Journal articles: 'Neighbourhood algorithm'

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Wightman, Rick A., and Emin Z. Baskent. "Forest neighbourhoods for timber harvest scheduling." Forestry Chronicle 70, no. 6 (December 1, 1994): 768–72. http://dx.doi.org/10.5558/tfc70768-6.

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Forest management involves exploring through time the scheduling opportunities for timber and non-timber values within a forest. The ability to identify and form neighbourhoods — areas of suitable stand conditions and locations — is critical to this endeavour. This paper presents a GIS-based algorithm for identifying and forming forest neighbourhoods suitable for timber harvest scheduling. The resulting neighbourhoods are contiguous and overlapping, composed of stands sharing similar attributes. Similarity is based on a definable similarity list where stand conditions closest to one another in the list are most similar to one another. The algorithm is demonstrated with a single stand example and then a small forest example. Control of neighbourhood size is limited using a vector data model, except in forests composed of small stands. The examples illustrate that neighbourhood inclusion is dependent on both a forest stand's condition and relative position in the forest. The paper concludes with suggestions for further development of the algorithm. Key words: timber harvest scheduling, forest management, spatial modelling, GIS, neighbourhoods

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Chmiel, W., P. Kadłuczka, J. Kwiecień, and B. Filipowicz. "A comparison of nature inspired algorithms for the quadratic assignment problem." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 4 (August 1, 2017): 513–22. http://dx.doi.org/10.1515/bpasts-2017-0056.

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AbstractThis paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NP-hard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined.

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QIU, KE. "ON A UNIFIED NEIGHBOURHOOD BROADCASTING SCHEME FOR INTERCONNECTION NETWORKS." Parallel Processing Letters 17, no. 04 (December 2007): 425–37. http://dx.doi.org/10.1142/s0129626407003137.

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The neighbourhood broadcasting problem in an interconnection network is defined as sending a fixed sized message from the source node to all its neighbours in a single-port model. Previously, this problem has been studied for several interconnection networks including the hypercube and the star. The objective of such works has been to minimize the total number of steps required for the neighbourhood broadcasting algorithms. Here, we first use a general neighbourhood broadcasting scheme to develop a neighbourhood broadcasting algorithm for the star interconnection network that is asymptotically optimal, conceptually simple, and easy to implement since routing for all nodes involved is uniform. It uses the cycle structures of the star graph as well as the standard technique of recursive doubling. We then show that the scheme for the star network is general enough to be applied to a broader family of interconnection networks such as the pancake interconnection network for which no previous neighbourhood broadcasting algorithm is known, resulting in asymptotically optimal algorithms. Finally, we use this scheme to develop neighbourhood broadcasting algorithms for multiple messages for several interconnection networks.

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Wang, Y. D., X. C. Lu, and J. R. Shen. "Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP)." Advances in Production Engineering & Management 16, no. 2 (June 25, 2021): 173–84. http://dx.doi.org/10.14743/apem2021.2.392.

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The multiple traveling salesman problem (mTSP) is an extension of the traveling salesman problem (TSP), which has wider applications in real life than the traveling salesman problem such as transportation and delivery, task allocation, etc. In this paper, an improved genetic algorithm (VNS-GA) that uses polar coordinate classification to generate the initial solutions is proposed. It integrates the variable neighbourhood algorithm to solve the multiple objective optimization of the mTSP with workload balance. Aiming to workload balance, the first design of this paper is about generating initial solutions based on the polar coordinate classification. Then a distance comparison insertion operator is designed as a neighbourhood action for allocating paths in a targeted manner. Finally, the neighbourhood descent process in the variable neighbourhood algorithm is fused into the genetic algorithm for the expansion of search space. The improved algorithm is tested on the TSPLIB standard data set and compared with other genetic algorithms. The results show that the improved genetic algorithm can increase computational efficiency and obtain a better solution for workload balance and this algorithm has wild applications in real life such as multiple robots task allocation, school bus routing problem and other optimization problems.

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Wang, Y. D., X. C. Lu, and J. R. Shen. "Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP)." Advances in Production Engineering & Management 16, no. 2 (June 25, 2021): 173–84. http://dx.doi.org/10.14743/apem2021.2.392.

