Planning of distribution substations, feeders and sectionalizing switches using heuristic algorithms

A heuristic search algorithm is given that determines and resolves the Hamiltonian circuit problem in directed graphs. The heuristic information of each vertex is a set composed of its possible path length values from the starting vertex, which is obtained by the path length extension algorithm. A detailed analysis of algorithm examples shows that the heuristic algorithm can greatly reduce the number of processing nodes compared to the backtracking algorithm without information; meanwhile, the algorithm’s time complexity is related to the number of loops in the directed graph. If a directed graph G(V, E) only contains loops that contain the starting point, then the Hamiltonian problem of G can be determined in polynomial time. The algorithm is implemented using C++ and Python programming, and all of the test graphs are generated by the random graph algorithm. The experiments show that the heuristic algorithm can reduce the number of extended nodes by at least 10% in directed random graphs with no or only a few loops. This heuristic algorithm can save time in applications that perform tasks while extending nodes because it reduce the number of extended nodes.

The objective of feeder reconfiguration on distribution system during normal operation is to find the best switch operation plan for reducing power losses and enhancing service reliability. By changing on/off status of sectionalizing-switches and tie-switches on a distribution system, feeder reconfiguration can be achieved. Also, the configured system must satisfy operation conditions as well as restrictions. Since the reconfiguration is done by changing the status of switches, it can be categorized as a discrete combinational optimization problem. Particle swarm optimization (PSO) is the one of the methods that can be used to solve optimal problems. Typical PSO is designed for continuous functions optimization, it is not designed for discrete functions optimization. Therefore, the operators of PSO algorithm must be reviewed and redefined to fit the application field of distribution feeder reconfiguration. This paper proposed a method which modifies the operators of PSO's formula based on the characters of both status of switches and shift operator to construct the binary coding particle swarm optimization for feeder reconfiguration. The test results show that the proposed method can apply to feeder reconfiguration effectively.

Heuristic search algorithm has been widely used in the fields of geographic information system, intelligent robot path planning, etc. This paper proposes a heuristic search algorithm based on artificial neural network, which is named ANN*. The architecture of the ANN* algorithm is similar to the A* algorithm, but it contains a novel heuristic function of the new design. This novel heuristic function utilizes the regression network, which is a kind of classical artificial neural network. In the ANN* algorithm, the artificial neural network is used to learn the features of massive map data and predict the path search difficulty of the map. In order to guarantee the diversity of the training data, this paper designs a random map generation framework, which is able to generate random grid maps according to the length, width and density. The experiments show that the ANN* algorithm can accomplish the task of path planning in the grid map with high quality. This paper provides a new scheme of exploring the applications of artificial neural network for heuristic search algorithm.

However, most heuristic algorithms are solely based on image features, due to the lack of diverse attributes of samples. Rather than rely on the existing datasets, this paper fully processes the original data. Firstly, the original videos were collected from several intelligent traffic surveillance cameras (monitoring nodes). Then, the three categories of objects in all videos were classified by a video image classifier. For each object in the critical class of cars, four attributes were identified, including ID, region of interest (ROI), sampling time and sampling position (position of sampling node). Next, the moving attitude (i.e. moving direction and speed) of each object was estimated through moving target tracking, and added to the preprocessed data. On this basis, a basic hyper-spherical hash search algorithm was created for the initial identification of similar objects. Furthermore, 93% of the objects were accurately identified through the recognition of license plate. Thereafter, a spatiotemporal correlation Bayesian network (STCBN) was established to predict the probability of each object to appear at a certain node in a period of time. Based on the probability, the heuristic factor was designed and used to enhance the basic hyper-spherical hash search algorithm. The enhancement improves the search performance, while reducing the environmental requirements of a single node. Therefore, the enhanced algorithm can be deployed effectively in edge computing systems. Our method was proved effective through experiment on a number of actual monitoring nodes.

