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1
Shi, Haotian, Xiutao Feng, and Shengyuan Xu. "A Framework with Improved Heuristics to Optimize Low-Latency Implementations of Linear Layers." IACR Transactions on Symmetric Cryptology 2023, no. 4 (2023): 489–510. http://dx.doi.org/10.46586/tosc.v2023.i4.489-510.
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In recent years, lightweight cryptography has been a hot field in symmetric cryptography. One of the most crucial problems is to find low-latency implementations of linear layers. The current main heuristic search methods include the Boyar-Peralta (BP) algorithm with depth limit and the backward search. In this paper we firstly propose two improved BP algorithms with depth limit mainly by minimizing the Euclidean norm of the new distance vector instead of maximizing it in the tie-breaking process of the BP algorithm. They can significantly increase the potential for finding better results. Furthermore, we give a new framework that combines forward search with backward search to expand the search space of implementations, where the forward search is one of the two improved BP algorithms. In the new framework, we make a minor adjustment of the priority of rules in the backward search process to enable the exploration of a significantly larger search space. As results, we find better results for the most of matrices studied in previous works. For example, we find an implementation of AES MixColumns of depth 3 with 99 XOR gates, which represents a substantial reduction of 3 XOR gates compared to the existing record of 102 XOR gates.
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Chumpungam, Dawan, Panitarn Sarnmeta, and Suthep Suantai. "An Accelerated Convex Optimization Algorithm with Line Search and Applications in Machine Learning." Mathematics 10, no. 9 (2022): 1491. http://dx.doi.org/10.3390/math10091491.
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In this paper, we introduce a new line search technique, then employ it to construct a novel accelerated forward–backward algorithm for solving convex minimization problems of the form of the summation of two convex functions in which one of these functions is smooth in a real Hilbert space. We establish a weak convergence to a solution of the proposed algorithm without the Lipschitz assumption on the gradient of the objective function. Furthermore, we analyze its performance by applying the proposed algorithm to solving classification problems on various data sets and compare with other line search algorithms. Based on the experiments, the proposed algorithm performs better than other line search algorithms.
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Maliah, Shlomi, Ronen Brafman, and Guy Shani. "Increased Privacy with Reduced Communication in Multi-Agent Planning." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 209–17. http://dx.doi.org/10.1609/icaps.v27i1.13821.
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Multi-agent forward search (MAFS) is a state-of-the-art privacy-preserving planning algorithm. We describe a new variant of MAFS, called multi-agent forward-backward search (MAFBS) that uses both forward and backward messages to reduce the number of messages sent and obtain new privacy properties. While MAFS requires agents to send a state s produced by an action a to all agents that can apply any action in s, MAFBS sends such messages forward only to agents that have an action that requires one of the effects of a. To achieve completeness, it sends messages backward to agents that can supply a missing precondition. This more focused message passing scheme reduces states exchanged, and requires that agents be aware only of other agents that they directly interact with, leading to agent privacy.
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Wilt, Christopher, and Wheeler Ruml. "Robust Bidirectional Search via Heuristic Improvement." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 954–61. http://dx.doi.org/10.1609/aaai.v27i1.8662.
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Although the heuristic search algorithm A* is well-known to be optimally efficient, this result explicitly assumes forward search. Bidirectional search has long held promise for surpassing A*'s efficiency, and many varieties have been proposed, but it has proven difficult to achieve robust performance across multiple domains in practice. We introduce a simple bidirectional search technique called Incremental KKAdd that judiciously performs backward search to improve the accuracy of the forward heuristic function for any search algorithm. We integrate this technique with A*, assess its theoretical properties, and empirically evaluate its performance across seven benchmark domains. In the best case, it yields a factor of six reduction in node expansions and CPU time compared to A*, and in the worst case, its overhead is provably bounded by a user-supplied parameter, such as 1%. Viewing performance across all domains, it also surpasses previously proposed bidirectional search algorithms. These results indicate that Incremental KKAdd is a robust way to leverage bidirectional search in practice.
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Yang, Zhen-Ping, Yuliang Wang, and Gui-Hua Lin. "Variance-Based Modified Backward-Forward Algorithm with Line Search for Stochastic Variational Inequality Problems and Its Applications." Asia-Pacific Journal of Operational Research 37, no. 03 (2020): 2050011. http://dx.doi.org/10.1142/s0217595920500116.
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We propose a variance-based modified backward-forward algorithm with a stochastic approximation version of Armijo’s line search, which is robust with respect to an unknown Lipschitz constant, for solving a class of stochastic variational inequality problems. A salient feature of the proposed algorithm is to compute only one projection and two independent queries of a stochastic oracle at each iteration. We analyze the proposed algorithm for its asymptotic convergence, sublinear convergence rate in terms of the mean natural residual function, and optimal oracle complexity under moderate conditions. We also discuss the linear convergence rate with finite computational budget for the proposed algorithm without strong monotonicity. Preliminary numerical experiments indicate that the proposed algorithm is competitive with some existing algorithms. Furthermore, we consider an application in dealing with an equilibrium problem in stochastic natural gas trading market.
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Xu, Tong, Yang Xu, Dong Wang, Siwei Chen, Weigong Zhang, and Lihang Feng. "Path Planning for Autonomous Articulated Vehicle Based on Improved Goal-Directed Rapid-Exploring Random Tree." Mathematical Problems in Engineering 2020 (May 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/7123164.
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The special steering characteristics and task complexity of autonomous articulated vehicle (AAV) make it often require multiple forward and backward movements during autonomous driving. In this paper, we present a simple yet effective method, named head correction with fixed wheel position (HC-FWP), for the demand of multiple forward and backward movements. The goal-directed rapid-exploring random tree (GDRRT) algorithm is first used to search for a feasible path in the obstacle map, and then, the farthest node search (FNS) algorithm is applied to obtain a series of key nodes, on which HC-FWP is used to correct AAV heading angles. Simulation experiments with Dynapac CC6200 articulated road roller parameters show that the proposed improved goal-directed rapid-exploring random tree (IGDRRT), consisting of GDRRT, FNS, and HC-FWP, can search a feasible path on maps that require the AAV to move back and forth.
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Edelkamp, Stefan, Peter Kissmann, and Martha Rohte. "Symbolic and Explicit Search Hybrid through Perfect Hash Functions — A Case Study in Connect Four." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 10, 2014): 101–10. http://dx.doi.org/10.1609/icaps.v24i1.13637.
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This work combines recent advances in AI planning under memory limitation, namely bitvector and symbolic search. Bitvector search assumes a bijective mapping between state and memory addresses, while symbolic search compactly represents state sets. The memory requirements vary with the structure of the problem to be solved. The integration of the two algorithms into one hybrid algorithm for strongly solving general games initiates a BDD-based solving algorithm, which consists of a forward computation of the reachable state set, possibly followed by a layered backward retrograde analysis. If the main memory becomes exhausted, it switches to explicit-state two-bit retrograde search. We use the classical game of Connect Four as a case study, and solve some instances of the problem space-efficiently with the proposed hybrid search algorithm.