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The multiple traveling salesman problem (mTSP) is an extension of the traveling salesman problem (TSP), which has wider applications in real life than the traveling salesman problem such as transportation and delivery, task allocation, etc. In this paper, an improved genetic algorithm (VNS-GA) that uses polar coordinate classification to generate the initial solutions is proposed. It integrates the variable neighbourhood algorithm to solve the multiple objective optimization of the mTSP with workload balance. Aiming to workload balance, the first design of this paper is about generating initial solutions based on the polar coordinate classification. Then a distance comparison insertion operator is designed as a neighbourhood action for allocating paths in a targeted manner. Finally, the neighbourhood descent process in the variable neighbourhood algorithm is fused into the genetic algorithm for the expansion of search space. The improved algorithm is tested on the TSPLIB standard data set and compared with other genetic algorithms. The results show that the improved genetic algorithm can increase computational efficiency and obtain a better solution for workload balance and this algorithm has wild applications in real life such as multiple robots task allocation, school bus routing problem and other optimization problems.

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Kazakovtsev, Lev, Dmitry Stashkov, Mikhail Gudyma, and Vladimir Kazakovtsev. "Algorithms with greedy heuristic procedures for mixture probability distribution separation." Yugoslav Journal of Operations Research 29, no. 1 (2019): 51–67. http://dx.doi.org/10.2298/yjor171107030k.

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For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization). In our new VNS algorithms, this combination is used for forming randomized neighbourhoods to search for better solutions. The results of computational experiments made on classical data sets and the testings of production batches of semiconductor devices shipped for the space industry demonstrate that new algorithms allow us to obtain better results, higher values of the log likelihood objective function, in comparison with the EM algorithm and its modifications.

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Heinz, Jeffrey. "On the role of locality in learning stress patterns." Phonology 26, no. 2 (August 2009): 303–51. http://dx.doi.org/10.1017/s0952675709990145.

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AbstractThis paper presents a previously unnoticed universal property of stress patterns in the world's languages: they are, for small neighbourhoods, neighbourhood-distinct. Neighbourhood-distinctness is a locality condition defined in automata-theoretic terms. This universal is established by examining stress patterns contained in two typological studies. Strikingly, many logically possible – but unattested – patterns do not have this property. Not only does neighbourhood-distinctness unite the attested patterns in a non-trivial way, it also naturally provides an inductive principle allowing learners to generalise from limited data. A learning algorithm is presented which generalises by failing to distinguish same-neighbourhood environments perceived in the learner's linguistic input – hence learning neighbourhood-distinct patterns – as well as almost every stress pattern in the typology. In this way, this work lends support to the idea that properties of the learner can explain certain properties of the attested typology, an idea not straightforwardly available in optimality-theoretic and Principle and Parameter frameworks.

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Zhang, Da Ming, Hua Yong Liu, Juan Chen, and Lu Li. "Two-Dimensional Extensions of Neighborhood Preserving Embedding." Applied Mechanics and Materials 198-199 (September 2012): 420–25. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.420.

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Neighbourhood Preserving Embedding (NPE) is a novel subspace learning algorithm, which aims at preserving the local neighbourhood structure on the data manifold and is a linear approximation to Locally Linear Embedding (LLE). However, in typical image recognition in 1D vectors space, where the number of data samples is smaller than the dimension of data space, suffering from the singularity problem of matrix, NPE algorithm cannot be implemented directly. In this paper, we investigate NPE directly on image matrix for image recognition. The proposed two-dimensional neighbourhood preserving embedding (2DNPE) and bilateral two-dimensional neighbourhood preserving embedding (B2DNPE) algorithms are all based directly on 2D image matrices rather than on 1D vectors as NPE does, thus the problem of singularity confronted in 1D case is overcome. 2DNPE performs compression only in row direction, while B2DNPE performs compression both in row and in column direction. The relation of them to 2DLPP (B2DLPP) are also presented. The proposed algorithms are evaluated on ORL face database and handwritten digits database.

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Yan, Xingya, Jian Lei, and Zhi Zhao. "Multidirectional Gradient Neighbourhood-Weighted Image Sharpness Evaluation Algorithm." Mathematical Problems in Engineering 2020 (April 7, 2020): 1–7. http://dx.doi.org/10.1155/2020/7864024.

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Aiming at the problem that the image sharpness evaluation algorithm in the photoelectric system has a slow speed in actual processing and is severely disturbed by noise, an improved image sharpness evaluation algorithm is proposed by combining multiscale decomposition tools and multidirectional gradient neighbourhood weighting. This paper applies non-subsampled shearlet transform (NSST) to perform multiscale transformation of the input images, obtaining high-frequency sub-band images and low-frequency sub-band images. In order to enhance the detection of the edge orientation of images, multidirectional gradient processing of the image matrix is added to each sub-band image. In addition, the weight corresponding to the current pixel is obtained by calculating the inverse ratio of the gradient of each direction and the distance of the center pixel. Through calculating the ratio of the gradient neighbourhood weighting operators of high-frequency sub-band images and low-frequency sub-band images, the image sharpness evaluation value can be acquired further. Moreover, the image sequence collected by a certain type of photoelectric system is selected as the image sequence of the noisy real environment for simulation experiments and compared with the current mainstream algorithms. Finally, the experimental draws a conclusion that compared with the mainstream evaluation algorithms, the evaluation results of the proposed method perform better in terms of steepness, sensitivity, and flat area fluctuation, it can better suppress noise and improve accuracy, and its running speed meets the basic requirements of the image sharpness evaluation algorithm.

