时延;同时与禁忌搜索算法进行融合,通过引入忌禁表,在每一次迭代中防止算法陷 入局部最优,提高算法的寻优能力。 CloudSim3. 0 仿真平台实验评测结表明,本文所提 AFETSA 方法和已有的 AFSA、ACO 和 PSO 这 3 种调度算法相比,在任务执行时间、算法稳定性、负载均衡方面都有明显的提升,可充分利用边缘服务器计算资源,提 升计算任务的计算性能,有效解决边缘计算中任务调度不均导致的时延过高和负载不均衡问题。     

Multiple tabu search algorithm for economic dispatch problem considering valve-point effects

This paper presents a multiple tabu search (MTS) algorithm to solve the economic dispatch (ED) problem by taking valve-point effects into consideration. The practical ED problem with valve-point effects is represented as a non-smooth optimization problem with equality and inequality constraints that make the problem of finding the global or near global optimum difficult. The proposed MTS algorithm is the sequential execution of individual tabu search (TS) algorithm simultaneously by only one personal microcomputer. The MTS algorithm introduces additional techniques for improvement of search process, such as initialization, adaptive searches, multiple searches, replacing and restarting process. To show its effectiveness, the MTS is applied to test two studied systems consisting of 13 and 40 power generating units with valve-point effects. The optimized results by MTS are compared with those of conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), TS algorithm and particle swarm optimization (PSO). Studied results confirm that the proposed MTS approach is capable of obtaining higher quality solution efficiently and lowest computational time.     

禁忌搜索–粒子群算法在无功优化中的应用

禁忌搜索粒子群算法是针对粒子群算法局部搜索能力较弱和存在早熟收敛的问题,将禁忌搜索思想融入到粒子群算法中的混合算法,并将该算法应用到电力系统无功优化中。该方法在粒子群算法寻优过程的后期加入了禁忌表,扩大搜索空间,避免陷入局部最优。通过对IEEE 30节点测试系统和鸡西电网进行仿真计算,并与其他算法进行比较,结果表明该算法能取得更好的全局最优解,既加快了收敛速度,又提高了收敛精度。     

基于改进蜂群算法的工业机器人路径规划研究

针对工业机器人在复杂环境中运动的避障及路径优化问题,提出基于改进人工蜂群算法的工业机器人避障路径规划策略。首先针对传统人工蜂群算法搜索能力不足且容易陷入局部最优的问题,将禁忌搜索思想引入到人工蜂群算法最优解搜索过程中,形成了基于禁忌搜索的改进型人工蜂群算法,然后将其应用到工业机器人的路径规划问题中,并进行了仿真实验。结果表明,改进后的方法能够得到最优的路径,且寻优速度快、过程稳定。该方法可用于解决工业机器人路径规划问题。     

基于改进禁忌算法的区域控制偏差分区优化方法

为合理确定区域控制偏差分区的大小、提高自动发电控制系统的性能,根据区域电网典型负荷曲线和受电曲线,以区域电网控制性能指标最大化为目标函数,提出了基于改进禁忌算法的区域控制偏差分区优化方法。该方法采用记忆指导搜索策略重点搜索了各记忆段的局部最优值,避免了全局寻优的盲目性,利用动态管理的禁忌频率信息避免了大范围的迂回搜索,提高了新区域的搜索效率。算例分析和实际应用结果验证了该区域控制偏差分区方法的合理性和有效性。     

Assignment problem for system with precedence relationships among elements

We have studied the problem of assign elements to an ordered sequence of stations such that the precedence relations are satisfied. At this time, we can obtain the best evaluation value decided under certain constrained conditions to the assignment. This kind of problem includes the line balancing problem. In addition, this can be adjusted to various problems by changing the constrained condition and objective function. These problems can be shown as sequential partitions of the nodes of a directed acyclic graph into subsets. We especially consider the problem of finding a minimum total cost of the cut edge under the restriction of the size of block. In this paper, we propose the general framework for sequential partitions of directed acyclic graphs. We also describe an efficient algorithm that can be used to reduce computational requirements and, possibly, storage. We estimate that the complexity of the algorithm is the polynomial order, if structure of directed acyclic graphs is near parallel. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 121(2): 54–62, 1997     

An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems

This paper presents an improved tabu search (ITS) algorithm for loss-minimization reconfiguration in large-scale distribution systems. TS algorithm is an efficient meta-heuristic searching algorithm. It has advantages of both high local search efficiency of hill-climbing method and global search ability of intelligent algorithm. But tabu lengths and candidate neighborhood are two key parameters affecting searching performance of TS algorithm, and these two parameters are hard to be effectively determined in advance. In ITS algorithm, mutation operation, a main operator used in genetic algorithm, is introduced to weaken the dependence of global search ability on tabu length. In addition, the candidate neighborhood, which only contains several optimal switch exchanges in each tie switch associated loop network, is designed to improve local search efficiency and to save a large amount of computing time. The proposed ITS algorithm is applied to the sample system and numerical results well demonstrate the validity and effectiveness of the proposed ITS algorithm.     

配电网中辐射线路的最优分段处理

配电网中辐射线路的最优分段处理是一个需要同时考虑经济性和可靠性的非线性组合优化问题.采用结合了遗传算法的全局搜索特性和禁忌搜索算法的局部搜索特性的混合算法来求解该问题,所采用模型的目标函数同时考虑了分段开关的年投资与维护费用以及将可靠性效益转化为经济性指标的用户缺电成本.最后采用一个简单算例验证了文中方法的可行性,并在实际系统中得到了应用.     

