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Research on the application of search algorithm in computer communication network

JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 1150-1159页

作者： Ai, Hua Chai, Jianwei Zhang, Jilei Khanna, Shaweta Ghafoor, Kayhan Zrar Langfang Yanjing Vocat & Tech Coll Dept Comp Engn Langfang 065000 Hebei Peoples R China ITS Engn Coll Greater Noida UP India Knowledge Univ Dept Comp Sci Erbil 44001 Iraq

This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation (CFA) problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks.

关键词： search algorithm harmony search algorithm communication network network optimization

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search algorithm, repetitive information, and sales on online platforms

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INTERNATIONAL JOURNAL OF INDUSTRIAL ORGANIZATION 2023年第1期88卷

作者： Huang, Yangguang Xie, Yu Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China

A prominent feature of online sales is that buyers rely on the search tools offered by plat-forms to process information when searching for products. We develop a model that cap-tures how the search algorithm affects buyers' search processes, which further influences market equilibrium and welfare. If a platform adopts a highly unequal search algorithm, buyers are likely to obtain repetitive information about a small group of sellers, which causes buyers to consider fewer options and suppresses competition. By using data from food delivery platforms, we provide empirical evidence that markets with less equal dis-tributions of store rankings in search results have higher average prices and more con-centrated sales. We suggest that regulators should restrict search algorithms from showing repetitive information.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Online platform search algorithm Repetitive information Consideration set Food delivery platform

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Baseline Correction Based on a search algorithm from Artificial Intelligence

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APPLIED SPECTROSCOPY 2021年第5期75卷 531-544页

作者： Wang, Xin Chen, Xia Beijing Univ Technol Coll Mech Engn & Appl Elect Technol Beijing Peoples R China Beijing Univ Technol Coll Environm & Energy Engn Key Lab Enhanced Heat Transfer & Energy Conservat Minist Educ Beijing Peoples R China Beijing Univ Technol Coll Environm & Energy Engn Key Lab Heat Transfer & Energy Convers Beijing Peoples R China

Many spectra have a polynomial-like baseline. Iterative polynomial fitting is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by iterative polynomial fitting may have a substantial error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, iterative polynomial fitting uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have a significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods, the Lieber and Mahadevan-Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real Fourier transform infrared and Raman spectra with polynomia

关键词： Baseline correction polynomial fitting search algorithm artificial intelligence

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Oppositional Crow search algorithm with mutation operator for global optimization and application in designing FOPID controller

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EVOLVING SYSTEMS 2021年第2期12卷 463-488页

作者： Majhi, Santosh Kumar Sahoo, Madhusmita Pradhan, Rosy Veer Surendra Sai Univ Technol Dept Comp Sci & Engn Burla 768018 Odisha India Veer Surendra Sai Univ Technol Dept Elect Engn Burla 768018 Odisha India

The Swarm based algorithms deliver better performance when it can maintain perfect balance of the exploration and exploitation process and converges faster by successfully avoiding local optima entrapment. At recent time, Crow search algorithm (CSA) is developed as a nature inspired swarm based algorithm. It can solve continuous, non-linear and complex day to day life optimization problems. Like many other optimization algorithms, CSA suffers with the problem of local stagnation. This paper introduces an improved version of the CSA, which improves the performance of the existing CSA algorithm by using oppositional learning and mutation operator. The proposed algorithm is termed as OBL-CSA-MO. The OBL-CSA-MO enhances the exploration and exploitation capability in the search space and successfully avoids local optima entrapment. The OBL-CSA-MO is evaluated by considering the IEEE CEC 2017 standard benchmark functions set. The efficiency and robustness of the proposed OBL-CSA-MO is measured using performance metrics, convergence analysis and statistical significance. A demonstration is given as an application of the proposed algorithm for designing fractional order PID controller as the real life problem. For this purpose, the first order and higher order plants are considered in the FOPID designing using the proposed OBL-CSA-MO. The experimental results demonstrate that the developed OBL-CSA-MO can be used for solving optimization problems effectively.

