An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia by Obaid Algahtani, Mohammed M. A. Almazah, Farouq Alshormani

“…Eventually, the modified pelican optimization algorithm (MPOA) is utilized to fine-tune the optimal hyperparameter of ensemble model parameters to achieve high predictive performance. …”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…In addition, samples are needed to solve such system optimization problems. Therefore, to improve the optimization accuracy of nonlinear constrained single objective systems that are without accurate mathematical models while considering the cost of obtaining samples, a new method based on a combination of support vector machine and immune particle swarm optimization algorithm (SVM-IPSO) is proposed. …”
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Building Construction Design Based on Particle Swarm Optimization Algorithm by Wenxue Song

“…The relationship between the various risk factors was described by conditional probability, and a safety risk loss-control investment double objective optimization model was built. The corresponding algorithm was designed and the R language programming was used to solve the problem. …”
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Robust reinforcement learning algorithm based on pigeon-inspired optimization by Mingying ZHANG, Bing HUA, Yuguang ZHANG, Haidong LI, Mohong ZHENG

“…Reinforcement learning(RL) is an artificial intelligence algorithm with the advantages of clear calculation logic and easy expansion of the model.Through interacting with the environment and maximizing value functions on the premise of obtaining little or no prior information, RL can optimize the performance of strategies and effectively reduce the complexity caused by physical models .The RL algorithm based on strategy gradient has been successfully applied in many fields such as intelligent image recognition, robot control and path planning for automatic driving.However, the highly sampling-dependent characteristics of RL determine that the training process needs a large number of samples to converge, and the accuracy of decision making is easily affected by slight interference that does not match with the simulation environment.Especially when RL is applied to the control field, it is difficult to prove the stability of the algorithm because the convergence of the algorithm cannot be guaranteed.Considering that swarm intelligence algorithm can solve complex problems through group cooperation and has the characteristics of self-organization and strong stability, it is an effective way to be used for improving the stability of RL model.The pigeon-inspired optimization algorithm in swarm intelligence was combined to improve RL based on strategy gradient.A RL algorithm based on pigeon-inspired optimization was proposed to solve the strategy gradient in order to maximize long-term future rewards.Adaptive function of pigeon-inspired optimization algorithm and RL were combined to estimate the advantages and disadvantages of strategies, avoid solving into an infinite loop, and improve the stability of the algorithm.A nonlinear two-wheel inverted pendulum robot control system was selected for simulation verification.The simulation results show that the RL algorithm based on pigeon-inspired optimization can improve the robustness of the system, reduce the computational cost, and reduce the algorithm’s dependence on the sample database.…”
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GNN-based optimization algorithm for joint user scheduling and beamforming by Shiwen HE, Jun YUAN, Zhenyu AN, Min ZHANG, Yongming HUANG, Yaoxue ZHANG

“…The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer, respectively.Under the consideration of user service quality requirements, the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information, a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead.…”
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Design of improved JAYA algorithm for cigarette finished product logistics delivery by Jun Wen, Yewei Hu, Le Li, Zongrui Wu, Guangwei Xiao, Kai Guo, Lei Li

“…In response to these challenges, this study proposes an improved Jaya algorithm that integrates a reverse learning mechanism and a cosine similarity strategy to enhance optimization performance. …”
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Optimization of distribution networks using quantum annealing for loss reduction and voltage improvement in electrical vehicle parking management by Naser Rashnu, Babak Mozafari, Reza Sharifi

“…Traditional optimization techniques like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) often struggle with the nonlinear, high-dimensional nature of EV-grid interaction problems. …”
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TBESO-BP: an improved regression model for predicting subclinical mastitis by Kexin Han, Yongqiang Dai, Huan Liu, Junjie Hu, Leilei Liu, Zhihui Wang, Liping Wei

“…The TBESO algorithm notably enhances the efficacy of the BP neural network in regression prediction, ensuring elevated computational efficiency and practicality post-improvement.…”
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Modelling and optimization of well hole cleaning using artificial intelligence techniques by Nageswara Rao Lakkimsetty, Hassan Rashid Ali Al Araimi, G. Kavitha