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Mohammed Jabbar, Ayad, Ku Ruhana Ku-Mahamud, and Rafid Sagban. "An improved ACS algorithm for data clustering." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1506. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1506-1515.

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<span lang="EN-GB">Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms. </span>

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John, Collether. "Complete Neighbourhood Search Heuristic Algorithm for Portfolio Optimization." Tanzania Journal of Engineering and Technology 40, no. 2 (February 20, 2022): 97–104. http://dx.doi.org/10.52339/tjet.v40i2.736.

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In portfolio optimization, the fundamental goal of an investor is to optimally allocate investments between different assets. Mean-variance optimization methods make unrealistic assumptions to solve the problem of optimal allocation. On the other hand, when realistic constraints like holding size and cardinality are introduced it leads to optimal asset allocation which differ from the mean variance optimization. The resulting optimization problem become quite complex as it exhibits multiple local extrema and discontinuities. Heuristic algorithms work well for the complex problem. Therefore, a heuristic algorithm is developed which is based on hill climbing complete (HC-C). It is utilized to solve the extended portfolio optimization problem. In order to validate its performance, the proposed HC-C is tested with standard portfolio optimization problem. Experimental results are benchmarked with the quadratic programming method and threshold accepting (TA) algorithm.

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Azfanizam, A. S., D. T. Pham, and A. A. Faieza. "Combination of Adaptive Enlargement and Reduction in the Search Neighbourhood in the Bees Algorithm." Applied Mechanics and Materials 564 (June 2014): 614–18. http://dx.doi.org/10.4028/www.scientific.net/amm.564.614.

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The Bees Algorithm, a heuristic optimisation procedure that mimics bees foraging behaviour, is becoming more popular among swarm intelligence researchers. The algorithm involves neighbourhood and global search and is able to find promising solutions to complex multimodal optimisation problems. The purpose of neighbourhood search is to intensify the search effort around promising solutions, while global search is to enable avoidance of local optima. Despite numerous studies aimed at enhancing the Bees Algorithm, there have not been many attempts at studying neighbourhood search. In this work, the combination of adaptive enlargement and reduction of the search neighbourhood is presented. Two engineering design problems with constraints which were the pressure vessel and speed reducer were selected to demonstrate the performance of the modified algorithm. The experimental results obtained showed that this combination is beneficial to the proposed algorithm.

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Rickwood, P., and M. Sambridge. "Efficient parallel inversion using the Neighbourhood Algorithm." Geochemistry, Geophysics, Geosystems 7, no. 11 (November 2006): n/a. http://dx.doi.org/10.1029/2006gc001246.

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Kennett, B. L. N., K. Marson-Pidgeon, and M. S. Sambridge. "Seismic Source characterization using a neighbourhood algorithm." Geophysical Research Letters 27, no. 20 (October 15, 2000): 3401–4. http://dx.doi.org/10.1029/2000gl011559.

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Valcarce, Daniel, Javier Parapar, and Álvaro Barreiro. "Axiomatic Analysis of Language Modelling of Recommender Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 113–27. http://dx.doi.org/10.1142/s0218488517400141.

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Language Models constitute an effective framework for text retrieval tasks. Recently, it has been extended to various collaborative filtering tasks. In particular, relevance-based language models can be used for generating highly accurate recommendations using a memory-based approach. On the other hand, the query likelihood model has proven to be a successful strategy for neighbourhood computation. Since relevance-based language models rely on user neighbourhoods for producing recommendations, we propose to use the query likelihood model for computing those neighbourhoods instead of cosine similarity. The combination of both techniques results in a formal probabilistic recommender system which has not been used before in collaborative filtering. A thorough evaluation on three datasets shows that the query likelihood model provides better results than cosine similarity. To understand this improvement, we devise two properties that a good neighbourhood algorithm should satisfy. Our axiomatic analysis shows that the query likelihood model always enforces those constraints while cosine similarity does not.

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Miao, Yongfei, Luo Zhong, Yufu Yin, Chengming Zou, and Zhenjun Luo. "Research on dynamic task allocation for multiple unmanned aerial vehicles." Transactions of the Institute of Measurement and Control 39, no. 4 (February 1, 2017): 466–74. http://dx.doi.org/10.1177/0142331217693077.

