Optimal PID controller with evolutionary optimization algorithms for rehabilitation robots by Kamran Joyo, Talha Ahmed Khan, Kushsairy Abdul Kadir, Mohd Nizam Bin Husen, Haidawati Mohamad Nasir, Munaza Azhar

“…The Firefly algorithm also delivered promising results, achieving the shortest rise time (0.689 s) and a quick settling time (1.08 s), while keeping the overshoot (4.45%) nearly on par with PSO. …”
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Statistical Analysis Method for Optimal Prameters of Telemetry Communication by XIE Jun, WU Sailong, HAO Hang, ZHANG Songwei, WU Ruiqing, SUN Xiangyang

“…In addition, the proposed method completes a parameter optimization time of merely 0.08 seconds when processing 350 000 logging data points, demonstrating significant superiority over the existing optimization method based on machine learning and genetic algorithm. …”
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An innovative genetic algorithm-based master schedule to optimize job shop scheduling problem by Thi Phuong Quyen Nguyen, Thi Hoa Thuong Le

“…Consequently, the objective of this study is to develop an advanced method to optimize the production scheduling problem. Accordingly, this study proposes an innovative genetic algorithm-based master schedule (GA-MS) focused on minimizing makespan. …”
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Sigmoid-Function-Based Adaptive Pelican Optimization Algorithm for Global Optimization by A. F. S. Yussif, S. Adjei, B. E. Wilson

“… This paper introduces the Sigmoid-function-based Adaptive Pelican Optimization Algorithm (MPOA), an enhanced version of the traditional Pelican Optimization Algorithm (POA) aimed at improving the POA's performance. …”
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UWB Base Station Deployment Optimization Method Considering NLOS Effects Based on Levy Flight-Improved Particle Swarm Optimizer by Shengliang Wang, Ming Gao, Ling’ai Li, Dong Lv, Yingqi Li

“…In this paper, we propose a new UWB BS deployment optimization method that takes into account the influence of NLOS occlusion, determines the BS deployment range and occlusion by indoor map information, uses the locatable points coverage rate in the whole indoor positioning area as the fitness function, and proposes an improved particle swarm optimization algorithm based on the Levy flight strategy (LPSO) to solve the optimization problem. …”
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Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption by Kürşat Ayan, Abdal Kasule

“…We use particle swarm optimization (PSO) algorithm and genetic algorithm (GA) to optimize the parameters of the model. …”
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SOMO-m Optimization Algorithm with Multiple Winners by Wei Wu, Atlas Khan

“…More importantly, SOMO-m algorithm with m≥2 can be used to find two or more minimums simultaneously in a single learning iteration process, while the original SOM-based optimization (SOMO) algorithm has to fulfil the same task much less efficiently by restarting the learning iteration process twice or more times.…”
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Metaheuristic Algorithms for Optimization: A Brief Review by Vinita Tomar, Mamta Bansal, Pooja Singh

“…In the area of optimization, metaheuristic algorithms have attracted a lot of interest. …”
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ＲGB-DSLAM Visual Odometry Optimization Algorithm by DONG Jing-wei, JIANG Yi-ming, HAN Zhi-yu

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Comprehensive Research on Energy Saving Synergy Optimization and Optimal Real-time Control of Hybrid Electric Mining Trucks by Li Hongliang, Zhang Guojing, Fan Ping

“…This study takes the hybrid electric mining truck as a research object, establishes the mathematical model of the whole vehicle, takes reducing fuel consumption as the goal, establishes a collaborative optimization model that comprehensively considers the interaction between transmission system parameters and energy management strategies, and combines particle swarm optimization (PSO) algorithm and dynamic programming (DP) algorithm to build a two-layer interactive optimization algorithm to eliminate the interaction between transmission system parameters and energy management strategies. …”
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NETWORK MODEL AND ALGORITHM FOR SOLVING PROBLEM PERTAINING TO OPTIMUM DISTRIBUTION OF CAPITAL INVESTMENT WHILE MODERNIZING ENTERPRISES OF HEATING SYSTEMS by V. A. Sednin, A. D. Korznikov, А. V. Sednin

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Advancing Pile-Bearing Capacity Prediction with Meta-Heuristic Enhanced Specific ANFIS strategys by Zhongmiao Dang

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Hierarchical multi step Gray Wolf optimization algorithm for energy systems optimization by Idriss Dagal, AL-Wesabi Ibrahim, Ambe Harrison, Wulfran Fendzi Mbasso, Ahmad O. Hourani, Ievgen Zaitsev

“…Abstract Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems in various domains, including engineering design, image processing, and machine learning. …”
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Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm by Fanghai Gong

“…After 114 iterations of the heuristic search algorithm, the optimal value is 6.83353635e-001, while the average optimal value is 6.83551939e-001. …”
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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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Bone scintigraphy based on deep learning model and modified growth optimizer by Omnia Magdy, Mohamed Abd Elaziz, Abdelghani Dahou, Ahmed A. Ewees, Ahmed Elgarayhi, Mohammed Sallah

“…In addition, the second phase is named feature selection, and it is dependent on the Arithmetic Optimization Algorithm (AOA) being used to improve the Growth Optimizer (GO). …”
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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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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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