With the proliferation of Web services and the evolution towards the Semantic Web comes the opportunity to automate various Web services tasks. A number of approaches have been proposed to tackle the problem of automatic web service composition. Most of them are inspired by the researches in AI planning. As heuristics plays a key role in the promotion of planning efficiency, the selection of heuristic mechanism is very important. In this paper, we propose the conception of composition distance, and propose a heuristic bidirectional search algorithm based on composition distance. The most contribution of this paper is : (1)propose the conception of composition distance; (2)a heuristic bidirectional search algorithm. The algorithm we proposed is effective and efficiency when applied in web service composition engine.

Today, congestion management on higher voltage levels is almost exclusively done preventively. However, integrating curative measures which utilize flexible power and innovative control equipment such as Impedance Controllers (ICs) can leverage significant benefits for grid operation. To ensure these curative measures take effect in due time, they should be activated by an automated system. In this work, a centralized replica of a distributed Multi-Agent System (MAS) is developed as a kind of simplified digital twin. The MAS uses heuristic search algorithms based on dc sensitivity analysis to eliminate grid congestion. By centrally replicating the underlying control algorithms, control interventions of the distributed system can be predicted. Also, testing and troubleshooting of new Heuristic Control Algorithms (HCAs) is facilitated by the centralized version. This way, in addition to the existing HCA, alternative heuristics are developed, implemented and tested in this centralized version. The proposed heuristics focus on the differences in the calculation of IC sensitivities and their accuracies for overload control. Besides, they make a broader range of choices of Flexible Power Units (FPUs) available to solve overloads in the grid, making the algorithm more efficient. Furthermore, the control heuristics are validated with Optimal Power Flow (OPF) approaches. For the validation of the system, the IEEE 39 New England Test-Bus system is used in Matlab MATPOWER.

Different types of distributed generation (DG) are broadly used and optimally placed in a distribution system to improve its performance. Since the network configuration affects the system operational conditions, the network reconfiguration and DG placement should be manipulated simultaneously. Nevertheless, the complexity of the problem may prevent from achieving the optimal solution. This paper presents a novel hybrid method of metaheuristic and heuristic algorithms, in order to boost robustness and shorten the computational runtime to achieve network minimum loss configuration in the presence of DGs. The developed backward/forward power flow is adopted to consider the PV(Q) model of DG. Moreover, different patterns of load types are taken into consideration to perform a practical study. To assess the capabilities of the proposed method, simulations are carried out on IEEE 33-bus and 83-bus practical distribution network of Taiwan Power Company. Furthermore, the proposed method is applied to a 33-bus unbalanced distribution network to verify its applicability in unbalanced distribution systems. The obtained results demonstrate the effectiveness of the proposed method to find optimal status of switches, as well as locations and sizes of DG units, in a rather shorter time than other approaches in the literature.

Path planning is one of the important directions in the field of intelligent vehicles research. Traditional path planning algorithms generally use Dijkstra algorithm, Breadth-First-Search (BFS) algorithm and A* algorithm. Dijkstra algorithm is a search-based algorithm, which can search to an optimal path, but the disadvantage is too many expansion nodes, which leads to insufficient search efficiency. BFS algorithm is a heuristic search algorithm, which reduces the disadvantage of too many expansion nodes and improves the search efficiency by heuristic function. A* algorithm is a heuristic search algorithm that combines Dijkstra’s algorithm and BFS algorithm, which has higher search efficiency and can search to an optimal path at the same time, but it is still lacking in the search mode and smoothness of the planned route. This paper first introduces the general path planning algorithm, then introduces and analyzes the A* algorithm, and proposes improvement measures for its shortcomings; finally, the executability and effectiveness of the improved algorithm are tested using simulation, and compared with the traditional A* algorithm, and the results show that the improved A* algorithm has good effect on path planning of intelligent vehicles.

Solving the medium newspaper production/distribution problem

In this work, we present a new multiuser detection (MUD) structure which results from incorporating the tabu search (TS) heuristic algorithm with the local search (LS) heuristic algorithm. The new proposed structure brings much improvement when compared to both the conventional (matched filter) detector and the decorrelating detector. Moreover, the new TS‐LS‐detector proposed here approximates well the performance of the optimal MUD detector but with a very low computational complexity. Copyright © 2008 John Wiley & Sons, Ltd.