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To solve the distributed task allocation problems of search and rescue missions for multiple unmanned aerial vehicles (UAVs), this paper establishes a dynamic task allocation model under three conditions: 1) when new targets are detected, 2) when UAVs break down and 3) when unexpected threats suddenly occur. A distributed immune multi-agent algorithm (DIMAA) based on an immune multi-agent network framework is then proposed. The technologies employed by the proposed algorithm include a multi-agent system (MAS) with immune memory, neighbourhood clonal selection, neighbourhood suppression, neighbourhood crossover and self-learning operators. The DIMAA algorithm simplifies the decision-making process among agents. The simulation results show that this algorithm not only obtains the global optimum solution, but also reduces the communication load between agents.

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Kalayci, Can B., Olcay Polat, and Surendra M. Gupta. "A variable neighbourhood search algorithm for disassembly lines." Journal of Manufacturing Technology Management 26, no. 2 (March 2, 2015): 182–94. http://dx.doi.org/10.1108/jmtm-11-2013-0168.

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Purpose – The purpose of this paper is to efficiently solve disassembly line balancing problem (DLBP) and the sequence-dependent disassembly line balancing problem (SDDLBP) which are both known to be NP-complete. Design/methodology/approach – This manuscript utilizes a well-proven metaheuristics solution methodology, namely, variable neighborhood search (VNS), to address the problem. Findings – DLBPs are analyzed using the numerical instances from the literature to show the efficiency of the proposed approach. The proposed algorithm showed superior performance compared to other techniques provided in the literature in terms of robustness to reach better solutions. Practical implications – Since disassembly is the most critical step in end-of-life product treatment, every step toward improving disassembly line balancing brings us closer to cost savings and compelling practicality. Originality/value – This paper is the first adaptation of VNS algorithm for solving DLBP and SDDLBP.

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Liu, Xiaobo, Kun Li, and Huizhi Ren. "A Hybrid Algorithm for the Permutation Flowshop Scheduling Problem without Intermediate Buffers." Discrete Dynamics in Nature and Society 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/548363.

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This paper deals with the permutation flowshop scheduling problem without intermediate buffers and presents a hybrid algorithm based on the scatter search and the variable neighborhood search. In the hybrid algorithm, the solutions with good quality and diversity are maintained by a reference set of scatter search, and the search at each generation starts from a solution generated from the reference set so as to improve the search diversity while guaranteeing the quality of the initial solution. In addition, a variable neighbourhood based on the notion of job-block is developed, and the neighbourhood size can adaptively change according to the construction of the job-block. Such a dynamic strategy can help to obtain a balance between search depth and diversity. Extensive experiments on benchmark problems are carried out and the results show that the proposed hybrid algorithm is powerful and competitive with the other powerful algorithms in the literature.

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Lenin, K. "TAILORED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research -GRANTHAALAYAH 5, no. 12 (June 30, 2020): 246–55. http://dx.doi.org/10.29121/granthaalayah.v5.i12.2017.500.

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This paper presents Tailored Particle Swarm Optimization (TPSO) algorithm for solving optimal reactive power problem. Particle Swarm optimization algorithm based on Membrane Computing is proposed to solve the problem. Tailored Particle Swarm Optimization (TPSO) algorithm designed with the framework and rules of a cell-like P systems, and particle swarm optimization with the neighbourhood search. In order to evaluate the efficiency of the proposed algorithm, it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to other specified algorithms. Simulation results show that Tailored Particle Swarm Optimization (TPSO) algorithm is superior to other algorithms in reducing the real power loss.

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Zhou, Shi Bo, and Wei Xiang Xu. "Local Outlier Detection Algorithm Based on Coefficient of Variation." Applied Mechanics and Materials 635-637 (September 2014): 1723–28. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1723.

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Local outliers detection is an important issue in data mining. By analyzing the limitations of the existing outlier detection algorthms, a local outlier detection algorthm based on coefficient of variation is introduced. This algorthms applies K-means which is strong in outliers searching, divides data set into sections, puts outliers and their nearing clusters into a local neighbourhood, then figures out the local deviation factor of each local neighbourhood by coefficient of variation, as a result, local outliers can more likely be found.The heoretic analysis and experimental results indicate that the method is ef fective and efficient.

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Sambridge, M. S., and B. L. N. Kennett. "Seismic Event Location: Nonlinear Inversion Using a Neighbourhood Algorithm." Pure and Applied Geophysics 158, no. 1 (February 2001): 241–57. http://dx.doi.org/10.1007/pl00001158.

