Improving Quantum Optimization Algorithms by Constraint Relaxation by Tomasz Pecyna, Rafał Różycki

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Data Decomposition Modeling Based on Improved Dung Beetle Optimization Algorithm for Wind Power Prediction by Jiajian Ke, Tian Chen

“…To enhance prediction accuracy, this paper presents a hybrid wind power prediction model that integrates the improved complementary ensemble empirical mode decomposition (ICEEMDAN), the RIME optimization algorithm (RIME), sample entropy (SE), the improved dung beetle optimization (IDBO) algorithm, the bidirectional long short-term memory (BiLSTM) network, and multi-head attention (MHA). …”
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Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation by Rui Fan, Huiying Jing, Zhixin Jing

“…In order to solve the current situation, an optimized scheduling model for power mobile jobs based on improved genetic algorithm is proposed. …”
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Optimizing pyrolysis and Co-Pyrolysis of plastic and biomass using Artificial Intelligence by Manish Sharma Timilsina, Yuvraj Chaudhary, Prikshya Bhattarai, Bibek Uprety, Dilip Khatiwada

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Coal Price Forecasting Using CEEMDAN Decomposition and IFOA-Optimized LSTM Model by Zhuang Liu, Xiaotuan Li

“…Abstract This study introduces a novel hybrid forecasting model for coking coal prices, integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long short-term memory (LSTM) neural networks, enhanced by an improved fruit fly optimization algorithm (IFOA). …”
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IoT driven healthcare monitoring with evolutionary optimization and game theory by Shitharth Selvarajan, Hariprasath Manoharan, Taher Al-Shehari, Nasser A. Alsadhan, Subav Singh

“…By incorporating two evolutionary algorithms, the proposed approach optimizes the state of action for each participant while reducing energy consumption and processing delay. …”
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Shuffled Puma Optimizer for Parameter Extraction and Sensitivity Analysis in Photovoltaic Models by En-Jui Liu, Rou-Wen Chen, Qing-An Wang, Wan-Ling Lu

“…To address this challenge, a novel metaheuristic algorithm called shuffled puma optimizer (SPO) is deployed to perform parameter extraction and optimal configuration identification across four PV models. …”
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Optimization of UAV Airfoil Based on Improved Particle Swarm Optimization Algorithm by Tieying Jiang, Liang Jiang

“…The parametric mathematical model of the airfoil and aerodynamic parameter proxy model of airfoil were constructed using the Hicks-Henne improved function and CFD solution sample, focusing on the issues with particle swarm optimization algorithms such as slow convergence, a tendency to fall into local optimal solutions, and oscillation at a late stage; an optimization method for the low-speed airfoil of a small UAV based on improved particle swarm optimization was developed. …”
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Optimal Operation of Microgrid Based on Improved Particle Swarm Optimization Algorithm by Shaoming ZHANG, Siqing SHENG

“…Under same conditions, the optimal operation model of the microgrid is solved using the original algorithm and the improved algorithm respectively, and the superiority of the improved algorithm is verified by comparing the solution results.…”
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Research on Interference Resource Optimization Based on Improved Whale Optimization Algorithm by Xuyi Chen, Mingxi Ma, Chengkui Liu, Haifeng Xie, Shaoqi Wang

“…Then, in the solution process, to address the issues of the Whale Optimization Algorithm (WOA) easily falling into local optima and low convergence accuracy, the BIO-WOA (Bernoulli Chaotic mapping In-nonlinear Factors and Opposition-based Learning Improved Whale Optimization Algorithm, BIO-WOA) is proposed. …”
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A Framework for Breast Cancer Classification with Deep Features and Modified Grey Wolf Optimization by Fathimathul Rajeena P.P, Sara Tehsin

“…A modified Grey Wolf Optimization algorithm with three significant adjustments improves feature selection and redundancy removal over the previous approach. …”
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Hybrid RSA-ROA Scheduling Algorithm for Minimization of Power Loss and Improving the Renewable with Sustainable Energy Harvesting in Power System by Cuddapah Anitha, Virendra Swaroop Sangtani, Ajay Kumar Bansal, Mahaveerakannan R., R. Rajesh Sharma, Saravanan M. S.

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An electricity price optimization model considering time-of-use and active distribution network efficiency improvements by Yan Li, Yaheng Su, Qixin Zhao, Bala Wuda, Kaibo Qu, Lei Tang

“…To address the issues of high energy costs and inadequate system response speed in complex electricity markets, we propose an electricity price optimization model. This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the Shuffle Frog Leaping Algorithm. …”
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Beyond boundaries: AI-optimized global landslide susceptibility mapping by Mahdi Panahi, Fatemeh Rezaie, Khabat Khosravi, Zahra Kalantari, Sayed M. Bateni, Jeong-A Lee

“…This study addresses these gaps by developing an optimized framework using support vector regression (SVR) enhanced with meta-heuristic algorithms (grey wolf optimizer [GWO] and bat algorithm) to refine model hyper-parameters. …”
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Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks by Manal Abdullah Alohali, Hamed Alqahtani, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K., Jaehyuk Cho

“…The methodology consists of three key phases: (1) Data preprocessing, where missing values are handled using the multiple imputations by chain equation (MICE) technique and feature scaling is applied using standard and min-max scalers; (2) Feature selection, where the FFXO algorithm reduces feature dimensionality to enhance classification efficiency; and (3) Lung tumor classification, utilizing Bi-GAN to improve predictive accuracy. …”
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Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks by Anjana Koyalil, Sivacoumar Rajalingam

“…Additionally, the absence of clustering in certain models compromises the network capacity. To tackle these challenges, a new multi-level clustering technique with a heuristic optimization algorithm is proposed in this research work. …”
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Cloud-based Optimized Intelligent Substation Secondary System Rapid Test Model and System Design by Rongrong JI, Mengzhi WANG, Guowei ZHOU, Qingyao LIU, Hai LI, Ruofan HUANG

“…The operations of switching the applied quantity, throwing and closing the soft and hard plates and starting and stopping the terminal are further considered, and a fast testing mathematical model aiming at the shortest time is proposed. The test sequence is optimized based on the improved whale optimization algorithm (SWOA). …”
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Combination of Intermittent Search Strategy and an Improve Particle Swarm Optimization algorithm (IPSO) for model updating by Thanh Sang-To, Hoang-Le Minh, Tran-Thanh Danh, Samir Khatir, Magd Abdel Wahab., Thanh Cuong-Le

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Optimizing performance and economic viability of combined energy systems: A novel energy hub framework by Dan Wang, Chenlin Ying

“…To solve this complex optimization problem, the spider monkey optimization (SMO) algorithm is modified and applied. …”
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VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection by Sanam Salman Kazi, Bhakti Palkar, Dhirendra Mishra

“…It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification.…”
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