A novel methodological approach to SaaS churn prediction using whale optimization algorithm. by Muhammed Kotan, Ömer Faruk Seymen, Levent Çallı, Sena Kasım, Burcu Çarklı Yavuz, Tijen Över Özçelik

“…This study introduces a novel approach to SaaS churn prediction using the Whale Optimization Algorithm (WOA) for feature selection. Results show that WOA-reduced datasets improve processing efficiency and outperform full-variable datasets in predictive performance. …”
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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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HYBRID FIREFLY AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR DESIGN OF NON-LINEAR CHANNEL EQUALIZER by Vundavalli Ravindra, Pradyumna Kumar Mohapatra, Ravi Narayan Panda, Saroja Kumar Rout

“…This optimum solution has been modelled using the FFA algorithm, which is driven mainly by light intensity attraction. …”
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Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm by Weibo LI, Feng GAO, Peng XIAO, Kangzheng HUANG, Daojie RUAN, Junzhuo GAO

“…Therefore, a fault diagnosis method based on whale optimization algorithm-optimized random forest (WOA-RF) is proposed for the marine DG power distribution system.MethodsThe marine DG power distribution system model is built using Matlab/Simulink simulation software. …”
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Quality of service optimization algorithm based on deep reinforcement learning in software defined network by Cenhuishan LIAO, Junyan CHEN, Guanping LIANG, Xiaolan XIE, Xiaoye LU

“…Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.An algorithm of quality of service optimization algorithm of based on deep reinforcement learning (AQSDRL) was proposed to solve the QoS problem of SDN in the data center network (DCN) applications.AQSDRL introduces the softmax deep double deterministic policy gradient (SD3) algorithm for model training, and a SumTree-based prioritized empirical replay mechanism was used to optimize the SD3 algorithm.The samples with more significant temporal-difference error (TD-error) were extracted with higher probability to train the neural network, effectively improving the convergence speed and stability of the algorithm.The experimental results show that the proposed AQSDRL effectively reduces the network transmission delay and improves the load balancing performance of the network than the existing deep reinforcement learning algorithms.…”
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Fruit-Fly-Optimized Weighted Averaging Algorithm for Data Fusion in MEMS IMU Array by Ting Zhu, Gao Peng, Jianping Li, Jiawei Xuan, Jingbei Tian

“…In this study, an optimal weighted averaging algorithm based on the fruit fly optimization algorithm (FOA) is proposed by analyzing the data fusion mechanism of the MEMS IMU array. …”
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Load optimization of cogeneration units based on intuitive multi-objective fish swarm algorithm by Xueqiang Shen, Jiaxin Wang

“…Convergence direction is adaptively adjusted using intuitionistic fuzzy entropy, with Pareto frontier solutions determining optimal load allocation. Evaluated via the Zitzler-Deb-Thiele (ZDT) benchmark functions, IFEMOAFSA achieves a 42.63% comprehensive performance improvement over four benchmark algorithms, verified by Mean Inverted Generational Distance (MIGD) and Mean Hypervolume Metric (MHV). …”
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An Enhanced Genetic Algorithm for Optimized Educational Assessment Test Generation Through Population Variation by Doru-Anastasiu Popescu

“…The most important aspect of a genetic algorithm (GA) lies in the optimal solution found. …”
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Detection and Classification of Power Quality Disturbances Based on Improved Adaptive S-Transform and Random Forest by Dongdong Yang, Shixuan Lü, Junming Wei, Lijun Zheng, Yunguang Gao

“…The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). …”
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Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model by Yiqing Yang, Guoyin Zhang, Yanxia Wu, Zhixiang Zhao, Yan Fu

“…Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system’s response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems.…”
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Portfolio optimization with MOPSO-Shrinkage hybrid model by Minh Tran, Nhat M. Nguyen

“…Unlike traditional shrinkage covariance models, which struggle in highly dynamic environments, our hybrid model optimally selects stocks and improves risk-adjusted returns. …”
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Practical Application of Complementary Regulation Strategy of Run-of-River Small Hydropower and Distributed Photovoltaic Based on Multi-Scale Copula-MPC Algorithm by Xianpin Zhu, Weibo Li, Shuai Cao, Wei Xu

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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…The sensitivity assessment approves that the training and generalization abilities of the ELM and ANFIS models for the CS prediction of FRC are improved by their integration with the GWO algorithm. …”
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Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) by Armin Moghimi, Ava Tavakoli Darestani, Nikrouz Mostofi, Mahdiyeh Fathi, Meisam Amani

“…Furthermore, incorporating GA-based feature selection substantially improved the accuracy of both models, resulting in more dependable AGB estimations. …”
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Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid

“…These evolutionary algorithms simulate natural processes like selection, mutation, and crossover to optimize flood predictions and management strategies, improving adaptability in dynamic environments. …”
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Mathematical Models for Management Information Systems on Digital Platforms: from Resource Management to Demand Forecasting by Viktor Godliuk

“…The use of optimization methods, graph algorithms, forecasting, and machine learning to improve the efficiency of digital systems is investigated. …”
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Storage Location Assignment in Emergency Reserve Warehouses: A Multi-Objective Optimization Algorithm by Chen Liang, Tao Cui, Yu Wei, Kun Zhao, Xiongping Yue, Chao Wang

“…The findings indicate that the application of the second scenario significantly improves storage location optimization in emergency reserve warehouses.…”
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Study on College English Online Teaching Model in Mixed Context Based on Genetic Algorithm and Neural Network Algorithm by Xiaoxia Ma

“…The algorithm avoids subjective errors and improves the accuracy and reliability of the evaluation results, and a comprehensive evaluation model is constructed. …”
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Load Frequency Control Based on Gray Wolf Optimizer Algorithm for Modern Power Systems by Dao Huy Tuan, Dao Trong Tran, Van Nguyen Ngoc Thanh, Van Van Huynh

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Optimizing Solid Oxide Fuel Cell Performance Using Advanced Meta-Heuristic Algorithms by Siva Ram Rajeyyagari, Srinivas Nowduri

“…These findings suggest that the AOA method not only offers superior performance but also exhibits the highest convergence among the tested optimization models. This research confirms the potential of advanced optimization techniques in improving the operational parameters of SOFCs, setting the stage for future advancements in fuel cell technologies.…”
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