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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Enhanced Dung Beetle Optimizer-Optimized KELM for Pile Bearing Capacity Prediction by Bohang Chen, Mingwei Hai, Gaojian Di, Bin Zhou, Qi Zhang, Miao Wang, Yanxiu Guo

“…In response to the need for rapid and precise predictions of pile bearing capacity, this study introduces a kernel extreme learning machine (KELM) prediction model optimized through a multi-strategy improved beetle optimization algorithm (IDBO), referred to as the IDBO-KELM model. …”
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A Wi-Fi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm by Xu Feng, Khuong An Nguyen, Zhiyuan Luo

“…However, their performance varies significantly across different indoor environments, making it challenging to identify the most suitable system for all scenarios. To address this challenge, we propose an algorithm that dynamically selects the most optimal Wi-Fi positioning model for each location. …”
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An Improved Salp Swarm Algorithm for Solving a Multi-Temperature Joint Distribution Route Optimization Problem by Yimei Chang, Jiaqi Yu, Yang Wang, Xiaoling Xie

“…To solve this model, an improved salp swarm algorithm (SSA) has been developed. …”
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Research on intelligent control of coal slime flotation based on the WOA-GRU model by DOU Zhiheng, WANG Ranfeng, QIN Xinkai, CHAI Yuqing, LI Pinyu, LIU Shutong

“…Due to the complexity of the flotation process mechanism, which makes it difficult to meet the requirements of advanced process control, a system identification modeling method was adopted. To address the low fitting accuracy of traditional identification methods, a Whale Optimization Algorithm (WOA)-based Gated Recurrent Unit (GRU) system identification model (WOA-GRU) was proposed. …”
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Improvement teaching-learning-based optimization algorithm for solar cell parameter extraction in photovoltaic systems by H. Khaterchi, M. H. Moulahi, A. Jeridi, R. Ben Messaoud, A. Zaafouri

“…Goal. The work aims to improve the Teaching-Learning-Based Optimization (TLBO) algorithm to enhance the accuracy of parameter extraction in PV models. …”
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Modelling and optimization for cognitive radio networks with preemption backoff mechanism by Yuan Zhao, Zhiyu Xiang, Kang Chen, Zhisheng Ye, Qi Lu

“…At last, we design a cost function by compromising various performance indices and find the optimum parameter combination under the lowest system cost through the Transient Search Optimization (TSO) algorithm. The numerical and simulation results illustrate our proposed PBM can improve SUs’ transmission continuity. …”
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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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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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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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Optimization based machine learning algorithms for software reliability growth models by Myeongguen Shin, Juwon Jung, Jihyun Lee, Insoo Ryu, Sanggun Park

“… Software reliability is a critical factor for system performance and safety, especially in defense industries, where operational failures can have severe consequences. To evaluate and improve software reliability, Software Reliability Growth Models (SRGMs) are widely used. …”
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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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Modeling and Optimization of Cable Production Scheduling by Incorporating an Ant Colony Algorithm by Changbiao Zhu, Chongxin Wang, Zhonghua Ni, Xiaojun Liu, Abbas Raza

“…Applying an ant colony (ACO) algorithm to solve the production scheduling problem achieved the intelligent scheduling and optimization of production tasks. …”
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Optimal Allocation of Hybrid Energy Storage in Low-Voltage Distribution Networks with Incentive-based Demand Response by Fengliang XU, Keqian WANG, Wenhao WANG, Peng WANG, Wenye WANG, Shuai ZHANG, Fengzhan ZHAO

“…Then, based on the characteristics of energy storage devices and incentive-based demand-side response resources at different time scales, it is proposed to use the improved VMD algorithm to make a multi-scale decomposition and combined reconstruction of the net load curves, and the improved whale optimization algorithm is used to solve the optimal allocation model with the objective of the minimum sum of the total system cost and active power fluctuation value. …”
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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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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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