考虑运行风险的主动配电网分布式电源多目标优化配置

考虑配电网运行中的不确定性,文章通过改进蒙特卡洛法生成大量预想事故集,利用潮流计算和拓扑分析得到接入分布式电源后系统运的行风险。提出一种考虑主动配电网运行风险的分布式电源多目标优化配置模型,将主动配电网运行带来的运行风险RL与分布式电源运行成本CDG作为目标函数,采用改进的粒子群算法对多目标优化模型进行求解,获得分布式电源安装位置和安装容量以及运行风险与运行成本之间的权衡关系。仿真算例表明,所提出的考虑主动配电网运行风险的分布式电源多目标优化配置方法,与单一只考虑经济性或者可靠性的优化模型相比更加合理,适用于分布式电源的优化选址和定容,验证了该模型的可行性。     

目标驱动的基于形状线探索布线算法

对于实现焊盘外形尺寸各异、线宽及线间距离多变的印刷电路板布线,本文提出了目标驱动的基于形状线探索布线算法,该算法是在对障碍物做出包容矩形的基础上,又结合无网格线探索布线算法的特点来完成布线,使得布线具有目标性,同时,对于障碍物采用了灵活的绕障探索,大大提高了布线的成功率和布线速度。     

石油化工用阀门外漏的分析及控制

介绍了石化行业用阀门外漏的现状，分析了目前国际上关于阀门外漏的主要标准及技术指标，对如何解决阀门外漏问题进行了研究和探讨。     

Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing – tabu search algorithm to solve the symmetrical traveling salesman problem

This paper applies a hybrid simulated annealing – tabu search algorithm to solve the Traveling Salesman Problem (TSP). Fully considering the characteristics of the hybrid algorithm, we develop a dynamic neighborhood structure for the hybrid algorithm to improve search efficiency by reducing the randomness of the conventional 2-opt neighborhood. A circle-directed mutation is developed to achieve this dynamic neighborhood structure. Furthermore, we propose adaptive parameters that can be automatically adjusted by the algorithm based on context specific examples. This negates the need to frequently readjust algorithm parameters. We employ benchmarks obtained from TSPLIB (a library of sample instances for the TSP) to test our algorithm, and find that the proposed algorithm can obtain satisfactory solutions within a reasonable amount of time. The experimental results demonstrate that the proposed hybrid algorithm can overcome the disadvantages of traditional simulated annealing and tabu search methods. The results also show that the dynamic neighborhood structure is more efficient and accurate than the classical 2-opt. Also, adaptive parameters are appropriate for almost all of the numerical examples tested in this paper. Finally, the experimental results are compared with those of other algorithms, to demonstrate the improved accuracy and efficiency of the proposed algorithm.     

On the linear approximation of mixture internal energies of departure

Direct Monte Carlo simulation of internal energies of departure for binary mixtures of geological interest are gathered and compared to those calculated using a linear mixing rule. Simulation results for gas–oil, oil–oil, and oil–water mixtures show that the linear mixing rule used in the Gibbs–Helmholtz Constrained (GHC) equation of state framework gives accurate approximations of binary mixture internal energies of departure. A flowchart for computing internal energies of departure using Monte Carlo simulation is included along with a sensitivity analysis for the GHC mixture energy parameter with respect to uncertainty in internal energies of departure.     

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.     

Regional and Global Technological Knowledge Search Strategies and the Innovative Performance of Large Multinational Corporations

This study focuses on the innovative performance implications of large MNCs' regional and global technological knowledge search strategies. In networked MNCs, the parent can still offer valuable knowledge to subsidiaries. The parent's and a subsidiary's knowledge becomes complementary if an MNC appropriately adopts a global strategy at the parent level and a regional strategy at the subsidiary level. An analysis of the world's largest firms in the Electrical Equipment industry shows that, in general, a global strategy improves the innovative performance of the MNC. Meanwhile, only the combination of a global strategy at the parent level and a regional strategy at the subsidiary level is positively associated with the innovative performance of the MNC. This study contributes to the literature on networked MNCs and the debate of globalization and regionalization. Managerial implications are discussed as well.     

A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows

This paper presents a new hybrid variable neighborhood-tabu search heuristic for the Vehicle Routing Problem with Multiple Time windows. It also proposes a minimum backward time slack algorithm applicable to a multiple time windows environment. This algorithm records the minimum waiting time and the minimum delay during route generation and adjusts the arrival and departure times backward. The implementation of the proposed heuristic is compared to an ant colony heuristic on benchmark instances involving multiple time windows. Computational results on newly generated instances are provided.     

Modified harmony search optimization for constrained design problems

Constrained optimization is a major real-world problem. Constrained optimization problems consist of an objective function subjected to both linear and nonlinear constraints. Here a constraint handling procedure based on the fitness priority-based ranking method (FPBRM) is proposed. It is embedded into a harmony search (HS) algorithm that allows it to satisfy constraints. The HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. Here, the original heuristic HS was improved by combining both improved and global-best methods along with the FPBRM. The resulting modified harmony search (MHS) was then compared with the original HS technique and other optimization methods for several test problems.     

Distillation separation sequence optimization synthesis based on application of adaptive parallel tabu search

Abstract

Distillation separation sequences can be described as binary tree data structures, because of the analogous structures of distillation separation sequences and binary trees, and then by applying graph theory, the change mechanism of neighborhood separation points based on binary trees is built, correspondingly a kind of highly effective, evolutionary neighborhood structure is constructed. For the purpose of researching further tabu search algorithms, adaptive mechanisms and parallel techniques are introduced. That is, according to memory frequency information, the tabu length and the number of candidates are adaptively adjusted, and multitask parallel technology is realized through the arrangement of the search assignments. The example shows that adaptive parallel tabu searches can solve, successfully, large‐scale distillation separation sequence synthesis problems.