Self-adapted task allocation algorithm with complicated coalition in wireless sensor network by Wen-zhong GUO, Jin-shu SU, Cheng-yu CHEN, Guo-long CHEN

“…Considering the real-time requirement and some specific limitations (e.g.insufficient computing resource,energy constraint,etc) in task scheduling of wireless sensor networks,different priorities were assigned to tasks according to their deadline,and an adaptive task allocation algorithm with complicated coalition was designed through analyzing historical information.Moreover,a discrete particle swarm optimization algorithm was designed via employing binary matrix coding form.The proposed optimization algorithm generates coalitions in parallel and then performs subtask allocation algorithm based on load and energy balance.Finally,the experimental results show that the proposed algorithm strikes a good balance between local solution and global exploration,and achieves a satisfactory result within a short period of time.…”
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Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm by Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra

“…Therefore, to automate this process, weighted cognitive avoidance particle swarm optimization (WCAPSO) is employed for hyperparameter optimization. …”
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Multi-Criteria Optimization of a Hybrid Renewable Energy System Using Particle Swarm Optimization for Optimal Sizing and Performance Evaluation by Shree Om Bade, Olusegun Stanley Tomomewo, Ajan Meenakshisundaram, Maharshi Dey, Moones Alamooti, Nabil Halwany

“…The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization framework to design an HRES in Kern County, USA. …”
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Image cluster algorithm of hybrid encoding method by Chun-hui ZHAO, Xue-yuan LI, Ying CUI

Subjects: “…image cluster analysis;hybrid encoding;rain forest algorithm;quantum particle swarm optimization…”
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Improved Tuna Swarm Optimization (ITSO) Algorithm based on Adaptive Fitness-Weight for Global Optimization by A. F. Seini Yussif, T. Seini, C. Adu

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Application of the metaheuristic algorithms to quantify the GSI based on the RMR classification by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…This study addresses this challenge by analyzing data from fourteen different rock types and employing three metaheuristic optimization algorithms, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimization (GWO), to develop predictive models for quantifying GSI based on the RMR. …”
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Review of Software Tools for Working with Evolutionary and Swarm Optimization Methods by Aleksei Nikolashkin, Nikolay Ershov

“…The article is devoted to a review of software tools that allow applying, developing and investigating evolutionary and swarm optimization methods for solving complex discrete and continuous optimization problems. …”
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Hybridization of Swarm for Features Selection to Modeling Heart Attack Data by Omar Shakir, Ibrahim Saleh

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Cervical Cancer Detection Using Deep Neural Network and Hybrid Waterwheel Plant Optimization Algorithm by Sarah A. Alzakari, Amel Ali Alhussan, S.K. Towfek, Marwa Metwally, Dina Ahmed Salem

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Optimization of multi effect evaporation systems using a metaheuristic hybrid algorithm by Orhun Uzdiyem, Yavuz Özçelik

“…C# programming language is used in the development of the computer program. A Particle Swarm Optimization (PSO) based algorithm is developed and hybridized with a Levenberg-Marquardt (LM) based algorithm. …”
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Particle Swarm Optimization Support Vector Machine-Based Grounding Fault Detection Method in Distribution Network by Zhongqin Xiong, Shichang Huang, Shen Ren, Yutong Lin, Zewen Li, Dongyu Li, Fangming Deng

“…With the present fault detection method for low-voltage distribution networks, it is difficult to detect single-phase grounding faults under complex working conditions. In this paper, a particle swarm optimization (PSO) support vector machine (SVM)-based grounding fault detection method is proposed for distribution networks. …”
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Optimization model of electricity metering management based on MOPSO by Sheng Li, Xiaodan Zhou

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Path Planning Optimization of Smart Vehicle With Fast Converging Distance-Dependent PSO Algorithm by Muhammad Haris, Haewoon Nam

“…Path planning is a crucial technology and challenge in various fields, including robotics, autonomous systems, and intelligent transportation systems. The Particle Swarm Optimization (PSO) algorithm is widely used for optimization problems due to its simplicity and efficiency. …”
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Memetic Salp Swarm Algorithm for economic load dispatch problems by Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Malik Braik, Raed Abu Zitar, Khaled Assaleh, Mahmud Alkoffash, Qusai Yousef Shambour

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3D Measurement of Particle Movement in a Silo Using Magnetic Positioning and Inertial Navigation Technologies by Hongyang Dai, Yiming Li, Shijie Wang, Ran Li, Hui Yang

Subjects: “…Particle swarm optimization algorithm…”
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A new localization method based on improved particle swarm optimization for wireless sensor networks by Qiaohe Yang

“…However, the particle swarm diversity of the PSO algorithm is easy to lose quickly and fall into local optimal solution in the iterative process. …”
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Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization by Ying Zhang, Yunlong Qiao, Wei Zhao, Wei Chen, Jinde Cao

“…The proposed deployment algorithm PFPSO (Potential Field-Directed Particle Swarm Optimization) can overcome this problem and guide the mobile nodes to the optimal positions. …”
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Comprehensive Evaluation Method of Teaching Effect Based on Particle Swarm Optimization Neural Network Model by Heng Cao, Qianhui Gao

“…In view of these characteristics, this paper has conducted in-depth research to fully prove the feasibility and superiority of the content of this article. The specific summary is as follows: (1) Introduced the design concept of particle swarm optimization teaching evaluation system. (2) The use of object-oriented programming algorithms makes it easier for the algorithm to find an entry point, solve practical problems, and optimize the reusability of the algorithm method. (3) Particle swarm optimization based on quantum behavior, adjusting parameter values, the highest and the lowest, greatly reduces the difficulty of program parameter adjustment. (4) In terms of operation, it can quickly and efficiently complete the maintenance of teacher teaching information, evaluation relationship management of teacher teaching quality evaluation, evaluation content management, student evaluation, supervision evaluation, college leadership evaluation, evaluation performance management, and other operations. …”
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Minimum energy consumption multicast routing in ad hoc networks based on particle swarm optimization by Xiao-jian ZHU, Jun SHEN

“…In wireless ad hoc networks,because devices are powered by batteries,and multicast applications are constantly emerging,how to construct a multicast tree of the minimum energy consumption is an important problem.For the effect of the different choices of relay nodes to the construction of the minimum energy consumption multicast tree,a discrete particle swarm optimization algorithm to optimize the construction of the minimum energy consumption multicast tree was proposed.In order to avoid the premature convergence of the discrete particle swarm optimization algorithm,an inertia weight strategy was introduced to balance the global searchin ability and the local searching ability.The results of simulated experiments show that the modified discrete particle swarm optimization algorithm has strong optimization ability,and can effectively optimize the construction of the minimum energy consumption multicast tree.…”
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Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization by Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu

“…To address this problem, we propose a novel algorithm by combining particle swarm optimization and semi-supervised extreme learning machine to automatically select the optimal hyper parameters of semi-supervised extreme learning machine in this article. …”
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