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Chumpungam, Dawan, Panitarn Sarnmeta, and Suthep Suantai. "A New Forward–Backward Algorithm with Line Searchand Inertial Techniques for Convex Minimization Problems with Applications." Mathematics 9, no. 13 (2021): 1562. http://dx.doi.org/10.3390/math9131562.
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For the past few decades, various algorithms have been proposed to solve convex minimization problems in the form of the sum of two lower semicontinuous and convex functions. The convergence of these algorithms was guaranteed under the L-Lipschitz condition on the gradient of the objective function. In recent years, an inertial technique has been widely used to accelerate the convergence behavior of an algorithm. In this work, we introduce a new forward–backward splitting algorithm using a new line search and inertial technique to solve convex minimization problems in the form of the sum of two lower semicontinuous and convex functions. A weak convergence of our proposed method is established without assuming the L-Lipschitz continuity of the gradient of the objective function. Moreover, a complexity theorem is also given. As applications, we employed our algorithm to solve data classification and image restoration by conducting some experiments on these problems. The performance of our algorithm was evaluated using various evaluation tools. Furthermore, we compared its performance with other algorithms. Based on the experiments, we found that the proposed algorithm performed better than other algorithms mentioned in the literature.
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Suantai, Suthep, Kunrada Kankam, and Prasit Cholamjiak. "A Novel Forward-Backward Algorithm for Solving Convex Minimization Problem in Hilbert Spaces." Mathematics 8, no. 1 (2020): 42. http://dx.doi.org/10.3390/math8010042.
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In this work, we aim to investigate the convex minimization problem of the sum of two objective functions. This optimization problem includes, in particular, image reconstruction and signal recovery. We then propose a new modified forward-backward splitting method without the assumption of the Lipschitz continuity of the gradient of functions by using the line search procedures. It is shown that the sequence generated by the proposed algorithm weakly converges to minimizers of the sum of two convex functions. We also provide some applications of the proposed method to compressed sensing in the frequency domain. The numerical reports show that our method has a better convergence behavior than other methods in terms of the number of iterations and CPU time. Moreover, the numerical results of the comparative analysis are also discussed to show the optimal choice of parameters in the line search.
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Zhou, Wentao. "The Influence of Uneven Distribution of Online Education Resources on the Development of Students in Backward Areas." Advances in Education, Humanities and Social Science Research 12, no. 1 (2024): 655. https://doi.org/10.56028/aehssr.12.1.655.2024.
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This paper aims to explore the influence of uneven distribution of online education resources on the development of students in backward areas, and puts forward the strategy of optimizing resource allocation through algorithms to improve this situation. Firstly, the study analyzes the current situation of online education resource allocation, and points out the uneven distribution of resources caused by geography, economic conditions and students' needs. Subsequently, an improved algorithm based on multi-objective optimization and heuristic search (FERA algorithm) is designed and implemented, aiming at distributing online education resources to students in backward areas more fairly and effectively. The experiment selects representative schools and students in backward areas as samples, collects relevant data, and uses FERA algorithm to optimize resource allocation. The experimental results show that FERA algorithm significantly improves the fairness and efficiency of resource allocation, meets the diverse needs of students, and has a positive impact on students' academic performance and comprehensive quality. This paper calls on the government, schools and all walks of life to make joint efforts to popularize algorithm optimization technology, strengthen infrastructure construction and formulate differentiated policies in order to realize the fair distribution of educational resources.
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Duran-Quintero, Michel, John E. Candelo, and Jose Soto-Ortiz. "A modified backward/forward sweep-based method for reconfiguration of unbalanced distribution networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 85–101. https://doi.org/10.11591/ijece.v9i1.pp85-101.
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A three-phase unbalanced power flow method can provide a more realistic scenario of how distribution networks operate. The backward/forward sweepbased power flow method (BF-PF) has been used for many years as an important computational tool to solve the power flow for unbalanced and radial power systems. However, some of the few available research tools produce many errors when they are used for network reconfiguration because the topology changes after multiple switch actions and the nodes are disorganized continually. This paper presents a modified BF-PF for threephase unbalanced radial distribution networks that is capable of arranging the system topology when reconfiguration changes the branch connections. A binary search is used to determine the connections between nodes, allowing the algorithm to avoid those problems when reconfiguration is carried out, regardless of node numbers. Tests are made to verify the usefulness of the proposed algorithm in both the IEEE 13-node test feeder and the 123-node test feeder, converging in every run where constraints are accomplished. This approach can be used easily for a large-scale feeder network reconfiguration. The full version of this modified backward/forward sweep algorithm is available for research at MathWorks.
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Holte, Robert. "Common Misconceptions Concerning Heuristic Search." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (2010): 46–51. http://dx.doi.org/10.1609/socs.v1i1.18160.
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This paper examines the following statements about heuristic search, which are commonly held to be true:More accurate heuristics result in fewer node expansions by A* and IDA*.A* does fewer node expansions than any other equally informed algorithm that finds optimal solutions. Any admissible heuristic can be turned into a consistent heuristic by a simple technique called pathmax.In search spaces whose operators all have the same cost A* with the heuristic function h(s)=0 for all states, s, is the same as breadth-first search.Bidirectional A* stops when the forward and backward search frontiers meet.The paper demonstrates that all these statements are false and provides alternative statements that are true.
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Sharon, Guni, Robert Holte, Ariel Felner, and Nathan Sturtevant. "Extended Abstract: An Improved Priority Function for Bidirectional Heuristic Search." Proceedings of the International Symposium on Combinatorial Search 7, no. 1 (2021): 139–40. http://dx.doi.org/10.1609/socs.v7i1.18399.
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Bidirectional search algorithms interleave a search forward from the start state (start ) and a search backward (i.e. using reverse operators) from the goal state (goal). We say that the two searches “meet in the middle” if neither search expands a node whose g-value (in the given direction) exceeds C*/2 , where C* is the cost of an optimal solution. The only bidirectional heuristic search algorithm that is guaranteed to meet in the middle under all circumstances is the recently introduced MM algorithm (Holte et al. 2016). The feature of MM that provides this guarantee is its unique priority functions for nodes on its open lists. In this short note we present MMe, which enhances MM’s priority function and is expected to expand fewer nodes than MM under most circumstances. We sketch a proof of MMe’s correctness, describe conditions under which MMe will expand fewer nodes than MM and vice versa, and experimentally compare MMe and MM on the 10-Pancake problem.
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Tang, Haili, Xiaojuan Cao, and Yuanbo Hu. "Application and effect evaluation of intelligent computing in education and teaching reform and innovation." HKIE Transactions 31, no. 3 (2024): 1–7. https://doi.org/10.33430/v31n3thie-2024-0027.