This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution from a solvable deal, effectiveness of each approach was measured by how quickly a solution was reached, and how many nodes were traversed until a solution was reached. The employed methods have a potential to provide artificial intelligence enthusiasts with a better understanding of BoF and novel ways to solve perfect-information games and puzzles in general. The results indicate that the proposed heuristic search algorithms guided by machine learning provide a significant improvement in terms of number of nodes traversed over the provided DFS algorithm.

Scheduling is key towards improving the performance of a Flexible Assembly Line (FAL). In this paper, a Bilevel Differential Evolution (BiDE) algorithm to solve a FAL scheduling problem is proposed. The BiDE algorithm optimizes the performance of the FAL with respect to two criteria: the weighted sum of Earliness/Tardiness (E/T) penalties and the balance of the FAL. The performance of BiDE is evaluated using the data sets available in the literature and an evolutionary heuristic algorithm published earlier called BiGA. The experimental results show that the BiDE algorithm can solve the FAL scheduling problem effectively and exhibits a superior performance over BiGA.Note to Practitioners-This paper was motivated by the problem of allocating time and equipment resources efficiently in a flexible assembly line to improve its performance. Among the different methods to solve this problem, heuristic search techniques are becoming more popular. Current work in literature have proposed a heuristic search algorithm to solve this problem. However, there is ambiguity in the model. This paper aims to clarify the ambiguity in the proposed flexible assembly line model, as well as applying a different heuristic search algorithm for comparison purposes. Experimental results show that the proposed technique can solve the problem effectively.

A heuristic algorithm based on Monte Carlo methods for the Rural Postman Problem

Feeder reconfiguration is an important operational process in power distribution grids, which is implemented to enhance the system’s performance by managing the switches. Given variations of the electricity price and load pattern in smart distribution networks, the operational problems of the distribution system are largely time-dependent and very complex. To deal with these temporal dependencies, it is important to extend the problem across different time intervals. Toward this end, a multi-objective optimization model is presented in this study for dynamic feeder reconfiguration problem in the distribution system over multiple time intervals considering distributed generators, energy storage systems, and solar photovoltaic units. The demand response program including interruptible/curtailable service is proposed to enable energy consumers to rethink their energy consumption patterns based on incentive and punitive policies. The common objectives considered in the feeder reconfiguration problem are power loss and voltage deviation which are important objectives for traditional distribution systems, but less attention has been paid to distribution network voltage security. This study considers operational cost, energy loss and voltage stability index as objective functions that can meet operational and voltage security expectations. Dynamic feeder reconfiguration problem is a complex integer nonlinear program problem and hence it is difficult to solve which requires the use of appropriate optimization algorithms to converge to global optima or find close to global optima. In this paper, a modified honey bee matting optimization algorithm based on the new mating mechanism is presented to solve the multi-objective dynamic feeder reconfiguration problem. The proposed approach uses an eliminating zone concept to finds a set of non-dominated solutions during the search process. Furthermore, a fuzzy decision-maker is adopted to select the best compromise solution among the non-dominated solutions. The suggested approach is tested on the IEEE 33-node and 136-node test systems and its superiorities are shown through comparison with other evolutionary algorithms.

It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network, which is a candidate solution in 5G and beyond, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink scenario and aim to minimize total power spent by designing beamformers. We consider the case where perfect channel state information is not available in the CP. The original problem includes discontinuous terms with many constraints. We propose a novel method which transforms the problem into a smooth constraint-free form and a solution is found by the gradient descent approach. As a comparison, we consider the optimal method solving an extensive number of convex sub-problems, a known heuristic search algorithm and some sparse solution techniques. Heuristic search methods find a solution by solving a subset of all possible convex sub-problems. Sparse techniques apply some norm approximation (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) or convex approximation to make the objective function more tractable. We also derive a theoretical performance bound in order to observe how far the proposed method performs off the optimal method when running the optimal method is prohibitive due to computational complexity. Detailed simulations show that the performance of the proposed method is close to the optimal one, and it outperforms other methods analyzed.