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Chen, Rongfang, and Jun Tang. "A hybrid firefly algorithm based on modified neighbourhood attraction." International Journal of Innovative Computing and Applications 13, no. 5/6 (2022): 290. http://dx.doi.org/10.1504/ijica.2022.10053558.

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Chen, Rongfang, and Jun Tang. "A hybrid firefly algorithm based on modified neighbourhood attraction." International Journal of Innovative Computing and Applications 13, no. 5/6 (2022): 290. http://dx.doi.org/10.1504/ijica.2022.128436.

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Wallace, Richard J. "Partial (Neighbourhood) Singleton Arc Consistency for Constraint Satisfaction Problems." Fundamenta Informaticae 174, no. 3-4 (September 28, 2020): 311–44. http://dx.doi.org/10.3233/fi-2020-1944.

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Algorithms based on singleton arc consistency (SAC) show considerable promise for improving backtrack search algorithms for constraint satisfaction problems (CSPs). The drawback is that even the most efficient of them is still comparatively expensive. Even when limited to preprocessing, they give overall improvement only when problems are quite difficult to solve with more typical procedures such as maintained arc consistency (MAC). The present work examines a form of partial SAC and neighbourhood SAC (NSAC) in which a subset of the variables in a CSP are chosen to be made SAC-consistent or neighbourhood-SAC-consistent. Such consistencies, despite their partial character, are still well-characterized in that algorithms have unique fixpoints. Heuristic strategies for choosing an effective subset of variables are described and tested, the best being choice by highest degree and a more complex strategy of choosing by constraint weight after random probing. Experimental results justify the claim that these methods can be nearly as effective as the corresponding full version of the algorithm in terms of values discarded or problems proven unsatisfiable, while significantly reducing the effort required to achieve this.

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Wang, Shuangxin, Guibin Tian, Dingli Yu, and Yijiang Lin. "Dynamic Particle Swarm Optimization with Any Irregular Initial Small-World Topology." International Journal of Swarm Intelligence Research 6, no. 4 (October 2015): 1–23. http://dx.doi.org/10.4018/ijsir.2015100101.

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It is realized that the topological structure of the particle swarm optimization (PSO) algorithm has a great influence on its optimization ability. This paper presents a new dynamic small-world neighborhood PSO (D-SWPSO) algorithm whose neighbourhood structure can be constructed with any irregular initial networks. The choice of the learning exemplar is not only based upon the big clustering coefficient and the average shortest distance for a regular network, but also based upon the eigenvalues of Laplacian matrix for irregular networks. Therefore, the D-SWPSO is a PSO algorithm based on small-world topological neighbourhood with universal significance. The proposed algorithm is tested by some typical benchmark test functions, and the results confirm that there is a significant improvement over the basic PSO algorithm. Finally, the algorithm is applied to a real-world optimization problem, the economic dispatch on the IEEE30 system with wind farms. The results demonstrate that the proposed D-SWPSO is a practically feasible and effective algorithm.

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Maher, Stephen J. "Enhancing large neighbourhood search heuristics for Benders’ decomposition." Journal of Heuristics 27, no. 4 (February 23, 2021): 615–48. http://dx.doi.org/10.1007/s10732-021-09467-z.

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AbstractA general enhancement of the Benders’ decomposition (BD) algorithm can be achieved through the improved use of large neighbourhood search heuristics within mixed-integer programming solvers. While mixed-integer programming solvers are endowed with an array of large neighbourhood search heuristics, few, if any, have been designed for BD. Further, typically the use of large neighbourhood search heuristics is limited to finding solutions to the BD master problem. Given the lack of general frameworks for BD, only ad hoc approaches have been developed to enhance the ability of BD to find high quality primal feasible solutions through the use of large neighbourhood search heuristics. The general BD framework of SCIP has been extended with a trust region based heuristic and a general enhancement for large neighbourhood search heuristics. The general enhancement employs BD to solve the auxiliary problems of all large neighbourhood search heuristics to improve the quality of the identified solutions. The computational results demonstrate that the trust region heuristic and a general large neighbourhood search enhancement technique accelerate the improvement in the primal bound when applying BD.

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S N, Lohith Raj. "Using Median-LBPH Algorithm for Real-Time Face Recognition System." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1497–503. http://dx.doi.org/10.22214/ijraset.2021.39038.

