Improved grey wolf optimization algorithm based service function chain mapping algorithm by Yue ZHANG, Junnan ZHANG, Xiaochun WU, Chen HONG, Jingjing ZHOU

“…With the rise of new Internet applications such as the industrial Internet, the Internet of vehicles, and the metaverse, the network’s requirements for low latency, reliability, security, and certainty are facing severe challenges.In the process of virtual network deployment, when using network function virtualization technology, there were problems such as low service function chain mapping efficiency and high deployment resource overhead.The node activation cost and instantiation cost was jointly considered, an integer linear programming model with the optimization goal of minimizing the average deployment network cost was established, and an improved grey wolf optimization service function chain mapping (IMGWO-SFCM) algorithm was proposed.Three strategies: mapping scheme search based on acyclic KSP algorithm, mapping scheme coding and improvement based on reverse learning and nonlinear convergence were added to the standard grey wolf optimization algorithm to form this algorithm.The global search and local search capabilities were well balanced and the service function chain mapping scheme was quickly determined by IMGWO-SFCM.Compared with the comparison algorithm, IMGWO-SFCM reduces the average deployment network cost by 11.86% while ensuring a higher service function chain request acceptance rate.…”
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Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm. by Xiaotong Bai, Yuefeng Zheng, Yang Lu, Yongtao Shi

“…In particular, the wrapper algorithm is an improved Grey Wolf Optimization (IGWO) algorithm based on random disturbance factors, while the parameters are adjusted to vary randomly to make the population variations rich in diversity. …”
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Minimum-cost network hardening algorithm based on stochastic loose optimize strategy by Guang-sheng ZHAO, Qing-feng CHENG, Yong-lin SUN

“…To construst a minimum-cost network hardening (MCNH) scheme in large-scale network,a stochastic loose optimize strategy based algorithm (MCNHA-SLOS) was proposed,and its effectiveness was analyzed.MCNHA-SLOS was a near-optimal approximation algorithm,which could achieve iterative computations in the array of sparse spaces of the whole plan space,so that the near-optimal scheme must exist in the low cost plan space.Instantiation analysis and experimental results show that the MCNHA-SLOS algorithm to be efficient,precision controllable and asymptotically optimal,and thus very applicable for large-scale network.…”
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Research of the Parameter Comprehensive Optimization of Excavator Working Device based on the Hybrid Optimization Algorithm by Zhang Xian, Liu Baixi, Qu Tao

“…The parameter of working device in the anti shovel hydraulic excavator are integrated optimized by using the hybrid algorithm of simulated annealing and penalty function. …”
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Application of Improved Whale Algorithm to Optimize Dephosphorization Process Parameters in Converter Steelmaking by Congrui Wu, Yueping Kong

“…This study addresses process parameter optimization challenges in converter steelmaking by proposing an improved multi-objective whale optimization algorithm (IMOWOA) that synergistically integrates metallurgical thermodynamics with data-driven modeling. …”
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Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation by Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho

“…This confirms that employing Genetic Algorithms (GA) to automate the training and optimization of the model is effective and enhances the neural network's capacity to predict ETo.…”
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Microservice combination optimisation based on improved gray wolf algorithm by Jingjing Hu, Xiaojun Xu, Jin Hao, Xiuqi Yang, Kefan Qiu, Yuanzhang Li

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An evolutionary-based optimization algorithm for truss sizing design by Pham Hoang Anh

“…Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. …”
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Multiplier leadership optimization algorithm (MLOA): unconstrained global optimization approach for melanoma classification by Sukanta Ghosh, Amar Singh, Shakti Kumar

“…Abstract This paper proposes the multiplier leadership optimization algorithm, which draws inspiration from multiplier leadership principles to search for and optimize solutions to complex problems effectively. …”
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Optimization of Jaw Crusher Based on Improved Genetic Algorithm by SUN Yu-fei, ZHENG Xiao-wen, CHEN Hao, LIU Xiao-Shan

