Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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An Adaptive Layering Dual-Parameter Regularization Inversion Method for an Improved Giant Trevally Optimizer Algorithm by Chao Tan, Menghao Sun, Wei Liu, Wenrui Tan, Xiaoling Zhang, Chengang Zhu, Da Li

“…Subsequently, the current model parameters of the inversion objective function are optimized using the Giant Trevally Optimizer (GTO) algorithm, improved by the Particle Swarm Optimization (PSO) algorithm. …”
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Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models by Sadeq Mohammed Kadhm Sarkhi, Hakan Koyuncu

“…One of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like “PacMan”. …”
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Optimization method for cloud manufacturing service composition based on the improved artificial bee colony algorithm by Qiang HU, Yuqing TIAN, Haoquan QI, Peng WU, Qingxue LIU

“…To improve the optimization quality, efficiency and stability of cloud manufacturing service composition, a optimization method for cloud manufacturing service composition based on improved artificial bee colony algorithm was proposed.Firstly, three methods of service collaboration quality calculation under cloud manufacturing service composition scenario were put forward.Then, the optimization model with service collaboration quality was constructed.Finally, an artificial bee colony algorithm with multi-search strategy island model was designed to solve the optimal cloud manufacturing service composition.The experimental results show that the proposed algorithm is superior to the current popular improved artificial bee colony algorithms and other swarm intelligence algorithms in terms of optimization quality, efficiency and stability.…”
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Multi-objective optimization analysis of construction management site layout based on improved genetic algorithm by Hui Yin

“…In construction management, the rationality of on-site layout is crucial for project progress, cost, and safety. In order to improve the rationality of on-site layout, a multi-objective optimization model combining ant colony algorithm and Pareto optimal solution was constructed based on genetic algorithm, and this model was applied to practical engineering cases. …”
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Omni-Channel Product Distribution Network Design by Using the Improved Particle Swarm Optimization Algorithm by Si Zhang, Heying Zhu, Xia Li, Yan Wang

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Improved Snake Optimization and Particle Swarm Fusion Algorithm Based on AUV Global Path Planning by Haobo Jiang, Xinghong Kuang

“…An improved snake optimization algorithm (ISO) is proposed to obtain an effective and reliable three-dimensional path for an autonomous underwater vehicle (AUV) to navigate seabed barrier environments and ocean currents. …”
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Hybrid stochastic and robust optimization of a hybrid system with fuel cell for building electrification using an improved arithmetic optimization algorithm by Fude Duan, Mahdiyeh Eslami, Mustafa Okati, Dheyaa J. Jasim, Arsalan Khadim Mahmood

“…The study utilizes an improved arithmetic optimization algorithm (IAOA) to optimize component sizes and MRUs, incorporating a neighborhood search operator to enhance performance and prevent premature convergence. …”
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Enhanced Butterfly Optimization and Deep Learning Algorithm for Student Placement Prediction by T. Kavi Priya, N. Kumar

“…It is done by generating the optimal Fitness Values (FV). At last, the DL algorithm Improved Long Short-Term Memory (ILSTM) is used for predicting student placement and the results are superior. …”
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An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning by Xue Wang, Shiyuan Zhou, Zijia Wang, Xiaoyun Xia, Yaolong Duan

“…To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). …”
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A Novel Network Optimization Scheme Based on Anti-Flocking and Improved Nash Equilibrium Algorithm by Tianjun Wang, Shuchang Zhang, Lishan Liu, Duanpo Wu, Xinyu Jin, Shuwei Cen, Bing Fan

“…In this paper, a novel network optimization scheme based on anti-flocking model and improved Nash Equilibrium (NE) algorithm is proposed by studying the problem of dynamic UAV deployment and backhaul transmission. …”
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Optimization of Human Resource Performance Management System Based on Improved R-Means Clustering Algorithm by Rui Wang

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Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation by Lin Sun, Suisui Chen, Jiucheng Xu, Yun Tian

“…Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. …”
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Development of Spectral Clustering Algorithm in Cognitive Diagnosis Model: Approach for Student’s Psychological Growth by Xiao Chang

“…The spectral clustering (SC) algorithm iterative optimization was combined with a similarity matrix and Laplacian matrix to construct an improved spectral clustering cognitive diagnosis model. …”
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Extraction of the Optimal Parameters of Single-Diode Photovoltaic Cells Using the Earthworm Optimization Algorithm by Fatima Wardi, Mohamed Louzazni, Mohamed Hanine

“…This study introduces a novel method for assessing and deriving the electrical properties of simple diode model solar cells through the utilization of the Earthworm Optimization Algorithm (EOA). …”
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An Improved Optimal Linear Weighted Cooperative Spectrum Sensing Algorithm for Cognitive Radio Sensor Networks by Yonghua Wang, Yuehong Li, Jian Yang, Pin Wan, Qinruo Wang

“…Through mathematical modeling, the spectrum sensing problem is ultimately converted into a constrained nonconvex optimization problem, and the chaotic harmony search (CHS) algorithm is to be used to find the optimal weighting vector value. …”
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An Improved Multi-Objective Adaptive Human Learning Optimization Algorithm and Its Application in Optimizing Formulation Schemes for Rotary Hearth Furnaces by Jun Yao, Songcheng Zhou, Ling Wang, Xianxia Zhang

“…An improved multi-objective adaptive human learning optimization algorithm (IMOAHLO) is proposed, which enhances local optimization through neighborhood search and an adaptive learning mechanism. …”
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Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization by Urbanus Mwanzia Ngei, Abraham Mutunga Nyete, Peter Musau Moses, Cyrus Wekesa

“…Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.…”
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Enhancing network lifetime in WSNs through coot algorithm-based energy management model by Namita Shinde, Dr. Vinod H․ Patil

“…This addresses issues such as high energy consumption, communication delays, and security.To ensure energy savings and network reliability, the fitness function evaluates cluster heads and best routes based on constraints.COOT outperforms other Metaheuristics Algorithms like Butterfly Optimization Algorithm, Genetic Algorithm, Tunicate Swarm Gray Wolf Optimization Algorithm, and Bird Swarm Algorithm in simulation with performance measurements and enhancing network functionality and protection.Key methodology points include: • Proposed a multiple constraints clustering and routing technique using COAto solve the most crucial issues that arise in WSNs. • Integrated an advanced fitness function that determines cluster head selection, and the routing path based on residual energy, delay, security, trust, distance, and link quality so that energy load is evenly distributed and credible data flow is maintained across the network and made Innovative and Effective Solution. • Proven Results Demonstrated superior network performance, achieving the lowest delay, highest network lifetime (3571 rounds) and enhanced security (0.8) and trust (0.6) compared to existing algorithms with less energy consumption, making it the most suitable solution for WSN performance improvement.…”
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