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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Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms by Hu Caixiong, Zhang Lili, Li Haoran, Zhang Yaowen, Yao Yunsheng

“…A long short-term memory (LSTM) neural network combined with the gray wolf optimizer (GWO) algorithm was introduced to improve prediction accuracy. …”
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Optimization of Random Forest Algorithm with Backward Elimination Method in Classification of Academic Stress Levels by Salsabila Dani Amalia, Mula Agung Barata, Pelangi Eka Yuwita

“…Data mining is often used to detect diseases, one of which is the Random Forest algorithm. The Random Forest algorithm is applied as a classification technique for academic stress levels, with optimization using the Backward Elimination method for feature selection to improve model accuracy. …”
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Efficiency management of engineering projects based on particle swarm multi objective optimization algorithm by Xiu Luo, Zhouxin Yi

“…In transportation planning, multi-objective particle swarm optimization algorithm can optimize path planning, improve transportation efficiency and resource utilization.…”
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SMOTE algorithm optimization and application in corporate credit risk prediction with diversification strategy consideration by Han Wei

“…Empirical results demonstrate the optimized SMOTE algorithm’s superiority over six comparison models, such as random over-sampling, under-sampling, etc. …”
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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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ADVANCE HYDRAULIC SUPPORT MULTI-OBJECTIVE OPTIMIZATION DESIGN BASED ON NSGA-Ⅱ ALGORITHM by NIU YanQi, LIU Wei, ZHANG DeSheng

“…Contrapose the problems of hydraulic support connecting rod mechanical fracture,at the same external load force link mechanical to advance the sum of the absolute values,and minimum quality of the objective function,establishment of a multi-objective optimization model,to solve the optimization used the NSGA-Ⅱ algorithm,using fuzzy set theory method to choose the optimal solution,and has compiled into a visual computing program under matlab environment. …”
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A two-stage recommendation optimization algorithm based on item popularity and user features by Jun Wang, Rongjie Hu

“…The modified Pearson's similarity function is combined with popularity normalization and user features to improve modeling performance. The two-stage recommendation optimization procedure was combined with a collaborative filtering algorithm to improve recommendation precision. …”
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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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A mobile app to improve adherence to colorectal cancer screening and post polypectomy surveillance guidelines by María A. Casas, Francisco Schlottmann, Leandro Steinberg, Xavier Bessa, Anna Serradesanferm, Angels Pozo, Sonia Torres, Antoni Castells, Francesc Balaguer, Jaume Grau, Lisandro Pereyra, María Pellisé

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Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm by Shandong Yuan, Yun Ren, Han Zhou, Yongjing Cheng, Kai Yan

“…In addition, we innovatively incorporate the concept of ‘group effect’ from sociology, adding a socialization operator to the algorithm to accelerate convergence and improve the quality of optimal solutions. …”
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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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Time-optimal trajectory planning for excavators operating arm based on SA-PSO algorithm by ZHANG Yunyue, ZHAO Zhicheng, YANG Kai, ZHANG Yu, YANG Jiangtao

“…At the same time, the hybrid algorithm takes the least time to obtain the optimal trajectory, which helps to improve the real-time performance of the excavator operating arm in actual engineering operations. …”
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A Novel Method for News Recommendation on Websites Using the Clustered-Vectors Optimization Algorithm by Piyanuch Chaipornkaew, Thepparit Banditwattanawong

“…The proposed algorithm aims to optimize the clustering of news titles to improve the performance of news recommendations. …”
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Development of algorithms for constructing two-dimensional optimal boundary-adaptive grids and their software implementation by A. E. Chistyakov, V. V. Sidoryakina, S. V. Protsenko

“…It is noted that the use of adaptive grids in calculations makes it possible to improve the accuracy and efficiency of computational algorithms without increasing the number of nodes. …”
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Enhancing analogy-based software cost estimation using Grey Wolf Optimization algorithm by Taghi Javdani Gandomani, Maedeh Dashti, Sadegh Ansaripour, Hazura Zulzalil

“…Although this method has been customized in recent years with the help of optimization algorithms to achieve better results, the use of more powerful optimization algorithms can be effective in achieving better results in software size estimation. …”
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Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

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An effective parameter estimation on thermoelectric devices for power generation based on multiverse optimization algorithm by Luis Fernando Grisales-Noreña, Vanessa Botero-Gómez, Rubén Iván Bolaños, Faustino Moreno-Gamboa, Daniel Sanin-Villa

“…The parameters were estimated by conducting experimental power measurements and comparing the results with the real ones predicted by the equations model using the root mean square error. The Multiverse Optimization Algorithm demonstrates superior performance compared to other algorithms presented in previous works for this case. …”
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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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