“…Aiming at the deficiency of ANSYS′s own optimization algorithm, the improved genetic algorithm is applied to the structural optimization design of ANSYS.Taking the whole structure optimization of jaw crusher as an example, the optimization platform of jaw crusher based on SolidWorks and ANSYS is established.The traditional optimization algorithm of ANSYS and the improved genetic algorithm with embedded ANSYS finite element analysis are used to optimize the design.The results show that compared with the optimization algorithm of ANSYS, the improved genetic algorithm has obvious advantages in the overall structure design of jaw crusher. …”
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Optimal Placement of Phasor Measurement Unit in Electrical Grid Using Dingo Optimization Algorithm by ARIYO Funso Kehinde, AYANLADE Samson Oladayo, JIMOH Abdulrasaq, ADEBAYO Moses Taiwo

“…The study utilizes the Dingo Optimization Algorithm, a metaheuristic inspired by nature, to identify the best PMU placement. …”
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A new human-based offensive defensive optimization algorithm for solving optimization problems by Ning Fang, Cheng Xu, Xuxiong Gong, Zhouhua Wu

“…Abstract A novel human-inspired metaheuristic algorithm, termed Offensive Defensive Optimization, has been introduced to address single-objective optimization problems. …”
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Comparative Review of Multi-Objective Optimization Algorithms for Design and Safety Optimization in Electric Vehicles by I Gede S. S. Dharma, Rachman Setiawan

“…Despite the widespread use of established optimization algorithms like Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) in real-world engineering optimization problems, newer algorithms such as Two-Stage NSGA-II (TS-NSGA-II), Dynamic Constrained NSGA-III (DCNSGA-III), MOEA/D with Virtual Objective Vectors (MOEA/D-VOV), Large-Scale Evolutionary Multi-Objective Optimization Assisted by Directed Sampling (LMOEA-DS), and Super-Large-Scale Multi-Objective Evolutionary Algorithm (SLMEA) remain underexplored in the context of Battery Electric Vehicle (BEV) safety, particularly in optimizing complex, non-linear, and constrained multi-objective problems like crashworthiness and thermal management. …”
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Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry by Murat Erhan Çimen

“…Portfolio optimization, which is performed while investing in any asset, is an important issue for all investors and finance researchers. …”
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An optimal L∞-PLA algorithm for trajectory data compression by ZHAO Huanyu, SUN Guohao, LI Tongliang, YANG Jian, PANG Chaoyi

“…MDisPLA used a divide-and-conquer strategy to extend the one-dimensional optimal PLA algorithm for optimizing compression of multi-dimensional trajectory data. …”
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Multi-strategy collaborative optimization of gravitational search algorithm by Zhonghua Yang, Yuanli Cai, Ge Li, Peng Wang, Yu Chen

“…In the early iterations, particle positions are primarily updated using the original gravitational force, preserving the inherent characteristics of the gravitational search algorithm. In the later stages, particles with better fitness values are updated using a globally optimal Lévy random walk strategy to enhance local search capabilities, while particles with poorer fitness values are updated using the sparrow algorithm follower strategy. …”
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Fast autoscaling algorithm for cost optimization of container clusters by José María López, Joaquín Entrialgo, Manuel García, Javier García, José Luis Díaz, Rubén Usamentiaga

“…In comparison to the heuristics, FCMA achieves similar solving times while consistently delivering more cost-effective solutions.…”
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Optimization of the Weight Processing Algorithm in Multichannel Doppler Filtering by V. I. Koshelev, Ngoc Hieu Trinh

“…The use of modified FFT algorithms increases the efficiency of MDF against the background of white Gaussian noise, while reducing the efficiency of signal accumulation in the part of signal processing channels blocked by the narrow-band clutter. …”
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Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA by Chunfang Li, Yuqi Yao, Mingyi Jiang, Xinming Zhang, Linsen Song, Yiwen Zhang, Baoyan Zhao, Jingru Liu, Zhenglei Yu, Xinyang Du, Shouxin Ruan

“…This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. …”
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Optimizing Pile Bearing Capacity Prediction Using Specific Random Forest Models Optimized by Meta-Heuristic Algorithms for Enhanced Geomechanically Applications by Nengyuan Chen

“…To achieve highly accurate predictions of Pile Bearing Capacity (PBC), the study employs a cutting-edge approach featuring Specific Random Forest (RF) prediction models, strategically enhanced with two potent meta-heuristic algorithms: the Snake Optimizer (SO) and the Equilibrium Optimizer (EO). …”
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