Solving the medium newspaper production/distribution problem

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.

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.

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.

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

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.

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.

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.

Hybrid method integrating agent-based modeling and heuristic tree search for scheduling of complex batch processes

The objective of this study is to examine the impact of the Finished Goods Inventory Accounting Information System on the internal control of finished goods inventory at PT. Partners Adi Jaya Manunggal in Bandung City. Based on the obtained calculation results, it has been determined that the Accounting Information System (X) for Finished Goods Inventory is deemed to be of satisfactory quality, as indicated by a score of 3.39. The internal control system for managing finished goods inventory (Y) has demonstrated effective performance, as evidenced by a value of 3.37. The optimization and effective utilization of a precise accounting information system for finished goods inventory is expected to enhance internal control measures for such inventory.
 The research employed a descriptive verification research method at PT. Partners Adi Jaya Manunggal in Bandung City. In order to gather data and information that will substantiate this research, the approach involves the collection of secondary data and the utilization of linear regression hypothesis testing. This methodology aims to offer a comprehensive understanding of the impact of the Finished Goods Inventory Accounting Information System on the Internal Control System for Finished Goods Inventory.
 According to the findings of the study, the outcomes of the linear regression analysis indicate that the equation Y = 8.006 + 0.817. This finding indicates that a majority of the population in Mitra Adi Jaya Manunggal, Bandung City, accounts for 63.3%, while the remaining 36.7% represents an unexamined variable of influence.

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.

This paper is a study on minimum query peers and apply glossary semantic similarity of “HowNet” to P2P search. It introduces and analyzes the intelligence search algorithm and the glossary semantic similarity of “HowNet”. Based on the intelligence search algorithm, heuristic search algorithm on “HowNet” is proposed and analyzed.

The tumor diagnosis method based on gene expression profiles will be developed into the fast and effective method in clinical domain in the near future. Although DNA microarray experiments provide us with huge amount of gene expression data, only a few of genes are related to tumor in gene expression profiles. Moreover, it is difficult to select informative genes related to tumor from gene expression profiles because of its characteristics such as high dimensionality, small sample set and many noises in gene expression profiles. According to its characteristic, a novel heuristic breadth-first search algorithm based on support vector machines is proposed, which can simultaneously find as many informative gene subsets as possible in which the number of informative genes is almost least but its classification performance is almost highest in spite of its time-consuming characteristic. Three tumor sample sets are examined by the novel approach and experiments show that the novel approach is feasible and effective in tumor classification. Experiment results show that 100% of 4-fold cross-validation accuracy has been achieved by only two, four and four genes for leukemia, colon tumor and SRBCT (Small Round Blue Cells Tumor) datasets, respectively, which is superior to the results of other tumor classification methods. To avoid the affect of different partition of sample set, the full-fold cross-validated method that can more objectively evaluate the classification performance of informative gene subset is proposed.

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.

Weighted heuristic search in networks

Many problems of interest can be solved using heuristic search algorithms. When solving a heuristic search problem, we are often interested in estimating search progress, that is, how much longer until we have a solution. Previous work on search progress estimation derived formulas based on some relevant features that can be observed from the behavior of the search algorithm. In this paper, rather than manually deriving such formulas we leverage machine learning to learn more accurate search progress predictors automatically. We train a Long Short-Term Memory (LSTM) network, which takes as input sequences of nodes expanded by the search algorithm, and predicts how far along with the search we are. Importantly, our approach still treats the search algorithm as a black box and does not look into the contents of search nodes. An empirical evaluation shows our technique outperforms previous search progress estimation techniques.