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A multi-objective optimisation method based on the colony ant algorithm is studied to address the problem of learning resource allocation in a high-dimensional data environment. Each ant makes its own search decisions, but their sex hormones interact to speed up the search. A training method based on multi-objective optimisation is designed. Interaction with teachers and classmates is carried out and improvements are made to their global optimisation ability through forward and backward methods. The Pareto record set saves and updates. A set of multi-objective resource scheduling algorithms is constructed. The experimental results show that all the methods proposed in this paper can achieve better results in limited projects.
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Lo, Shaw-Hwa, and Yiqiao Yin. "Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network." Machine Learning and Knowledge Extraction 3, no. 4 (2021): 922–45. http://dx.doi.org/10.3390/make3040046.
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Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm, called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the “dagger technique”. First, the paper proposes to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for making good predictions in text classification tasks. Next, a greedy search algorithm, called the Backward Dropping Algorithm, is proposed to handle long-term dependencies in the dataset. Moreover, the paper proposes a novel engineering technique called the “dagger technique” that fully preserves the relationship between the explanatory variable and the response variable. The proposed techniques can be further generalized into any feed-forward Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), and any neural network. A real-world application on the Internet Movie Database (IMDB) is used and the proposed methods are applied to improve prediction performance with an 81% error reduction compared to other popular peers if I-score and “dagger technique” are not implemented.
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Shperberg, Shahaf S., Steven Danishevski, Ariel Felner, and Nathan R. Sturtevant. "Iterative-deepening Bidirectional Heuristic Search with Restricted Memory." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 331–39. http://dx.doi.org/10.1609/icaps.v31i1.15978.
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The field of bidirectional heuristic search has recently seen great advances. However, the subject of memory-restricted bidirectional search has not received recent attention. In this paper we introduce a general iterative deepening bidirectional heuristic search algorithm (IDBiHS) that searches simultaneously in both directions while controlling the meeting point of the search frontiers. First, we present the basic variant of IDBiHS, whose memory is linear in the search depth. We then add improvements that exploit consistency and front-to-front heuristics. Next, we move to the case where a fixed amount of memory is available to store nodes during the search and develop two variants of IDBiHS: (1) A*+IDBiHS, that starts with A* and moves to IDBiHS as soon as memory is exhausted. (2) A variant that stores partial forward frontiers until memory is exhausted and then tries to match each of them from the backward side. Finally, we experimentally compare the new algorithms to existing unidirectional and bidirectional ones. In many cases our new algorithms outperform previous ones in both node expansions and time.
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Chotchantarakun, Knitchepon, and Ohm Sornil. "Adaptive Multi-level Backward Tracking for Sequential Feature Selection." Journal of ICT Research and Applications 15, no. 1 (2021): 1–20. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.15.1.1.
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In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.
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Linares López, Carlos, and Daniel Borrajo. "Adding Diversity to Classical Heuristic Planning." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (2010): 73–80. http://dx.doi.org/10.1609/socs.v1i1.18171.
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In this paper we propose a new algorithm for solving general two-player turn-taking games that performs symbolic search utilizing binary decision diagrams (BDDs). It consists of two stages: First, it determines all breadth-first search (BFS) layers using forward search and omitting duplicate detection, next, the solving process operates in backward direction only within these BFS layers thereby partitioning all BDDs according to the layers the states reside in. We provide experimental results for selected games and compare to a previous approach. This comparison shows that in most cases the new algorithm outperforms the existing one in terms of runtime and used memory so that it can solve games that could not be solved before with a general approach.
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Kissmann, Peter, and Stefan Edelkamp. "Layer-Abstraction for Symbolically Solving General Two-Player Games." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (2010): 63–70. http://dx.doi.org/10.1609/socs.v1i1.18170.
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In this paper we propose a new algorithm for solving general two-player turn-taking games that performs symbolic search utilizing binary decision diagrams (BDDs). It consists of two stages: First, it determines all breadth-first search (BFS) layers using forward search and omitting duplicate detection, next, the solving process operates in backward direction only within these BFS layers thereby partitioning all BDDs according to the layers the states reside in. We provide experimental results for selected games and compare to a previous approach. This comparison shows that in most cases the new algorithm outperforms the existing one in terms of runtime and used memory so that it can solve games that could not be solved before with a general approach.
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Liu, Huiling, Huiyan Jiang, and Ruiping Zheng. "The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification." Computational and Mathematical Methods in Medicine 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/7369137.
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We propose a novel feature selection algorithm for liver tissue pathological image classification. To improve the efficiency of feature selection, the same feature values of positive and negative samples are removed in rough selection. To obtain the optimal feature subset, a new heuristic search algorithm, which is called Maximum Minimum Backward Selection (MMBS), is proposed in precise selection. MMBS search strategy has the following advantages. (1) For the deficiency of Discernibility of Feature Subsets (DFS) evaluation criteria, which makes the class of small samples invalid for unbalanced samples, the Weighted Discernibility of Feature Subsets (WDFS) evaluation criteria are proposed as the evaluation strategy of MMBS, which is also available for unbalanced samples. (2) For the deficiency of Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS), which can only add or only delete feature, MMBS decides whether to add the feature to feature subset according to WDFS criteria for each feature firstly; then it decides whether to remove the feature from feature subset according to SBS algorithm. In this way, the better feature subset can be obtained. The experiment results show that the proposed hybrid feature selection algorithm has good classification performance for liver tissue pathological image.
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Kumar, Vipin, Shubham Swapnil, and V. R. Singh. "Adaptive Algorithm for Solving the Load Flow Problem in Distribution System." Journal of Intelligent Systems 27, no. 3 (2018): 377–91. http://dx.doi.org/10.1515/jisys-2016-0084.
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Abstract This paper presents a fast and efficient method for load flow analysis of radial distribution networks. Here, an adaptive algorithm is proposed to analyze the load flow problem of distribution systems. An adaptive algorithm is the combination of backward/forward (BW/FW) sweep and cuckoo search (CS) algorithms. In the proposed method, the optimum load flow analysis of the radial distribution system is attained, while optimizing the voltage and current computation of the BW/FW sweep algorithm. Now, by the CS, the output voltage of the BW/FW sweep algorithm is compared with the standard voltage and optimized. From the optimized voltage and current, load flow parameters like power loss and real and reactive power flow are assessed. The proposed method is implemented using the MATLAB platform and tested into the IEEE 33 bus radial distribution system. The effectiveness of the proposed technique is determined by comparing with the BW/FW algorithm and genetic algorithm-based BW/FW algorithm.
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Chang, Jian Mei, and Huai Ping Feng. "Parameter Identification with FEM on Absorbing Boundary Condition and Genetic Algorithm (GA)." Applied Mechanics and Materials 52-54 (March 2011): 850–53. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.850.