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Abstract: The LBPH algorithm is used ubiquitously for Face Recognition applications in modern times because of its simplicity of implementation, despite providing high accuracy and less computation time. However, in conditions of varied illumination, face expression and angles at which face images are captured, its confidence is decreased. We propose a slightly modified algorithm that considers the median of the neighbourhood pixels rather than the pixel itself to overcome this issue. This algorithm is called Median-LBPH. The grey value of every pixel is replaced by the median of all the neighbourhood pixel values. Then the features are extracted, and a histogram representing the original image is saved in the model. This model, in turn, can be used to compare with histograms obtained from the faces in real-time footage to find a potential match. This algorithm is used in an end-to-end face recognition system, a web application prototype for Law Enforcement Agencies to maintain a central criminal database shared and accessed across various departments. A live surveillance system is added as part of this novel application so that whenever an already registered criminal appears live on surveillance cameras, a notification will be received, and personnel appropriate Law Enforcement authorities will receive e-mail and text messages through a secured channel. Keywords: Face Recognition, Median-Local Binary Pattern Histogram (MLBPH), Haar Cascade, Adaboost, Neighbourhood Median

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Cabrera-Guerrero, Guillermo, and Carolina Lagos. "Comparing Multi-Objective Local Search Algorithms for the Beam Angle Selection Problem." Mathematics 10, no. 1 (January 5, 2022): 159. http://dx.doi.org/10.3390/math10010159.

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In intensity-modulated radiation therapy, treatment planners aim to irradiate the tumour according to a medical prescription while sparing surrounding organs at risk as much as possible. Although this problem is inherently a multi-objective optimisation (MO) problem, most of the models in the literature are single-objective ones. For this reason, a large number of single-objective algorithms have been proposed in the literature to solve such single-objective models rather than multi-objective ones. Further, a difficulty that one has to face when solving the MO version of the problem is that the algorithms take too long before converging to a set of (approximately) non-dominated points. In this paper, we propose and compare three different strategies, namely random PLS (rPLS), judgement-function-guided PLS (jPLS) and neighbour-first PLS (nPLS), to accelerate a previously proposed Pareto local search (PLS) algorithm to solve the beam angle selection problem in IMRT. A distinctive feature of these strategies when compared to the PLS algorithms in the literature is that they do not evaluate their entire neighbourhood before performing the dominance analysis. The rPLS algorithm randomly chooses the next non-dominated solution in the archive and it is used as a baseline for the other implemented algorithms. The jPLS algorithm first chooses the non-dominated solution in the archive that has the best objective function value. Finally, the nPLS algorithm first chooses the solutions that are within the neighbourhood of the current solution. All these strategies prevent us from evaluating a large set of BACs, without any major impairment in the obtained solutions’ quality. We apply our algorithms to a prostate case and compare the obtained results to those obtained by the PLS from the literature. The results show that algorithms proposed in this paper reach a similar performance than PLS and require fewer function evaluations.

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Roy, Sharadindu, and Prof Samar Sen Sarma. "AN APPROACH TO GENERATE MST WITHOUT CHECKING CYCLE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (August 30, 2005): 408–18. http://dx.doi.org/10.24297/ijct.v4i2b1.3229.

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Abstract: A minimum spanning tree of an undirected graph can be easily obtained using classical algorithms by Prim or Kruskal. MST generation is a NP hard problem. Now this paper represents an algorithm to find minimum spanning tree without checking cycle. Good time and space complexities are the major concerns of this algorithm. Running Time (complexity) of this algorithm = O(E*log V) (E = edges, V = nodes),which is obviously better than primâ€™s algorithm(complexity- E +Vlog V) . Â This algorithms operate at O(E * log(V)) time, though Primâ€™s can be optimized to O(E + V log V) by using specialized data structures(heap). For large graphs, these algorithms can take significant amount of time to complete. This algorithm is important in many real world applications. One example is an internet service provider determining the best way to install underground wires in a neighbourhood in order to use the least amount of wire and dig the least amount of ground.

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Huo, Bing Quan, and Feng Ling Yin. "A Contour Tracing Algorithm for Ginseng Shape." Applied Mechanics and Materials 401-403 (September 2013): 1268–71. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1268.

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More and more applications of computer technology are used in the field of agriculture. In this paper, Image processing technology is applied to ginseng shape. Get a color image from the device, remove the color information and reserve the boundary information. Highlight the image edge information by gradient sharpening, binary image and use 8-neighbourhood algorithm for tracking the border.

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Ransan-Cooper, Hedda, Björn C. P. Sturmberg, Marnie E. Shaw, and Lachlan Blackhall. "Applying responsible algorithm design to neighbourhood-scale batteries in Australia." Nature Energy 6, no. 8 (July 22, 2021): 815–23. http://dx.doi.org/10.1038/s41560-021-00868-9.

