An Evolutionary Parallel Tabu Search approach for distribution systems reinforcement planning

In this paper a new meta-heuristic optimisation technique is proposed. The method is based on the Parallel Tabu Search (PTS) algorithm and the application is the optimal electrical distribution systems reinforcement planning through the installation of photovoltaic plants, parallel cables, capacitor banks and transformers. The issue is a combinatorial optimisation problem; the objective function is a non-linear expression of a large number of variables. In these cases, meta-heuristics have proved to work well and one of the most efficient is the Tabu Search algorithm. For large-scale problems, parallelisation improves Tabu Search computational efficiency as well as its exploration ability. In this paper, an enhanced version of PTS, Evolutionary Parallel Tabu Search (EPTS), is proposed. It performs reproduction operators on sub-neighbourhoods directing the search towards more promising areas of the search space. The problem of distribution systems reinforcement planning has been studied in detail and the results of the application show that the EPTS outperforms the PTS and Particle Swarm Optimisation algorithms.The algorithm's performance is also tested on mathematical test functions and other properties of the proposed algorithm are examined.     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.     

Real-time optimization of a hydroelectric power plant

An optimal control problem modelling a hydroelectric power plant was developed and discussed by Hj. Wacker and his co-workers in [1]. In the present paper, this problem is treated within a more general framework of “non-differentiable” optimal control problems. Necessary conditions of optimality are derived and it is proven that the restricted class of controls considered in [1] indeed contains the optimal control. Furthermore, a decoupling technique is established that allows the full problem to be split into several small subproblems. Based on the new results, an efficient algorithm is developed. This algorithm allows the optimal control to be computed for more general problems with greater accuracy and for a longer time period. Numerical results are given both for the model described in [1] and for the more refined model presented in this paper.     

并行机间歇过程生产调度的遗传局部搜索算法

研究了一类集成分批的并行机间歇过程调度问题(parallel machine batch process scheduling problem,简称PBPSP),将此问题转化为固定费用运输问题(6xed charge transportation problem,简称FCTP)后,提出了具有集中邻域搜索机制和局部最优逃逸机制的遗传局部搜索算法(genetic local search algorithm,简称GLSA).GLSA算法用先根遍历边排列模式编码生成树解,具有高效的子树补充式单点交叉操作.将基于网络单纯型方法的邻域搜索作为变异算子,并提出了连续随机节点邻域搜索的集中邻域搜索策略以及随机旋转变异与全局邻域搜索相结合的局部最优逃逸策略,极大地强化了遗传局部搜索算法的全局寻优能力.实验表明:GLSA算法获得的解质量优于基于排列编码的遗传算法和基于矩阵编码的遗传算法,得到了所有Benchmark问题的最优解,且具有高鲁棒性.针对一定规模的FCTP问题,GLSA算法比Tabu启发式搜索算法具有更高的获得最优解几率.     

求解带容量和时间窗约束车辆路径问题的改进蝙蝠算法

带时间窗和容量约束的车辆路径问题是车辆路径问题重要的扩展之一,属于NP难题,精确算法的求解效率较低,且对于较大规模问题难以在有限时间内给出最优解.为了满足企业和客户快速有效的配送需求,使用智能优化算法可以在有限的时间内给出相对较优解.研究了求解带容量和时间窗约束车辆路径问题的改进离散蝙蝠算法,为增加扰动机制,提高搜索速度和精度,在对客户点按其所在位置进行聚类的基础上,在算法中引入了变步长搜索策略和两元素优化方法进行局部搜索.仿真实验结果表明,所设计算法具有较高寻优能力和较强的实用价值.     

An efficient search method for job-shop scheduling problems

We present an efficient search method for job-shop scheduling problems. Our technique is based on an innovative way of relaxing and subsequently reimposing the capacity constraints on some critical operations. We integrate this technique into a fast tabu search algorithm. Our computational results on benchmark problems show that this approach is very effective. Upper bounds for 11 well-known test problems are thus improved. Through the work presented We hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The peripheral conditions for such a system are ripe with the increasingly widespread adoption of enterprise information systems and plant floor tracking systems based on bar code or wireless technologies. One of the remaining obstacles, however, is the fact that scheduling problems arising from many production environments, including job-shops, are extremely difficult to solve. Motivated by recent success of local search methods in solving the job-shop scheduling problem, we propose a new diversification technique based on relaxing and subsequently reimposing the capacity constraints on some critical operations. We integrate this technique into a fast tabu search algorithm and are able to demonstrate its effectiveness through extensive computational experiments. In future research, we will consider other diversification techniques that are not restricted to critical operations.     

DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization

Incorporation of distributed generation (DG) in distribution network may reduce the network loss if DG of appropriate size is placed at proper strategic location. The current article presents determination of optimal size and location of DG in radial distribution network (RDN) for the reduction of network loss considering deterministic load demand and DG generation using symbiotic organisms search (SOS) algorithm. SOS algorithm is a meta-heuristic technique, inspired by the symbiotic relationship between different biological species. In this paper, optimal size and location of DG are obtained for two different RDNs (such as, 33-bus and 69-bus distribution networks). The obtained results, using the proposed SOS, are compared to the results offered by some other optimization algorithms like particle swarm optimization, teaching-learning based optimization, cuckoo search, artificial bee colony, gravitational search algorithm and stochastic fractal search. The comparison is done based on minimum loss of the distribution network as well as based on the convergence mobility of the fitness function offered by each of the comparative algorithms for both the networks under consideration. It is established that the proposed SOS algorithm offers better result as compared to other optimization algorithms under consideration. The results are also compared to the existing solution available in the literature.     

Solution quality of random search methods for discrete stochastic optimization

In this paper, we propose a framework for selecting a high quality global optimal solution for discrete stochastic optimization problems with a predetermined confidence level using general random search methods. This procedure is based on performing the random search algorithm several replications to get estimate of the error gap between the estimated optimal value and the actual optimal value. A confidence set that contains the optimal solution is then constructed and methods of the indifference zone approach are used to select the optimal solution with high probability. The proposed procedure is applied on a simulated annealing algorithm for solving a particular discrete stochastic optimization problem involving queuing models. The numerical results indicate that the proposed technique indeed locate a high quality optimal solution.     

改进麻雀搜索算法在分布式电源配置中的应用

针对麻雀搜索算法（Sparrow Search Algorithm,SSA）在解决高维、非线性的分布式电源（Distributed Generation,DG）优化配置问题中求解精度与稳定性不足的问题,提出一种改进麻雀搜索算法进行求解。通过引入Tent混沌提高初始解的质量,利用Levy飞行策略和柯西高斯变异,增强算法搜索方向的多元性以及跳出局部最优的能力,针对算法在工程应用中产生大量无效麻雀的问题,优化了麻雀位置更新公式,以提高SSA的工程实用性。分别用标准SSA、ISSA、蝴蝶优化算法（Butterfly Optimization Algorithm,BOA）、鲸鱼优化算法（Whale Optimization Algorithm,WOA）测试基准函数,对比验证ISSA的有效性,并将ISSA应用于IEEE33节点系统的DG化配置模型求解,所求的DG配置方案能更大程度地降低配电网有功损耗与电压偏差。     

A multi-objective fuzzy adaptive PSO algorithm for location of automatic voltage regulators in radial distribution networks

This paper proposes a multi-objective optimal location of Automatic Voltage Regulators (AVRs) in distribution systems at the presence of Distributed Generators (DGs) by a Fuzzy Adaptive Particle Swarm Optimization (FAPSO) algorithm. The proposed algorithm utilizes an external repository to save founded Pareto optimal solutions during the search process. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by trade-off) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as Genetic algorithm and PSO is extraordinary.     

Shared feature extraction for nearest neighbor face recognition.

In this paper, we propose a new supervised linear feature extraction technique for multiclass classification problems that is specially suited to the nearest neighbor classifier (NN). The problem of finding the optimal linear projection matrix is defined as a classification problem and the Adaboost algorithm is used to compute it in an iterative way. This strategy allows the introduction of a multitask learning (MTL) criterion in the method and results in a solution that makes no assumptions about the data distribution and that is specially appropriated to solve the small sample size problem. The performance of the method is illustrated by an application to the face recognition problem. The experiments show that the representation obtained following the multitask approach improves the classic feature extraction algorithms when using the NN classifier, especially when we have a few examples from each class.     

Optimal position and rating of DG in distribution networks by ABC–CS from load flow solutions illustrated by fuzzy-PSO

In this work, load flow problems of both radial distribution networks (RDNs) and mesh distribution networks (MDNs) have been solved using hybrid fuzzy-PSO algorithm. A new voltage stability index (VSI) is also indicated. Based on the suggested load flow, distributed generation (DG) is ready to conduct through the requirement; and with the support of inserting the optimal-sized DG unit in an exact way, the distribution system’s stability is also studied. The exact position of each DG unit has been computed using “loss sensitivity analysis,” whereas the optimal sizing of each DG unit has been done with the help of hybrid artificial bee colony and Cuckoo search algorithm. The suggested method is tested in the regular 33-node and 69-node RDNs as well as in 85-node and 119-node MDNs. The transcendence of the proposed operation has been centered with the aid of comparison to the other existing methods. The suggested VSI is also correlated with other two existing VSIs before and after placement of DG unit(s).