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Researches of wave motion are important on theory and application. The forward problem and the backward problem are the two aspects that relate with each other. In this paper, with the combination of FEM considering BGT absorbing boundary condition for forward problem and GA method, the inversion of target’s characters in infinite area is discussed. The absorbing boundary condition is obtained through approximating the Sommerfeld radiation condition. Although the absorbing boundary condition can’t simulate the effect exactly, it can reach the limitation in the practice and has the character of solving the coupling that simplifies the calculations. Because of the use of finite element model method, the forward problems that involve special geometrical shaped scatter and multi scatters can be solved. The finite element model is used iteratively to compute the scattered field. In the inversion process, the GA method is adopted because of its advantage of global optimum search and parallel calculation. The numerical simulation examples of single and multiple show the validity of this method.
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Danuri, Danuri, and Widodo Prijodiprodjo. "Penerapan Bee Colony Optimization Algorithm untuk Penentuan Rute Terpendek (Studi Kasus : Objek Wisata Daerah Istimewa Yogyakarta)." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 7, no. 1 (2013): 65. http://dx.doi.org/10.22146/ijccs.3053.
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AbstrakPencarian rute terpendek merupakan suatu permasalahan optimasi yang sering dijadikan studi kasus bagi penelitian. Jarak merupakan faktor yang paling menentukan dalam melakukan penelusuran jalur-jalur yang akan dilalui. Jalur dengan jarak terpendek akan dipilih sebagai jalur pilihan.Algoritma bee colony optimization digunakan dalam penelitian ini untuk menyelesaikan permasalah pencarian rute terpendek. Terdapat dua proses utama pada saat penelusuran jalur yaitu forward dan backward. Algoritma bee colony optimization bekerja pada proses forward. Nilai probabilitas suatu jalur dijadikan dasar pada proses transisi jalur kemudian durasi waggle dance dari tiap lebah yang berhasil menemukan posisi tujuan akan dijadikan rute pilihan.Hasil yang diperoleh dalam penelitian ini adalah algoritma bee colony optimization dapat digunakan untuk menemukan rute terpendek. Jumlah lebah yang dilepas sangat mempengaruhi dalam menemukan rute-rute yang bisa dilalui. Semakin banyak jumlah lebah yang dilepas semakin besar peluang ditemukannya rute terpendek. Kata kunci— Rute Terpendek, Algoritma Bee Colony Optimization. AbstractThe shortest path determination is an optimization problem which often used as a case study for research. Distance is the most defining factor in performing the search paths to be passed. Path with the shortest distance would be chosen as a path selection.Bee colony optimization algorithm used in this study to complete problems shortest path determination. There are two main process es during search path that is forward and backward. Bee colony optimization algorithm works on the process forward. The value probability of a path is base intransition process and the duration of waggle dance track of every bee who had found the position of the goal will be a preferred route.The results obtained in this study is the bee colony optimization algorithm can be used to find shortest path. The number of bees are released greatly affects in finding routes that can be passed. The more the number of bees that removed the greater the chances of finding the shortest path. Keyword— Shortest Path, Bee Colony Optimization Algorithm
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Xu, Wei, and Jian Sheng Feng. "Parameter Optimization Design of Soil Nail Supporting to Deep Foundation Pits." Advanced Materials Research 671-674 (March 2013): 235–39. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.235.
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In order to reduce the cost of soil nail supporting to deep foundation pits and ensure safety as well as stability, a model is established to optimize the design parameters for soil nail wall. This paper is concerned with the analysis of soil nail channel number, diameter, length, horizontal spacing, vertical spacing and dip angle. For parameter optimization design of soil nail wall is a complex problem, the traditional method is easy to fall into local optima. The genetic algorithm is a global optimization method. To some extent it has the drawback of appearing premature convergence and oscillation so as to slow iterative process. Therefore, a forward and backward search algorithm is proposed, which is combined with genetic algorithm. Furthermore some improvement measures are put forward by means of improved hybrid genetic algorithm. As a result engineering studies arrive at an optimum design. It shows that the optimized design results of IHGA not only ensure the stability of foundation, but greatly reduce the cost of engineering materials.
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Zhang, Hui, and Yong Qi Wang. "A Public Transportation Query System Based on Automatic Correction for Speech Recognition." Applied Mechanics and Materials 644-650 (September 2014): 4148–51. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4148.
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This paper presents our recent work towards the development of a public transportation query system based on speech error correction and natural language processing. The system enhances the accuracy of speech recognition by combining rules and word frequencies. This paper summarizes and classifies the error types of speech recognition in the field of transportation query, and then brings forth a semantic framework and a word-frequency-precedence mechanism with pinyin-text mapping. For the text after correctly recognizing, the system extracts the origin and the destination by forward-search-backward-match algorithm. And it synthesizes the least transferring algorithm and the shortest path algorithm to find a route. So it has characteristics of humanity and intelligence.
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Wu, Yinan, Gongzhuang Peng, Hongwei Wang, and Heming Zhang. "A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks." Complexity 2019 (April 1, 2019): 1–20. http://dx.doi.org/10.1155/2019/7819523.
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Service composition in a Cloud Manufacturing environment involves the adaptive and optimal assembly of manufacturing services to achieve quick responses to varied manufacturing needs. It is challenged by the inherent heterogeneity and complexity of these services in terms of their diverse and complex functions, qualities of service, execution paths, etc. In this paper, a manufacturing network is constructed to explicitly identify and describe the relationships between individual services based on their attributes. On this basis, the service composition problem can be modeled as a multiple-constrained optimal path (MCOP) selection problem by taking into account different types of composition, namely, sequence, parallel, selection, and cycle. A novel Dual Heuristic Functions based Optimal Service Composition Path algorithm (DHA_OSCP) is proposed to solve the NP-Complete MCOP problem, which involves exploiting the backward search procedure with different search targets to obtain two heuristic functions for the forward search procedure. The proposed algorithm is evaluated through a set of computational experiments in which the proposed algorithm and other popular algorithms such as MFPB_HOSTP are applied to the same dataset, and the results obtained show that DHA_OSCP can efficiently find the optimal service composition path with better Quality of Service (QoS). The viability of DHA_OSCP is further proved in a case study of services composition on a Cloud Manufacturing platform.
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Panyanak, Bancha, Chainarong Khunpanuk, Nattawut Pholasa, and Nuttapol Pakkaranang. "A novel class of forward-backward explicit iterative algorithms using inertial techniques to solve variational inequality problems with quasi-monotone operators." AIMS Mathematics 8, no. 4 (2023): 9692–715. http://dx.doi.org/10.3934/math.2023489.
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<abstract><p>The theory of variational inequalities is an important tool in physics, engineering, finance, and optimization theory. The projection algorithm and its variants are useful tools for determining the approximate solution to the variational inequality problem. This paper introduces three distinct extragradient algorithms for dealing with variational inequality problems involving quasi-monotone and semistrictly quasi-monotone operators in infinite-dimensional real Hilbert spaces. This problem is a general mathematical model that incorporates a set of applied mathematical models as an example, such as equilibrium models, optimization problems, fixed point problems, saddle point problems, and Nash equilibrium point problems. The proposed algorithms employ both fixed and variable stepsize rules that are iteratively transformed based on previous iterations. These algorithms are based on the fact that no prior knowledge of the Lipschitz constant or any line-search framework is required. To demonstrate the convergence of the proposed algorithms, some simple conditions are used. Numerous experiments have been conducted to highlight the numerical capabilities of algorithms.</p></abstract>
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Lin, Wei-Chen, Wei-Tzer Huang, Kai-Chao Yao, Hong-Ting Chen, and Chun-Chiang Ma. "Fault Location and Restoration of Microgrids via Particle Swarm Optimization." Applied Sciences 11, no. 15 (2021): 7036. http://dx.doi.org/10.3390/app11157036.
