Wind power generation prediction using LSTM model optimized by sparrow search algorithm and firefly algorithm by Wenjing Zhang, Hongjing Yan, Lili Xiang, Linling Shao

“…Then, the sparrow search algorithm and firefly algorithm are combined to optimize the hyperparameter configuration, improving the predictive performance and global search ability of the model. …”
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Cloud service composition optimization based on service association impact and improved NSGA-II algorithm by Chong Zhang, Longge Wang, Ketai He

“…To efficiently solve this model, we propose an enhanced NSGA-II algorithm with the following key improvements: (1) Good point set-based population initialization, integrating good point sets and random sampling to enhance solution diversity and search efficiency. (2) Reverse learning-based crossover operator, designed to improve exploration capability and prevent premature convergence. (3) Adaptive dynamic elitism strategy, which dynamically adjusts the elite retention ratio and adaptively incorporates local search operators to balance convergence and diversity. …”
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Optimized Intelligent Localization Through Mathematical Modeling and Crow Search Algorithms by Tamer Ramadan Badawy, Nesreen I. Ziedan

“…However, existing localization methods still fall short of achieving the precision needed for certain high-demand applications. The proposed algorithm is designed to enhance localization accuracy by integrating mathematical modeling with the Crow Search Algorithm (CSA). …”
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Optimization of Railway Transportation Planning by Combining TST Model and Genetic Algorithm by Wei Cao, Fan Chen

“…The study proposes an integrated method that combines the Temporal-Spatial Tunnel (TST) model with the Genetic Algorithm (GA). The TST model describes railway transportation changes dynamically by integrating temporal and spatial dimensions. …”
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IMPROVEMENT OF EVOLUTIONARY STRUCTURAL OPTIMIZATION METHOD FOR 2-D MODEL by CHEN XiaoMing, LAI XiDe, TANG Jian, ZHU Li, ZHAO Xi

“…which the singular element appears in the optimum process and the result may be a partial optimum solution are two disadvantages when using the ESO method to optimize 2- D model. In this paper,two algorithms are proposed for solving these disadvantages. …”
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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

“…This integration is essential for the improvement of DRL models in general and allows for more efficient and real-time game play. …”
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Study on Multiobjective Path Optimization of Plant Protection Robots Based on Improved ACO‐DWA Algorithm by Jing Niu, Chuanyan Shen, Lipeng Zhang, Guanghao Gao, Jiahao Zheng

“…To address the false judgment of visual information caused by the shading of branches and leaves and the difficulty in avoiding obstacles in complex orchard terrain, an operation trajectory optimization approach based on the improved dynamic window algorithm (DWA) with ant colony optimization (ACO) was developed. …”
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Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm by Rui Liang, Dingzhao Li, Haixin Sun, Liangpo Hong

“…We propose a multi-objective formation optimization framework based on an improved genetic algorithm that simultaneously considers the detection coverage area, forward detection width, inter-agent communication, and static obstacle avoidance. …”
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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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Bi-objective Rolling Operation Optimization Based on Surrogate Model Acceleratiy of Community-Level Integrated Energy Systems by Xiaoman HU, Weikun TIAN, Guanyu SONG, Hao YU

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Improved satellite resource allocation algorithm based on DRL and MOP by Pei ZHANG, Shuaijun LIU, Zhiguo MA, Xiaohui WANG, Junde SONG

“…In view of the multi-objective optimization (MOP) problem of sequential decision-making for resource allocations in multi-beam satellite systems,a deep reinforcement learning(DRL) based DRL-MOP algorithm was proposed to improve the system performance and user satisfaction degree.With considering the normalized weighted sum of spectrum efficiency,energy efficiency,and satisfaction index as the optimization goal,the dynamically changing system environments and user arrival model were built by the proposed algorithm,and the optimization of the accumulative performance in satellite systems based on DRL and MOP was realized.Simulation results show that the proposed algorithm can solve the MOP problem with rapid convergence ability and low complexity,and it is obviously superior to other algorithms in terms of system performance and user satisfaction optimization.…”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

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Optimal Active-Reactive Power Dispatch for Distribution Network With Carbon Trading Based on Improved Multi-Objective Equilibrium Optimizer Algorithm by Furong Tu, Sumei Zheng, Kuncan Chen

“…Then proposed an improved multi-objective equilibrium optimizer (IMOEO) algorithm for solving the OARPD problem with renewable generators. …”
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Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms by Jingkai Li, Jun Zhang, Jiaquan Xie, Wei Shi, Jianzhong Zhao

“…To achieve precise docking in unmanned conditions, we employed an inner-loop fractional-order proportional–integral–derivative (FOPID) controller optimized by an improved particle swarm optimization (ILPSO) algorithm. …”
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Application of Optimization Algorithms in Voter Service Module Allocation by Edgar Jardón, Marcelo Romero, José-Raymundo Marcial-Romero

“…Allocation models are essential tools for optimally distributing client requests across multiple services under defined restrictions and objective functions. …”
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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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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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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning by Muhammad Zubair Yameen, Zhigang Lu, Fayez F. M. El-Sousy, Waqar Younis, Baqar Ali Zardari, Abdul Khalique Junejo

“…The offline phase employs a novel Hybrid Crayfish Optimization and Self-Adaptive Differential Evolution Algorithm (COA-jDE) to minimize the cost function $$U_{offline}$$ , deriving optimal control parameters (Q, R) before real-time deployment. …”
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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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