“…This study aims to improve the accuracy and practicality of hole cleaning assessment by applying Artificial Intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to predict downhole parameters and optimize drilling processes. …”
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Cost-Based Optimal Allocation of Shunt Capacitors in Radial Distribution Networks Considering Load Types Using Crow Search Algorithm by Stephen W. Mathenge, Edwell. T. Mharakurwa, Lucas Mogaka

“…This study investigates the optimization of capacitor placement and sizing using the Crow Search Algorithm (CSA) to enhance voltage stability, minimize power losses, and reduce operational costs. …”
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Insulator Defect Detection Algorithm Based on Improved YOLOv11n by Junmei Zhao, Shangxiao Miao, Rui Kang, Longkun Cao, Liping Zhang, Yifeng Ren

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Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm by Pham Vu Hong Son, Luu Ngoc Quynh Khoi

“…To improve the suggested model's superiority and effectiveness, it is compared to the multiple-target swarm algorithm (MOPSO), multi-objective artificial bee colony (MOABC) and non-dominant sort genetic algorithm (NSGA-II). …”
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AC Optimal Power Flow Problem Considering Wind Energy by an Improved Particle Swarm Optimization by Mohammad Reza Ansari, Hossein Ramzaninezhad

“…To solve the AC-OPF model, an Improved Particle Swarm Optimization (IPSO) is presented. …”
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Operation Optimization Strategy of Commercial Combined Electric Heating System Based on Particle Swarm Optimization Algorithm by WANG Qing, LI Congcong, WANG Pingxin, WU Qingqing, CAI Xiaoyu

“… In order to improve the energy efficiency of the electric heating system, a particle swarm optimization (PSO, Particle Swarm Optimization)-based operation optimization strategy for the direct storage combined electric heating system is proposed.A mathematical model of influencing factors inside and outside the walls of electric heating buildings is established, and the simulink toolbox in matlab is used to build the overall system under the premise of determining the quantity of electric heating.Combining demand response ideas, the objective function is to establish the minimum heating and electricity cost of the user, and different sub-modules are selected to form the control module to achieve simulation verification, and the inverse cosine method is used to update the improved particle swarm algorithm to update the learning factor to solve the set objective function.Finally, through a calculation example of electricity consumption data of an enterprise in Jinan, Shandong, comparing energy consumption and economy can be obtained: the total energy consumption throughout the day is lower than the actual energy consumption, and the electricity bill is reduced by 17.16% compared with the unoptimized time.…”
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Presenting a multi-objective decision-making model for cost-time trade-off considering the time value of money and solving it using particle swarm optimization by Mohammadreza Shahriari

“…This model assists project managers in gaining better insight into cost-time impacts, optimizing resource allocation, and ultimately improving project performance by reducing delays.…”
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Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance by Ganesh Selvaraj, Kanimozhi Rajangam, Pradeep Vishnuram, Mohit Bajaj, Ievgen Zaitsev

“…The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. …”
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Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy by Katta Lakshmi Narasimhamu, Manikandan Natarajan, Pasupuleti Thejasree, Emad Makki, Jayant Giri, Neeraj Sunheriya, Rajkumar Chadge, Chetan Mahatme, Pallavi Giri, T. Sathish

“…Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.…”
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Coordinated optimal scheduling of island microgrid for power-hydrogen-carbon integration based on SAO-NSGA-II algorithm by XI Honglei, SUN Jingliao, QU Hezuo, SHI Zhengchai, HU Changhong, LIU Jinyuan

“…Finally， through simulation examples， a comparative analysis of the results before and after the algorithm improvement is performed， validating the feasibility of the proposed improved algorithm and optimal scheduling model. …”
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An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks by Xianglong Luo, Fengrong Yu, Jing Qian, Biao An, Nengpeng Duan

“…To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining the Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. …”
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A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electrici... by Xinfu Pang, Wen Jia, Haibo Li, Qingzhong Gao, Wei Liu

“…Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. …”
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