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This aim of this work was to develop an integrated fault location and restoration approach for microgrids (MGs). The work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of a fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations was utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve fault location and restoration problems in MGs.
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Prasetio, Rizki Tri. "Genetic Algorithm to Optimize k-Nearest Neighbor Parameter for Benchmarked Medical Datasets Classification." Jurnal Online Informatika 5, no. 2 (2020): 153. http://dx.doi.org/10.15575/join.v5i2.656.
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Computer assisted medical diagnosis is a major machine learning problem being researched recently. General classifiers learn from the data itself through training process, due to the inexperience of an expert in determining parameters. This research proposes a methodology based on machine learning paradigm. Integrates the search heuristic that is inspired by natural evolution called genetic algorithm with the simplest and the most used learning algorithm, k-nearest Neighbor. The genetic algorithm were used for feature selection and parameter optimization while k-nearest Neighbor were used as a classifier. The proposed method is experimented on five benchmarked medical datasets from University California Irvine Machine Learning Repository and compared with original k-NN and other feature selection algorithm i.e., forward selection, backward elimination and greedy feature selection. Experiment results show that the proposed method is able to achieve good performance with significant improvement with p value of t-Test is 0.0011.
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Duran-Quintero, Michel, John E. Candelo, and Jose Soto-Ortiz. "A modified backward/forward sweep-based method for reconfiguration of unbalanced distribution networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 85. http://dx.doi.org/10.11591/ijece.v9i1.pp85-101.
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<span lang="EN-US">A three-phase unbalanced power flow method can provide a more realistic scenario of how distribution networks operate. The backward/forward sweep-based power flow method </span><span lang="EN-AU">(BF-PF)</span><span lang="EN-US"> has been used for many years as an important computational tool to solve the power flow for unbalanced and radial power systems. However, some of the </span><span lang="EN-AU">few </span><span lang="EN-US">available research tools produce many errors when </span><span lang="EN-AU">they </span><span lang="EN-US">are used for </span><span lang="EN-AU">network </span><span lang="EN-US">reconfiguration </span><span lang="EN-AU">because the </span><span lang="EN-US">topology change</span><span lang="EN-AU">s</span><span lang="EN-AU">after multiple switch actions</span><span lang="EN-US"> and the nodes are disorganized continually. </span><span lang="EN-AU">T</span><span lang="EN-US">his paper presents </span><span lang="EN-AU">a modified</span><span lang="EN-AU">BF-PF for </span><span lang="EN-US">three-phase unbalanced radial </span><span lang="EN-AU">distribution networks</span><span lang="EN-US"> that is capable </span><span lang="EN-AU">of arranging</span><span lang="EN-US"> the system topology when reconfiguration changes the branch connections. A binary search is used to determine the connections between nodes, allowing the algorithm to avoid those problems when reconfiguration is carried out, regardless of node numbers. Tests are made to verify the usefulness of the proposed algorithm in both the IEEE 13-node test feeder and the 123-node test feeder, converging in every run where constraints are accomplished. This approach can be used easily for a large-scale feeder network reconfiguration.</span><span lang="EN-AU"> The full version of this modified </span><span lang="EN-US">backward/forward sweep</span><span lang="EN-AU"> algorithm is available for research at MathWorks</span><span lang="EN-US">.</span>
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Chen, Sian. "Research on quantitative measurement algorithm for e-commerce customer loyalty based on deep learning algorithm." Molecular & Cellular Biomechanics 21, no. 3 (2024): 562. http://dx.doi.org/10.62617/mcb562.
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Traditional algorithms cannot fully explore the potential patterns behind big data, lack personalized customer analysis, and cannot provide personalized services and suggestions for different types of customers. This article employs the Bi LSTM (Bidirectional Long Short-Term Memory) model to accurately capture the complex features and patterns of customer behavior, thereby improving the measurement accuracy of customer loyalty. Collect data on customer behavior, browsing history, and search behavior, and preprocess the collected data. Organize customer behavior data into a time series dataset in chronological order, and divide it into weekly windows to extract feature information from the data. Construct a bidirectional LSTM model while considering the forward and backward information of the sequence data, in order to more comprehensively capture the contextual relationships in the sequence data and quantify customer loyalty. The experimental results show that the average accuracy of Bi LSTM in predicting average customer loyalty is 97.1%. And it can effectively improve the prediction effect of repeat purchase rate. The application of Bi LSTM can accurately quantify customer loyalty in e-commerce, provide reference for enterprise decision-making, formulate corresponding marketing strategies and customer management plans, and improve customer loyalty and competitive advantages.
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Lin, Szu-Yin, Guan-Ting Lin, Kuo-Ming Chao, and Chi-Chun Lo. "A Cost-Effective Planning Graph Approach for Large-Scale Web Service Composition." Mathematical Problems in Engineering 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/783476.
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Web Service Composition (WSC) problems can be considered as a service matching problem, which means that the output parameters of a Web service can be used as inputs of another one. However, when a very large number of Web services are deployed in the environment, the service composition has become sophisticated and complicated process. In this study, we proposed a novel cost-effective Web service composition mechanism. It utilizes planning graph based on backward search algorithm to find multiple feasible solutions and recommends a best composition solution according to the lowest service cost. In other words, the proposed approach is a goal-driven mechanism, which can recommend the approximate solutions, but it consumes fewer amounts of Web services and less nested levels of composite service. Finally, we implement a simulation platform to validate the proposed cost-effective planning graph mechanism in large-scale Web services environment. The simulation results show that our proposed algorithm based on the backward planning graph has reduced by 94% service cost in three different environments of service composition that is compared with other existing service composition approaches which are based on a forward planning graph.
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Lasisi, H.O., T.O. Ajewole, O. Oladepo, and O.E. Olabode. "Optimization of Reactive Power Injection on Radial Distribution Network for Improved System Performance." Nigerian Research Journal of Engineering and Environmental Sciences 7, no. 1 (2022): 69–81. https://doi.org/10.5281/zenodo.6721014.