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Tan, Yuyang, Haijiang Zhang, Junlun Li, Chen Yin, and Furong Wu. "Focal mechanism determination for induced seismicity using the neighbourhood algorithm." Geophysical Journal International 214, no. 3 (June 6, 2018): 1715–31. http://dx.doi.org/10.1093/gji/ggy224.

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Sambridge, Malcolm. "Geophysical inversion with a neighbourhood algorithm-II. Appraising the ensemble." Geophysical Journal International 138, no. 3 (September 1999): 727–46. http://dx.doi.org/10.1046/j.1365-246x.1999.00900.x.

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Hu, Xiaolin, Carlos A. Coello Coello, and Zhangcan Huang. "A new multi-objective evolutionary algorithm: neighbourhood exploring evolution strategy." Engineering Optimization 37, no. 4 (June 2005): 351–79. http://dx.doi.org/10.1080/03052150500035658.

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Xiao, Yiyong, Qiuhong Zhao, Ikou Kaku, and Nenad Mladenovic. "Variable neighbourhood simulated annealing algorithm for capacitated vehicle routing problems." Engineering Optimization 46, no. 4 (June 19, 2013): 562–79. http://dx.doi.org/10.1080/0305215x.2013.791813.

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Herremans, Dorien, and Kenneth Sörensen. "Composing first species counterpoint with a variable neighbourhood search algorithm." Journal of Mathematics and the Arts 6, no. 4 (December 2012): 169–89. http://dx.doi.org/10.1080/17513472.2012.738554.

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Ying, Kuo Ching, Shih Wei Lin, and Chung Cheng Lu. "Cell formation using a simulated annealing algorithm with variable neighbourhood." European J. of Industrial Engineering 5, no. 1 (2011): 22. http://dx.doi.org/10.1504/ejie.2011.037224.

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İnkaya, Tülin, Sinan Kayalıgil, and Nur Evin Özdemirel. "An adaptive neighbourhood construction algorithm based on density and connectivity." Pattern Recognition Letters 52 (January 2015): 17–24. http://dx.doi.org/10.1016/j.patrec.2014.09.007.

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Akpinar, Sener. "Hybrid large neighbourhood search algorithm for capacitated vehicle routing problem." Expert Systems with Applications 61 (November 2016): 28–38. http://dx.doi.org/10.1016/j.eswa.2016.05.023.

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Zaixin, Zhao, Cheng Lizhi, and Cheng Guangquan. "Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation." IET Image Processing 8, no. 3 (March 1, 2014): 150–61. http://dx.doi.org/10.1049/iet-ipr.2011.0128.

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Santini, Alberto. "An adaptive large neighbourhood search algorithm for the orienteering problem." Expert Systems with Applications 123 (June 2019): 154–67. http://dx.doi.org/10.1016/j.eswa.2018.12.050.

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Ren, Hang, and Taotao Hu. "An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints." Sensors 20, no. 13 (July 3, 2020): 3722. http://dx.doi.org/10.3390/s20133722.

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This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.

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Wang, Sunxin, and Yan Li. "Variable Neighbourhood Search and Mathematical Programming for Just-in-Time Job-Shop Scheduling Problem." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/431325.

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This paper presents a combination of variable neighbourhood search and mathematical programming to minimize the sum of earliness and tardiness penalty costs of all operations for just-in-time job-shop scheduling problem (JITJSSP). Unlike classical E/T scheduling problem with each job having its earliness or tardiness penalty cost, each operation in this paper has its earliness and tardiness penalties, which are paid if the operation is completed before or after its due date. Our hybrid algorithm combines (i) a variable neighbourhood search procedure to explore the huge feasible solution spaces efficiently by alternating theswapandinsertionneighbourhood structures and (ii) a mathematical programming model to optimize the completion times of the operations for a given solution in each iteration procedure. Additionally, a threshold accepting mechanism is proposed to diversify the local search of variable neighbourhood search. Computational results on the 72 benchmark instances show that our algorithm can obtain the best known solution for 40 problems, and the best known solutions for 33 problems are updated.

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Ngo, Vuong M., Thuy-Van T. Duong, Tat-Bao-Thien Nguyen, Phuong T. Nguyen, and Owen Conlan. "An Efficient Classification Algorithm for Traditional Textile Patterns from Different Cultures Based on Structures." Journal on Computing and Cultural Heritage 14, no. 4 (December 31, 2021): 1–22. http://dx.doi.org/10.1145/3465381.

