An optimization-inspired intrusion detection model for software-defined networking by Hui Xu, Longtan Bai, Wei Huang

“…Currently, more and more intrusion detection systems based on machine learning and deep learning are being applied to SDN, but most have drawbacks such as complex models and low detection accuracy. This paper proposes an enhanced spider wasp optimizer (ESWO) algorithm for feature dimensionality reduction of intrusion detection datasets and constructs a new intrusion detection model (IDM), namely ESWO-IDM, for SDN. …”
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PCA-FSA-MLR Model and Its Application in Runoff Forecast by GUO Cunwen, CUI Dongwen

“…To improve the accuracy of runoff forecast,and establish a runoff forecast model combining principal component analysis (PCA),future search algorithm (FSA),and multiple linear regression (MLR),this paper reduces the dimensionality of the sample data by PCA,selects 8 standard test functions and simulates and verifies FSA under different dimensional conditions,optimizes MLR constant terms and partial regression coefficients by FSA,proposes a PCA-FSA-MLR runoff forecast model,constructs PCA-LS-MLR,PCA-FSA-SVM,and PCA-SVM models with dimensionality reduction processing by PCA and FSA-MLR,LS-MLR,FSA-SVM,and SVM without dimensionality reduction processing as a comparison model,and verifies each model through forecasting the annual runoff and monthly runoff in December of Longtan station in Yunnan Province.The results show that:①FSA has better optimization accuracy and global extremum search ability under different dimensional conditions;②The average absolute relative error of the annual runoff and monthly runoff in December of Longtan station through PCA-FSA-MLR model are 1.63% and 3.91% respectively,and its forecast accuracy is better than the other 7 models,with higher forecast accuracy and stronger generalization ability;③For the same model,the forecast accuracy after dimensionality reduction processing by PCA is better than that without dimensionality reduction processing,so the data dimensionality reduction by PCA is helpful to improve the forecast accuracy of models.…”
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Construction and Application of Agricultural Talent Training Model Based on AHP-KNN Algorithm by Shubing Qiu, Yong Liu, Xiaohong Zhou

“…To solve this problem, an improved AHP-KNN algorithm is proposed by combining the analytic hierarchy process (AHP) and the optimized K-nearest neighbor algorithm, and an agricultural talent training model is proposed based on this algorithm. …”
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Research on Stacking Distribution of Steel Plates Input Based on Improved Multi-objective Particle Swarm Optimization by ZHONG Chuanjie, CHENG Wenming, DU Run, GAO Xuchao, ZHANG Lin

“…Finally, the stacking situations before and after optimization were compared. Compared to the traditional stacking method used before optimization, the optimized stacking distribution scheme improved by 19.35%, 4.97%, and 62.23% under the three objectives, respectively, indicating a more significant optimization effect.ConclusionsBased on the actual demand of enterprises for optimizing automatic steel plate warehouse loading decisions, the PCDMOPSO algorithm has demonstrated good performance in the simulation test of solving the stack allocation model. …”
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Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models by Alexander Bott, Moritz Siems, Alexander Puchta, Jurgen Fleischer

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Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach by Debendra Muduli, Syed Irfan Yaqoob, Santosh Kumar Sharma, Anuradha S. Kanade, Mohammad Shameem, Harendra S. Jangwan, P.M. Ashok Kumar, Abu Taha Zamani

“…Finally, an improved gray wolf optimization algorithm integrated with an extreme learning machine (IMGWO-ELM) classifies the images as either healthy or glaucomatous. …”
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Encapsulation of anticancer drugs into carbon nanotubes: Heuristic algorithm approach and mathematical model. by Kanes Sumetpipat, Duangkamon Baowan

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Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm by Fen Yang

“…The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. …”
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Intelligent interference decision algorithm with prior knowledge embedded LSTM-PPO model by ZHANG Jingke, YANG Kai, LI Chao, WANG Hongyan

“…Focusing on the issues of low efficiency and effectiveness in decision-making as well as the instability of traditional reinforcement learning model-based multi-function radar (MFR) jamming decision algorithms, a prior knowledge embedded long short-term memory (LSTM) network-proximal policy optimization (PPO) model based intelligent interference decision algorithm was developed. …”
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Improved PICEA-g-based multi-objective optimization scheduling method for distribution network with large-scale electric vehicles by Meiyi Huo, Songling Pang, Hailong Zhao

“…Abstract Large-scale electric vehicle access to the distribution grid for charging can affect the security and economic operation of the grid. In this paper, an optimal scheduling method for large-scale EV access to the distribution grid based on the improved preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) is proposed. …”
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Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation by Matej Črepinšek, Miha Ravber, Luka Mernik, Marjan Mernik

“…Furthermore, evaluations on standard benchmarks without blind spots, such as CEC’15 and the soil model problem, confirm that LTMA+ maintains strong optimization performance without introducing significant computational overhead.…”
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Locality-guided based optimization method for bounded model checker by Shun WANG, Ye DU, Zhen HAN, Jiqiang LIU

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Direct methanol fuel cells parameter identification with enhanced algorithmic technique by Manish Kumar Singla, Muhammed Ali S.A, Ramesh Kumar, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…The parameter of a direct methanol fuel cell (DMFC) can be identified using optimization techniques to determine the optimal unknown parameter values that are needed for creating an accurate fuel cell performance prediction model. …”
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An enhanced multi-objective reactive power dispatch for hybrid Wind-Solar power system using Archimedes optimization algorithm by Prisma Megantoro, Syahirah Abd Halim, Nor Azwan Mohamed Kamari, Lilik Jamilatul Awalin, Ramizi Mohamed, Hazwani Mohd Rosli

“…This paper proposes a solution to the ORPD problem in systems with RE-DG integration using the Archimedes Optimization Algorithm (AOA). The uncertainties of wind and solar power generation were modelled using Weibull and lognormal probability density functions (PDFs), respectively, and the optimization model was tested using a scenario-based method. …”
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A study on the prediction of mountain slope displacement using a hybrid deep learning model by Yuyang Ma, Xiangxiang Hu, Yuhang Liu, Yaya Shi, Zhiyuan Yu, Xinmin Wang, Liangbai Hu, Shuailing Liu, Dongdong Pang

“…The method employs an Improved Whale Optimization Algorithm (IWOA) to fine-tune parameters for GNSS data fitting, ensuring accurate signal feature extraction. …”
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Multi-objective DG placement in radial distribution systems using the IbI logic algorithm by Nitin Saxena, M Pandit, L Srivastava

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A Computationally Efficient Iterative Algorithm for Estimating the Parameter of Chirp Signal Model by Jiawen Bian, Jing Xing, Zhihui Liu, Lihua Fu, Hongwei Li

“…A novel iterative algorithm is proposed to estimate the frequency rate of the considered model by constructing the iterative statistics with one-lag and multilag differential signals. …”
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Volute Optimization Based on Self-Adaption Kriging Surrogate Model by Fannian Meng, Ziqi Zhang, Liangwen Wang

“…Optimizing the volute performance can effectively improve the efficiency of a centrifugal fan by changing the volute geometric parameter, so the self-adaption Kriging surrogate model is used to optimize the volute geometric parameter. …”
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An orderly charging and discharging strategy for electric vehicles based on cooperative game and dynamic time-of-use pricing by Zhengyu SHU, Wencan LIU, Huangqiang LI, Can WANG, Qin YAO

“…In the solution process, an improved fruit fly optimization algorithm (FOA) is uesd to plan the charging periods of electric vehicles. …”
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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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