     

应用捕食搜索策略的改进多态蚁群算法

结合捕食搜索策略对多态蚁群算法进行改良。该算法引入以下机制：在人工蚁选择路径阶段，设置侦查素路径为优先，为非侦查素路径设置惩罚因子；利用权值在侦查素和非侦查素路径都施加信息素，通过该机制避免多态蚁群算法陷入停滞；在每轮人工蚁最优结果的邻域应用捕食搜索策略，并通过竞争机制选择最优解更新信息素。通过TSP的仿真实验结果表明，提出的融合算法可以有目的地指导信息素分布，加快算法向最优解的收敛速度及提高最优解质量，克服传统多态蚁群算法的缺陷。     

Global Optimization through Rotation Space Search

This paper introduces a new algorithmic technique for solving certain problems in geometric computer vision. The main novelty of the method is a branch-and-bound search over rotation space, which is used in this paper to determine camera orientation. By searching over all possible rotations, problems can be reduced to known fixed-rotation problems for which optimal solutions have been previously given. In particular, a method is developed for the estimation of the essential matrix, giving the first guaranteed optimal algorithm for estimating the relative pose using a cost function based on reprojection errors. Recently convex optimization techniques have been shown to provide optimal solutions to many of the common problems in structure from motion. However, they do not apply to problems involving rotations. The search method described in this paper allows such problems to be solved optimally. Apart from the essential matrix, the algorithm is applied to the camera pose problem, providing an optimal algorithm. The approach has been implemented and tested on a number of both synthetically generated and real data sets with good performance. NICTA is funded by the Australian Government’s Backing Australia’s Ability initiative, in part through the Australian Research Council.     

A new technique for multidimensional capacity expansion projects

The paper presents a new technique for the optimal capacity expansion of projects with multidemands. Fast Enumeration Algorithm is developed for both project timing problems and combined project selection and timing problems involving high cost water resources projects. The structure of the problem is seccessively exploited to produce computationally efficient algorithm to solve problems of dimensions encountered in water resources systems. A numerical example is solved on IBM-360 and the results are presented.     

改进乌鸦搜索算法在邮件集散中心航空运力调度中的应用

邮件集散中心航空运力调度涉及固定运力和备选运力两种调度对象,本文在航空运力资源充足的前提下,建立了以最小化运输成本为目标的优化模型,研究了一种改进的乌鸦搜索算法求解方法.首先根据问题的数学模型,引入惩罚函数法将部分约束转化为惩罚项,与目标函数共同构成适应度函数;然后引入Logistic混沌映射提高初始种群的多样性;根据问题的特点,提出了基于个体最优追随机制和正余弦算法的位置更新策略,并引入交叉变异机制以丰富搜索过程中种群的多样性.通过大量算例实验分析,证明了该算法的有效性与优越性.     

交互式学习的布谷鸟搜索算法

针对布谷鸟搜索算法在求解旅行商问题时,存在初期信息缺乏严重和收敛速度慢等问题,提出一种交互式学习的布谷鸟搜索算法（Interactive Learning Cuckoo Search Algorithm,ILCSA）。为提高布谷鸟搜索算法的搜索效率,结合蚁群优化算法构建双层交互学习模型,将蚁群作为底层种群,布谷鸟作为高层种群,双种群互相学习,合作寻优,提高搜索速度;此外,在布谷鸟搜索算法中引入强化学习策略,自适应更新步长,并对发现概率进行动态调整,深度优化最优解,进一步提高解的质量。最后采用多组不同规模的标准TSPLIB算例与其他优化算法进行对比,结果表明ILCSA算法在求解精度和稳定性方面表现更优。     

求解Web集群文档分布的混沌搜索算法

提出Web集群文档分布方案,用M/G/1/K PS排队模型对服务器进行建模,将文档分布问题转化为0-1整数规划问题,然后求解该规划问题。针对该类0-1整数规划问题,给出一种基于混沌搜索的求解算法,该算法让多个独立的混沌变量在其各自的轨道中搜索,使得对应生成的0-1矩阵能遍历任意一种可能的分布,从而能搜索到全局最优解。设计一种基于贪婪思想的文档分布算法。测试表明,混沌搜索算法能找到全局最优解,优于传统的贪婪算法。     

A unified analysis of value-function-based reinforcement- learning algorithms

Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.