融合禁忌搜索的SSA算法及其路径规划的应用

针对麻雀搜索算法易陷入局部最优解、收敛精度不足等问题,提出一种融合禁忌算法思想的改进麻雀搜索算法。首先,采用拉丁超立方抽样初始化种群,保证初始空间解分布更均匀;其次,对R2相似文献   

Frequency memory based gradient descent bit flipping algorithm

An escape method for the local optima of the gradient descent bit‐flipping (GDBF) algorithm for decoding low‐density parity‐check codes is proposed. In the GDBF algorithm, a search point is likely to be trapped in a local optimum, which causes performance degradation of the algorithm. To address this issue, we introduce a list to store the frequency of visits to each local optimum and devise a strategy to increase the distance of a search point from a local optimum with respect to the frequency. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A new genetic-based tabu search algorithm for unit commitment problem

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. The proposed algorithm, which is mainly based on genetic algorithms incorporates tabu search method to generate new population members in the reproduction phase of the genetic algorithm. In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the algorithm, a simple short term memory procedure is used to counter the danger of entrapment at a local optimum by preventing cycling of solutions, and the premature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algorithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods in the literature.     

An improved tabu search for economic dispatch with multiple minima

This paper develops an improved tabu search algorithm (ITS) for economic dispatch (ED) with noncontinuous and nonsmooth cost functions. ITS employs a flexible memory system to avoid the entrapment in a local minimum and developed the ideal of "distance" to the fitness to accelerate optimization. The new approach extends simple tabu search algorithm (STS) to real valued optimization problem and applies parallelism to weaken the dependence of the convergence rate of modified tabu search algorithm (NITS) on the initial condition. Effectiveness of the method was compared with many conventional methods. Results show that the proposed algorithm can provide accurate solutions with reasonable performance and has a great potential for other applications in the power system     

Self-adaptive evolutionary programming and its application to multi-objective optimal operation of power systems

This paper proposes a new algorithm to solve multi-objective optimal operation of power systems problem. The algorithm is based on combination of general evolutionary programming and random search technique. The algorithm includes two important procedures. First, a new pattern of mutation is developed in this paper. Secondly, the developed mutation operator is self-adaptive during optimization. Furthermore, in a multi-objective optimal operation study four objectives (cost of generation with valve point loading, transmission losses, environmental pollution and steady-state security regions) are considered for optimization, and an ideal point method is used to solve the problem. The proposed algorithm is tested on the IEEE six-bus and 30-bus systems. Numerical results and comparison demonstrate that the new method not only can deal agilely with constraints, but also can reduce the CPU time and prevent the search from being in local optima.     

基于TB-PC算法的MISO系统辨识的研究

如何保持MISO系统辨识的精确度、收敛度、耗时以及跳出局部最优解,是当今研究的热点和难点。提出了一种基于协同进化策略和禁忌搜索的蝙蝠和粒子群混合算法(TB-PC),分析蝙蝠算法控制参数,提出了合理的脉冲频度和音强初值,将蝙蝠算法与粒子群算法的优势用协同进化结合起来,并引入了禁忌搜索。通过对四个测试函数和MISO系统实例的辨识仿真,验证了TB-PC算法具有稳定性能好、收敛精度高等优点,对MISO系统有优良的辨识效果。     

基于Spark的并行模拟退火算法求解TSP

模拟退火算法是求解无约束优化问题的有效方法,但求解旅行商问题时存在精度较差、容易陷入局部最优且收敛速度慢等缺点。为了改进上述问题,本文提出了一种基于Spark平台的并行模拟退火算法。修改模拟退火算法的降温函数,构造旅行商问题的解空间,采用大邻域搜索技术和2-opt算子增强局部搜索能力,引入OX交叉思想增强全局搜索能力,提出交叉协同试验并行策略与Spark平台并行实现。选取若干TSPLIB数据集进行仿真实验,对求解质量和运行时间两个方面进行测试,与其它Spark框架的并行算法进行对比实验。仿真结果表明,该算法求解精度有较大的提高,求解速度上对比其他算法提升3-10倍,能够有效求解旅行商问题。     

基于改进免疫遗传算法的无功优化

针对目前电力系统无功优化算法所存在的问题提出了一种改进的免疫遗传算法,该算法把模糊逻辑、模拟退火和免疫算法相结合,根据模糊逻辑获得变化的交叉和变异算子,采用退火免疫方法对抗体进行选择,用免疫算子进行个体更新,从而增加了群的多样性,避免陷入局部最优。同时,还采用十进制整数编码和保存最优个体法来提高计算速度和精度。最后以IEEE 30-bus系统为例对算法的性能和求解精度进行了测试,结果表明本文提出的算法比其他遗传算法在计算速度和全局收敛方面有了很大提高。     

基于混合搜索算法的配电网最优无功补偿

针对目前无功优化算法所存在的问题,提出一种综合三种智能优化方法优点于一身的混合搜索算法。该方法在自适应遗传算法基础上,加入二次邻域变异过程,加快遗传进化速度,引导个体向符合无功优化问题的实际方向发展。然后运用模拟退火进行个体更新,以便增加群的多样性;最后将所得最优解作为禁忌搜索的初始解,进行局部寻优求解过程。以IEEE28-bus系统和一实际配电网系统为例进行优化计算,结果表明混合搜索算法具有较优的性能和求解精度。