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Textiles have an important role in many cultures and have been digitised. They are three-dimensional objects and have complex structures, especially archaeological fabric specimens and artifact textiles created manually by traditional craftsmen. In this article, we propose a novel algorithm for textile classification based on their structures. First, a hypergraph is used to represent the textile structure. Second, multisets of k -neighbourhoods are extracted from the hypergraph and converted to one feature vector for representation of each textile. Then, the k -neighbourhood vectors are classified using seven most popular supervised learning methods. Finally, we evaluate experimentally the different variants of our approach on a data set of 1,600 textile samples with the 4-fold cross-validation technique. The experimental results indicate that comparing the variants, the best classification accuracies are 0.999 with LR, 0.994 with LDA, 0.996 with KNN, 0.994 with CART, 0.998 with NB, 0.974 with SVM, and 0.999 with NNM.

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Arvin Jay C Nadal, Angelo S Reyes, Mark Christoper R Blanco, Antolin J Alipio, and Dan Michael A Cortez. "Enhancement of collaborative filtering using myers-briggs type indicator (mbti) applied in recommendation system." South Asian Journal of Engineering and Technology 12, no. 1 (March 31, 2022): 104–10. http://dx.doi.org/10.26524/sajet.2022.12.016.

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Collaborative filtering is one of the most popular recommender systems being used today. Collaborative filtering algorithm depends on the association of one client's activity with another client's activity to discover his nearest neighbors. Related items are expected to be recommended according to his neighbor's similar interests or inclinations. Collaborative filtering algorithm deals with a major problem called the new user challenge or also known as the ‘cold-start’ problem that arises due to the lack of enough information about the new-coming user. The authors have employed an enhanced collaborative filtering algorithm by incorporating Myers-Briggs Type Indicator or MBTI. With the means of identifying each users MBTI personality types to create neighbourhoods, the researchers have alleviated the problem on the lack of similarities between inexperienced users to existing users. In addition, the system can predict new user ratings for each item using the average rating of users in the same neighbourhood. After predicting the rating, the item with the highest rating is recommended to inexperienced users, which provides a solution to the ‘cold-start’ problem

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Nadala, Arvin Jay C. "Enhancement of collaborative filtering using myers-briggs type indicator (mbti) applied in recommendation system." South Asian Journal of Engineering and Technology 12, no. 1 (March 31, 2022): 104–10. http://dx.doi.org/10.26524/sajet.2022.12.16.

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Collaborative filtering is one of the most popular recommender systems being used today. Collaborative filtering algorithm depends on the association of one client's activity with another client's activity to discover his nearest neighbors. Related items are expected to be recommended according to his neighbor's similar interests or inclinations. Collaborative filtering algorithm deals with a major problem called the new user challenge or also known as the ‘cold-start’ problem that arises due to the lack of enough information about the new-coming user. The authors have employed an enhanced collaborative filtering algorithm by incorporating Myers-Briggs Type Indicator or MBTI. With the means of identifying each users MBTI personality types to create neighbourhoods, the researchers have alleviated the problem on the lack of similarities between inexperienced users to existing users. In addition, the system can predict new user ratings for each item using the average rating of users in the same neighbourhood. After predicting the rating, the item with the highest rating is recommended to inexperienced users, which provides a solution to the ‘cold-start’ problem

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Soares, A. R., T. S. Körting, L. M. G. Fonseca, and A. K. Neves. "AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 91–95. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-91-2020.

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Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation.

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Pham, D. T., and M. Castellani. "The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 12 (July 6, 2009): 2919–38. http://dx.doi.org/10.1243/09544062jmes1494.

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The Bees Algorithm models the foraging behaviour of honeybees in order to solve optimization problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This article describes the Bees Algorithm in its basic formulation, and two recently introduced procedures that increase the speed and accuracy of the search. A critical review of the related swarm intelligence literature is presented. The effectiveness of the proposed method is compared to that of three state-of-the-art biologically inspired search methods. The four algorithms were tested on a range of well-known benchmark function optimization problems of different degrees of complexity. The experimental results proved the reliability of the bees foraging metaphor. The Bees Algorithm performed optimally, or near optimally, in almost all the tests. Compared to the three control algorithms, the Bees Algorithm was highly competitive in terms of learning accuracy and speed. The experimental tests helped also to shed further light on the search mechanisms of the Bees Algorithm and the three control methods, and to highlight their differences, strengths, and weaknesses.

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Tamilarasi, K., M. Gogulkumar, and K. Velusamy. "Enhancing the performance of social spider optimization with neighbourhood attraction algorithm." Journal of Physics: Conference Series 1767, no. 1 (February 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/1767/1/012017.

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Akpınar, Şener. "Large neighbourhood search algorithm for type-II assembly line balancing problem." Pamukkale University Journal of Engineering Sciences 23, no. 4 (2017): 444–50. http://dx.doi.org/10.5505/pajes.2016.75975.

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Journal articles on the topic 'Neighbourhood algorithm'

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