关键词： Crow search algorithm Opposition based learning Mutation operator Optimization FOPID controller

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A Collaborative Beetle Antennae search algorithm Using Memory Based Adaptive Learning

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APPLIED ARTIFICIAL INTELLIGENCE 2021年第6期35卷 440-475页

作者： Ghosh, Tamal Martinsen, Kristian Norwegian Univ Sci & Technol Dept Mfg & Civil Engn Teknologivegen 22 N-2815 Gjovik Norway

Recently developed Beetle Antennae search algorithm (BAS) mimics the odor sensing mechanism of the longhorn beetles. The beetles have many species and many of these are advantageous to the nature as well as the mankind. Excepting the odor sensing activity, the beetles are naturally strong insects, and some of them have storage memory for adaptive learning and showcase social behavior. These natural mechanisms make them intelligent enough to perform the routine tasks for existence. This article proposes a novel Storage (Memory) Adaptive Collaborative BAS (SACBAS) algorithm, which incorporates the memory stored adaptive learning. This helps exploit the Group Extreme Value (GEV) instead of the Individual Extreme Values in swarm for faster convergence. Further, the SACBAS uses the reference points based on non-dominated sorting to diversify the state space. To test the data-driven performance of SACBAS, the Support Vector Machine (SVM) algorithm with linear kernel is used in this study. First, the SACBAS algorithm is tested on the multi-objective ZDT and DTLZ test-suites and compared with two recent techniques, the reference points based Non-dominated Sorting Genetic algorithm (NSGA III) and Multi-Objective Evolutionary algorithm based on Decomposition (MOEA/D). Second, the data-driven SACBAS is tested with real-world cases based on offline data. The proposed SACBAS is shown to handle the offline data efficiently and obtains promising results. The Friedman Test is carried out to differentiate the SACBAS from other two techniques and the Post Hoc Test confirms that the SACBAS obtains better HyperVolume indicator scores and outperforms the NSGA III and MOEA/D.

关键词： Adaptive learning Support-vector machine search algorithm Beetle Genetic algorithm Evolutionary algorithm

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A Network Security Transmission Model Based on Improved Sparrow search algorithm

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY 2024年第1期18卷 1页

作者： Tuxun, Alimire Maimaiti Guo, Qingrui Tuxun State Grid Kashi Elect Power Supply Co Kashgar Peoples R China State Grid Xinjiang Elect Power Res Inst Urumqi 830000 Xinjiang Peoples R China

With the development of the socio-economic landscape, the demand for electricity supply is steadily increasing. The application of 5G networks in this field has gradually alleviated the supply-demand response issues associated with large-scale distributed power sources. However, the broadcast nature of 5G channels raises concerns about secure data transmission. Addressing these challenges, this paper establishes a collaborative communication model for large-scale Demand Response (DR). Using the distributed power source network as a foundation, a communication system based on Power Line Communication (PLC) and Generic State Machine (GenSM) is proposed. Building on this, a self-adaptive data transmission algorithm based on the sparrow algorithm is proposed. This algorithm uses swarm intelligence for efficient energy management and secure transmission. Simulation results show that, compared to traditional Multiple-Input Multiple-Output (MIMO) models, under specific conditions, the proposed model achieves an energy efficiency improvement of 131% and 69%.

关键词： Distributed Energy Resources Demand Response Power Line Communication Generic State Machine Sparrow search algorithm

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Dependency Matrix-Based Fast Fault Diagnosis Using search algorithm

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Cui, Weiwei Li, Lianfeng Li, Hao Guan, Fei Hong, Dongpao Liu, Guanjun Natl Univ Def Technol Sch Intelligent Sci Changsha 410073 Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

The dependency matrix is widely used in fault diagnosis as a valid diagnostic model. However, the conventional dependency matrix-based diagnosis method pays more attention to the extension and improvement of the dependency matrix, and the timeliness is rarely discussed. To solve the problem and improve the diagnosis efficiency, fast diagnosis method using two search algorithms is proposed. The hash function is introduced, and the dependency matrix is transformed into the key-value pairs. The binary search algorithm is brought in to locate the desired failure mode by splitting the dependency matrix. Thus, the fault diagnosis is converted into a search process, which can improve the diagnosis efficiency by making full use of the advantages of the search algorithms. Finally, a numerical simulation is utilized to verify the proposed method. The proposed methods are applied to the matrices of different sizes. The results show that the hash-based diagnosis method has significantly better efficiency, making it more suitable for onboard diagnosis. On the other hand, the binary search-based method is advantageous from a memory perspective only when the matrix size is small.