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<em>Distribution systems occupied a core position in the hierarchical structure of conventional power systems. However, several factors limit the expected efficiency of many practical distribution systems. It is, therefore, a thing of concern to power distribution engineers to seek better ways to manage both the amount of real power loss and deviation on bus voltage profile. On this premise, this paper presents the use of a shunt capacitor as a mitigating device. The initial state of the test case system was determined using the backward forward sweep (BFS) power flow technique. Cuckoo search algorithm (CSA) and voltage stability index (VSI) were employed to site and size the amount of reactive power injection. The proposed approach was tested on IEEE 33 bus system. Simulation results obtained were validated with other optimization algorithms. Results comparison showed that the proposed method outperformed binary particle swarm optimization (BPSO), real coded genetic algorithm (RCGA) and simulated annealing (SA). In a similar vein, system stability also improved as shown in the values obtained for VSI after integrating the shunt capacitor. This approach is therefore capable of strengthening the performance of the radial distribution system</em><em>.</em>
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Tulp, Eduard, and Laurent Siklóssy. "Searching time-table networks." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 5, no. 3 (1991): 189–98. http://dx.doi.org/10.1017/s0890060400002675.
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In this paper we present an application of AI search techniques to a class of problems that arise in transportation systems analysis. Rather than adapting the time-space network formulation typically used in Operations Research, we propose a discrete dynamic network to represent a scheduled service network. In a discrete dynamic network, there are finite, discrete, predetermined possibilities for moving from one vertex to another. Visiting a vertex has a cost (possibly zero), which may depend both on how the vertex was reached and how it will be left.We describe the DYNET search algorithm for finding optimal paths in discrete dynamic networks. DYNET has been implemented in a working system (TRAINS) which searches the entire Dutch railway services network. An optimal path in a discrete dynamic network makes us arrive at our destination as early as possible (given our planned earliest departure time), and given this earliest arrival time (eat), will allow us to leave as late as possible, thereby guaranteeing a shortest path relative to the eat. DYNET first conducts a forward search to find the earliest possible arrival time, then a backward search which uses results of the forward search, to find the latest departure to arrive at that eat. Various AI techniques (symmetries, abstraction spaces, distance estimates, etc.) improve the performance of DYNET.
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Martínez-Gil, John Fernando, Nicolas Alejandro Moyano-García, Oscar Danilo Montoya, and Jorge Alexander Alarcon-Villamil. "Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm." Computation 9, no. 7 (2021): 80. http://dx.doi.org/10.3390/computation9070080.
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In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., Δ- and Y-connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced power-flow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function.
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Xue, Huiting, Leilei Meng, Peng Duan, Biao Zhang, Wenqiang Zou, and Hongyan Sang. "Modeling and optimization of the hybrid flow shop scheduling problem with sequence-dependent setup times." International Journal of Industrial Engineering Computations 15, no. 2 (2024): 473–90. http://dx.doi.org/10.5267/j.ijiec.2024.1.001.
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The hybrid flow shop scheduling problem (HFSP) is an extension of the classic flow shop scheduling problem and widely exists in real industrial production systems. In real production, sequence-dependent setup times (SDST) are very important and cannot be neglected. Therefore, this study focuses HFSP with SDST (HFSP-SDST) to minimize the makespan. To solve this problem, a mixed-integer linear programming (MILP) model to obtain the optimal solutions for small-scale instances is proposed. Given the NP-hard characteristics of HFSP-SDST, an improved artificial bee colony (IABC) algorithm is developed to efficiently solve large-sized instances. In IABC, permutation encoding is used and a hybrid representation that combines forward decoding and backward decoding methods is designed. To search for the solution space that is not included in the encoding and decoding, a problem-specific local search strategy is developed to enlarge the solution space. Experiments are conducted to evaluate the effectiveness of the MILP model and IABC. The results indicate that the proposed MILP model can find the optimal solutions for small-scale instances. The proposed IABC performs much better than the existing algorithms and improves 61 current best solutions of benchmark instances.
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Teles, Weber de Santana, Aydano Pamponet Machado, Paulo Celso Curvelo Cantos Júnior, et al. "Machine learning and automatic selection of attributes for the identification of Chagas disease from clinical and sociodemographic data." Research, Society and Development 10, no. 4 (2021): e19310413879. http://dx.doi.org/10.33448/rsd-v10i4.13879.
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Objective: evaluate the potential use of machine learning and the automatic selection of attributes in discrimination of individuals with and without Chagas disease based on clinical and sociodemographic data. Method: After the evaluation of many learning algorithms, they have been chosen and the comparison between neural network Multilayer Perceptron (MLP) and the Linear Regression (LR) was done, seeking which one presents the best performance for prediction of the Chagas disease diagnosis, being used the criteria of sensitivity, specificity, accuracy and area under the ROC curve (AUC). Generated models were also compared, using the methods of automatic selection of attributes: Forward Selection, Backward Elimination and genetic algorithm. Results: The best results were achieved using the genetic algorithm and the MLP presented accuracy of 95.95%, 78.30% sensitivity, and specificity of 75.00% and AUC of 0.861. Conclusion: It was proved to be a very interesting performance, given the nature of the data used for sorting and use in public health, glimpsing its relevance in the medical field, enabling an approximation of prevalence that justifies the actions of active search of individuals Chagas disease patients for treatment and prevention.
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Du, Ruizhong, Yuqing Zhang, and Mingyue Li. "Database Padding for Dynamic Symmetric Searchable Encryption." Security and Communication Networks 2021 (December 31, 2021): 1–12. http://dx.doi.org/10.1155/2021/9703969.
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Dynamic symmetric searchable encryption (DSSE) that enables the search and update of encrypted databases outsourced to cloud servers has recently received widespread attention for leakage-abuse attacks against DSSE. In this paper, we propose a dynamic database padding method to mitigate the threat of data leakage during the update operation of outsourcing data. First, we introduce an outlier detection technology where bogus files are generated for padding according to the outlier factors, hiding the document information currently matching search keywords. Furthermore, we design a new index structure suitable for the padded database using the bitmap index to simplify the update operation of the encrypted index. Finally, we present an application scenario of the padding method and realize a forward and backward privacy DSSE scheme (named PDB-DSSE). The security analysis and simulation results show that our dynamic padding algorithm is suitable for DSSE scheme and PDB-DSSE scheme maintains the security and efficiency of the retrieval and update of the DSSE scheme.
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Kaliaperumal Rukmani, Devabalaji, Yuvaraj Thangaraj, Umashankar Subramaniam, et al. "A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks Using Bio-Inspired Cuckoo Search Algorithm." Energies 13, no. 18 (2020): 4615. http://dx.doi.org/10.3390/en13184615.
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This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM are calculated by using the Loss Sensitivity Factor (LSF). The optimal size of the DSTATCOM is simulated by using the newly developed CSA. In the proposed method, load flow calculations are performed by using a fast and efficient backward/forward sweep algorithm. Here, the mathematically formed objective function of the proposed method is to reduce the total system power losses. Standard 33-bus and 69-bus systems have been used to show the effectiveness of the proposed CSA-based optimization method in the RDS with different load models. The simulated results confirm that the optimal allocation of DSTATCOM plays a significant role in power loss minimization and enhanced voltage profile. The placement of DSTATCOM in RDS also plan an important role for minimizing uncertainties in the distribution level. The proposed method encourages one to use renewable-based resources, which results in affordable and clean energy.