关键词： Dependency matrix fault diagnosis search algorithm testability

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How can a hybrid quantum-inspired gravitational search algorithm decrease energy consumption in IoT-based software-defined networks?

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2023年 40卷

作者： Tong, Lian Yang, Lan Zhao, Xin Liu, Li Changsha Univ Dept Comp Sci & Engn Changsha 410022 Hunan Peoples R China

The growth of Internet of Things (IoT) devices has prompted the growing use of software-defined networks (SDNs) in today's quickly changing technological environment. In SDN, execution and security of supporting applications and creating an adaptable network design allow the network to associate with applications legitimately. As a result, SDN promotes the growth of IoT-enabled devices, boosts network resource-sharing effectiveness, and boosts the reliability of IoT services. While these interconnected systems offer unprecedented convenience and efficiency, they also come with an increasing energy consumption challenge. The original features of these networks, such as the dynamic topology and energy constraints, challenge the routing issue in these networks. This article delves into the strategies and innovations that can effectively decrease energy consumption in IoT-based SDNs. The previous methods had some problems, such as increasing energy consumption, delay and network lifetime, etc. Thus, fuzzy and meta-heuristic methods have been used to maximize the search space and achieve optimum results. Due to the NP-hard nature of this issue, the Binary QuantumInspired Gravitational search algorithm (BQIGSA) is used in this paper to offer a fuzzy-based routing approach in IoT-based SDN, which aims to optimize energy, delay, and expected transmission rate. Fuzzy modeling, and particularly fuzzy routing algorithms, are explained in this study in relation to the decisionmaking component. The synergy of Fuzzy Logic and BQIGSA offers a promising avenue for enhancing IoTbased SDNs. This innovative approach tackles the challenges of uncertainty, energy optimization, and adaptive decision-making that are inherent in IoT networks. The simulation is performed through MATLAB. The outcomes of simulations and tests demonstrated that the suggested approach performed better than the current methods in terms of energy usage, delay rate, and data delivery rate.

关键词： IoT-based SDNs Routing Energy search algorithm Fuzzy logic

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Biomedical Document Clustering Based on Accelerated Symbiotic Organisms search algorithm

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE REsearch 2021年第4期12卷 169-185页

作者： Boushaki, Saida Ishak Bendjeghaba, Omar Kamel, Nadjet Univ MHammed Bouguera Boumerdes Comp Sci Dept Boumerdes Algeria Univ Mhammed Bouguera Boumerdes LREEI Boumerdes Algeria Ferhat Abbas Univ Setif 1 Comp Sci Dept LRSD Setif Algeria

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

关键词： Biomedical Documents Clustering Latent Semantic Indexing Metaheuristic Optimization Symbiotic Organisms search algorithm Vector Space Model

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Circuit implementation and analysis of a quantum-walk based search complement algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-26页

作者： Wing-Bocanegra, Allan Quintero-Narvaez, Carlos E. Venegas-Andraca, Salvador E. Tecnol Monterrey Escuela Ingn & Ciencias Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico

We propose a modified version of the quantum walk-based search algorithm created by Shenvi, Kempe and Whaley, also known as the SKW algorithm. In our version of the algorithm, we modified the evolution operator of the system so that it is composed by the product of the shift operator associated to the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2<^>n$$\end{document}-complete graph with self-loops and a perturbed coin operator based on the Hadamard operator that works as an oracle for the search. The modified evolution operator leads the opposite behavior as in the original algorithm, that is, the probability to measure the target state is reduced. We call this new behavior the search complement. Furthermore, we extend the functioning of the algorithm so that it targets at multiple nodes at the same time, which makes it suitable to be used as an initialization routine for the QAOA algorithm to solve restricted QUBO problems. Taking a multigraph and matrix approach, we were able to explain that the new algorithm decreases the probability of the target states given that there are less paths that lead towards the nodes that are associated to the target states in a Unitary Coined Discrete-Time Quantum Walk. The search complement algorithm was executed experimentally on IBM quantum processor ibmq_manila obtaining statistical distances \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1\le 0.0895$$\end{document} when decreasing the probability of one state out of four.

关键词： Quantum walks Quantum circuits search algorithm search complement Unstructured database IBM quantum Multigraph

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