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NAVEED, Nawazish, Hayan T. MADHLOOM, and Mohd Shahid HUSAIN. "BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK." Applied Computer Science 17, no. 3 (2021): 19–30. http://dx.doi.org/10.35784/acs-2021-18.
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Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. While accuracy recorded with the six SBS selected features was 98.54%. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model.
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Lavine, Barry K., Collin G. White, Matthew D. Allen, and Andrew Weakley. "Pattern Recognition-Assisted Infrared Library Searching of the Paint Data Query Database to Enhance Lead Information from Automotive Paint Trace Evidence." Applied Spectroscopy 71, no. 3 (2016): 480–95. http://dx.doi.org/10.1177/0003702816666287.
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Multilayered automotive paint fragments, which are one of the most complex materials encountered in the forensic science laboratory, provide crucial links in criminal investigations and prosecutions. To determine the origin of these paint fragments, forensic automotive paint examiners have turned to the paint data query (PDQ) database, which allows the forensic examiner to compare the layer sequence and color, texture, and composition of the sample to paint systems of the original equipment manufacturer (OEM). However, modern automotive paints have a thin color coat and this layer on a microscopic fragment is often too thin to obtain accurate chemical and topcoat color information. A search engine has been developed for the infrared (IR) spectral libraries of the PDQ database in an effort to improve discrimination capability and permit quantification of discrimination power for OEM automotive paint comparisons. The similarity of IR spectra of the corresponding layers of various records for original finishes in the PDQ database often results in poor discrimination using commercial library search algorithms. A pattern recognition approach employing pre-filters and a cross-correlation library search algorithm that performs both a forward and backward search has been used to significantly improve the discrimination of IR spectra in the PDQ database and thus improve the accuracy of the search. This improvement permits inter-comparison of OEM automotive paint layer systems using the IR spectra alone. Such information can serve to quantify the discrimination power of the original automotive paint encountered in casework and further efforts to succinctly communicate trace evidence to the courts.
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Krishnan, V. Gokula, D. Siva, S. Hemamalini, N. Sivakumar, and V. Vijayaraja. "Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 57–67. http://dx.doi.org/10.17762/ijritcc.v11i7.7830.
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Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial for farmers to implement effective and technical solutions to raise production, lessen the impact of issues associated to animal husbandry, and improve agricultural yields. Because of technological developments in computers and data storage, huge volumes of information are now available. The difficulty of extracting useful information from this mountain of data has prompted the development of novel approaches and tools, such as data mining, that can help close the informational gap. To evaluate data mining methods and put them to use in the Animal database to create meaningful connections was the goal of the suggested system. The study's primary objective was to develop an IoT-based Integrated Animal Health Care System. Various sensors were used as the research tool to collect physical and environmental data on the animals and their habitats. Temperature, heart rate, and air quality readings were the types of information collected. This research contributes to the field of health monitoring by introducing an Optimised Bidirectional Gated Recurrent Neural Network approach. The BiGRNN is an improved form of the Gated Recurrent Unit (GRU) in which input is sent both forward and backward through a network and the resulting outputs are connected to the same output layer. Since the BiGRNN method employs a number of hyper-parameters, it is optimised by means of the Sparrow Search Algorithm (SSA). The originality of the study is demonstrated by the development of an SSA technique for hyperparameter optimisation of the BiGRNN, with a focus on health forecasting. Hyperparameters like momentum, learning rate, and weight decay may all be adjusted with the SSA method. In conclusion, the results demonstrate that the suggested tactic is more effective than the current methods.
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Cortés-Caicedo, Brandon, Federico Molina-Martin, Luis Fernando Grisales-Noreña, Oscar Danilo Montoya, and Jesus C. Hernández. "Optimal Design of PV Systems in Electrical Distribution Networks by Minimizing the Annual Equivalent Operative Costs through the Discrete-Continuous Vortex Search Algorithm." Sensors 22, no. 3 (2022): 851. http://dx.doi.org/10.3390/s22030851.
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This paper discusses the minimization of the total annual operative cost for a planning period of 20 years composed by the annualized costs of the energy purchasing at the substation bus summed with the annualized investment costs in photovoltaic (PV) sources, including their maintenance costs in distribution networks based on their optimal siting and sizing. This problem is presented using a mixed-integer nonlinear programming model, which is resolved by applying a master–slave methodology. The master stage, consisting of a discrete-continuous version of the Vortex Search Algorithm (DCVSA), is responsible for providing the optimal locations and sizes for the PV sources—whereas the slave stage employs the Matricial Backward/Forward Power Flow Method, which is used to determine the fitness function value for each individual provided by the master stage. Numerical results in the IEEE 33- and 69-node systems with AC and DC topologies illustrate the efficiency of the proposed approach when compared to the discrete-continuous version of the Chu and Beasley genetic algorithm with the optimal location of three PV sources. All the numerical validations were carried out in the MATLAB programming environment.
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Piao, Zai Lin, Lu Wang, Juan Wang, and Yong Xiang Wang. "The Research of the Real-Time Performance of Power Flow Visualization Based on Digital Power Distribution Network." Advanced Materials Research 791-793 (September 2013): 1884–88. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1884.
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Based on the study of 10 kV digital power distribution network (DPDN) power flow visualization, the paper uses hierarchical backward and forward sweep algorithm (HBAFSA) to significantly shorten the calculation time and improve the real-time performance of power flow, which analysis and layers the distribution network topology according to the breadth-first search and then calculate in parallel between the same level. Programming with Visual C# in Visual Studio 2010 environment gets the GIS data library to actively obtain real-time operating data of distribution network from the SCADA data library, and calculates rapidly with the HBAFSA, and transfers the real-time power flow data to the GIS data library. On the platform of the GIS, clicking nodes and branches can get the visualized real-time power flow, which indicates the achievement of the real-time power flow of the DPDN visualization on the platform of the GIS.
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Cruz-Reyes, Jose Luis, Sergio Steven Salcedo-Marcelo, and Oscar Danilo Montoya. "Application of the Hurricane-Based Optimization Algorithm to the Phase-Balancing Problem in Three-Phase Asymmetric Networks." Computers 11, no. 3 (2022): 43. http://dx.doi.org/10.3390/computers11030043.
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This article addresses the problem of optimal phase-swapping in asymmetric distribution grids through the application of hurricane-based optimization algorithm (HOA). The exact mixed-integer nonlinear programming (MINLP) model is solved by using a master–slave optimization procedure. The master stage is entrusted with the definition of load connection at each stage by using an integer codification that ensures that, per node, only one from the possible six-load connections is assigned. In the slave stage, the load connection set provided by the master stage is applied with the backward/forward power flow method in its matricial form to determine the amount of grid power losses. The computational performance of the HOA was tested in three literature test feeders composed of 8, 25, and 37 nodes. Numerical results show the effectiveness of the proposed master–slave optimization approach when compared with the classical Chu and Beasley genetic algorithm (CBGA) and the discrete vortex search algorithm (DVSA). The reductions reached with HOA were 24.34%, 4.16%, and 19.25% for the 8-, 28-, and 37-bus systems; this confirms the literature reports in the first two test feeders and improves the best current solution of the IEEE 37-bus grid. All simulations are carried out in the MATLAB programming environment.
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R, Nandhini, and Evangelin Sonia S.V. "An Efficient Mining Approach for Handling Web Access Sequences." International Journal of Computer Communication and Informatics 3, no. 1 (2021): 15–25. http://dx.doi.org/10.34256/ijcci2112.
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The World Wide Web (WWW) becomes an important source for collecting, storing, and sharing the information. Based on the users query the traditional web page search approximately retrieves the related link and some of the search engines are Alta, Vista, Google, etc. The process of web mining defines to determine the unknown and useful information from web data. Web mining contains the two approaches such as data-based approach and process-based approach. Now a day the data-based approach is the widely used approach. It is used to extract the knowledge from web data in the form of hyper link, and web log data. In this study, the modern technique is presented for mining web access utility-based tree construction under Modified Genetic Algorithm (MGA). MGA tree are newly created to deploy the tree construction. In the web access sequences tree construction for the most part relies upon internal and external utility values. The performance of the proposed technique provides an efficient Web access sequences for both static and incremental data. Furthermore, this research work is helpful for both forward references and backward references of web access sequences.
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Kang, Ti, Huaqing Li, and Lifeng Zheng. "Aggregative Game for Distributed Charging Strategy of PEVs in a Smart Charging Station." Axioms 12, no. 2 (2023): 186. http://dx.doi.org/10.3390/axioms12020186.
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This paper proposes a charging strategy for plug-in electric vehicles (PEVs) in a smart charging station (SCS) that considers load constraints and time anxieties. Due to the rapidly growing load demand of PEVs and the load capacity investments in infrastructure, PEV charging needs to be subject to overload limits, beyond which failures can occur. The time anxiety is presented to address some of the uncertainties that may arise while charging PEVs. Under an aggregative game framework, this paper constructs a price-driven charging model to minimize costs by choosing the optimal charging strategy. Meanwhile, since the driver information is an aggregated item in the PEV cost function, the drivers’ privacy can be protected. Then, a distributed reflected forward–backward (RFB) splitting method is developed to search for the generalized Nash equilibria (GNE) of the game. The convergence of the proposed algorithm and the effectiveness of the charging strategy are verified by the detailed simulation and results.
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Montoya, Oscar Danilo, Luis Fernando Grisales-Noreña, and Carlos Andres Ramos-Paja. "Optimal Allocation and Sizing of PV Generation Units in Distribution Networks via the Generalized Normal Distribution Optimization Approach." Computers 11, no. 4 (2022): 53. http://dx.doi.org/10.3390/computers11040053.
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The problem of optimal siting and dimensioning of photovoltaic (PV) generators in medium-voltage distribution networks is addressed in this research from the perspective of combinatorial optimization. The exact mixed-integer programming (MINLP) model is solved using a master–slave (MS) optimization approach. In the master stage, the generalized normal distribution optimization (GNDO) with a discrete–continuous codification is used to represent the locations and sizes of the PV generators. In the slave stage, the generalization of the backward/forward power method, known as the successive approximation power flow method, is adopted. Numerical simulations in the IEEE 33-bus and 69-bus systems demonstrated that the GNDO approach is the most efficient method for solving the exact MINLP model, as it obtained better results than the genetic algorithm, vortex-search algorithm, Newton-metaheuristic optimizer, and exact solution using the General Algebraic Modeling System (GAMS) software with the BONMIN solver. Simulations showed that, on average, the proposed MS optimizer reduced the total annual operative costs by approximately 27% for both test feeders when compared with the reference case. In addition, variations in renewable generation availability showed that from 30% ahead, positive reductions with respect to the reference case were obtained.
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Wang, Na, Jian Jiao, Shancheng Zhang, and Junsong Fu. "Secure and Efficient Semantic Extension Search over Encrypted Documents by Integrating Cloud and Fog Systems." Mathematical Problems in Engineering 2022 (July 9, 2022): 1–15. http://dx.doi.org/10.1155/2022/9349651.
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With the explosive increase of document files, more and more data owners outsource their documents to the public cloud, which can decrease the costs of local data management systems. However, the problem of information privacy leakage in the cloud is a great challenge and it has been attracting more and more attention. In this article, we propose a secure and efficient document search scheme, named SES, based on both the cloud and fog systems. All the documents are symmetrically encrypted before being outsourced to the cloud, and an index vector is constructed based on the keywords for each document. Specifically, we integrate the position information of keywords into the TF-IDF model to generate document vectors, which are accurate and inherent summarizations about the documents. In query requests, a data user needs to provide a set of keywords, which are first extended by the Word2Vec tool and then mapped to a query vector. The extension process of keywords makes the provided keywords more comprehensive and accurate, and hence, it improves document search accuracy. To achieve the forward and backward security, both the document vectors and query vectors are appended with an ingenious vector. The relevance score between a document and a query is defined as the inner product of the document vector and the query vector. We return the most k relevant documents as the search results to the data users. To protect the contextual information stored in the document and query vectors, we encrypt the vectors by the secure kNN algorithm. To improve the search efficiency, a searchable index structure for the document set is constructed based on the Diffie–Hellman secret key negotiation algorithm. The analysis and simulation results illustrate that the proposed scheme performs well in terms of both security and search efficiency.
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Mishra, Ashish, and Suresh Jagannathan. "Specification-guided component-based synthesis from effectful libraries." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (2022): 616–45. http://dx.doi.org/10.1145/3563310.
Full textAbstract:
Component-based synthesis seeks to build programs using the APIs provided by a set of libraries. Oftentimes, these APIs have effects, which make it challenging to reason about the correctness of potential synthesis candidates. This is because changes to global state made by effectful library procedures affect how they may be composed together, yielding an intractably large search space that can confound typical enumerative synthesis techniques. If the nature of these effects are exposed as part of their specification, however, deductive synthesis approaches can be used to help guide the search for components. In this paper, we present a new specification-guided synthesis procedure that uses Hoare-style pre- and post-conditions to express fine-grained effects of potential library component candidates to drive a bi-directional synthesis search strategy. The procedure alternates between a forward search process that seeks to build larger terms given an existing context but which is otherwise unaware of the actual goal, alongside a backward search mechanism that seeks terms consistent with the desired goal but which is otherwise unaware of the context from which these terms must be synthesized. To further improve efficiency and scalability, we integrate a conflict-driven learning procedure into the synthesis algorithm that provides a semantic characterization of previously encountered unsuccessful search paths that is used to prune the space of possible candidates as synthesis proceeds. We have implemented our ideas in a tool called and demonstrate its effectiveness on a number of challenging synthesis problems defined over OCaml libraries equipped with effectful specifications.