Optimization of Wheel Reprofiling Based on the Improved NSGA-II by Xinghu Wang, Jiabin Yuan, Sha Hua, Bojia Duan

“…As a method to maintain the shape at the cost of the diameter size, reprofiling has significant impacts on the lifecycle of a train. …”
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Renewable energy forecasting using optimized quantum temporal model based on Ninja optimization algorithm by Mona Ahmed Yassen, El-Sayed M. El-kenawy, Mohamed Gamal Abdel-Fattah, Islam Ismael, Hossam El.Deen Salah Mostafa

“…Abstract Artificial intelligence allows improvements in renewable energy systems by increasing efficiency while enhancing reliability and reducing costs. …”
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Well Pattern optimization as a planning process using a novel developed optimization algorithm by Seyed Hayan Zaheri, Mahdi Hosseini, Mohammad Fathinasab

“…The novelty of this work is the integrated algorithm, which improves searching performance by leveraging the memorizing characteristics of the particle swarm optimization algorithm to enhance genetic algorithm efficiency. …”
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Multi strategy Horned Lizard Optimization Algorithm for complex optimization and advanced feature selection problems by Marwa M. Emam, Mosa E. Hosney, Reham R. Mostafa, Essam H. Houssein

“…However, when applied to high-dimensional datasets characterized by a vast number of features and limited samples-these methods often suffer from performance degradation and increased computational costs. The Horned Lizard Optimization Algorithm (HLOA) is a nature-inspired metaheuristic that mathematically mimics the adaptive defense mechanisms of horned lizards, including crypsis, skin color modulation, blood-squirting, and escape movements. …”
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Edge server deployment decision based on improved NSGA-Ⅱ in the Internet of vehicles edge computing scenario by Sifeng ZHU, Yu WANG, Hao CHEN, Hai ZHU, Zhengyi CHAI, Chengrui YANG

“…In the context of the Internet of vehicles, the placement and deployment number of edge servers directly affect the efficiency of edge computing.Due to the high cost of deploying a large edge server on a macro base station and a base station, it can be complemented by deploying a small edge server on a micro base station, and the cost reduction needs to be optimized by optimizing the placement of large edge servers.In order to minimize the deployment cost and service delay of the edge server, and maximize the operator’s revenue and server load balance, the edge server placement problem combined with the vehicle networking user application service was modeled as a multi-objective optimization problem and a placement scheme based on improved NSGA-Ⅱ algorithm was proposed.The experimental results show that the proposed scheme can reduce the deployment cost of edge servers by about 44%, the latency by about 14.2%, and improve the revenue of operators by 24.2%, which has good application value.…”
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Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm by Raji Krishna, Hemamalini S

“…Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.…”
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Impact of Network Configuration on Hydraulic Constraints and Cost in the Optimization of Water Distribution Networks by Mojtaba Nedaei

“…Further, a new approach based on the Coral Reef Algorithm (CRA) is developed and implemented to improve the technical and economic viability of the designed WDNs. …”
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Building Construction Design Based on Particle Swarm Optimization Algorithm by Wenxue Song

“…When the constraint cost was 320,000 yuan, the global optimal risk loss and global optimal control cost were 910,100 yuan and 317,300, yuan respectively. …”
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Optimization of machine learning algorithms for proteomic analysis using topsis by Javanbakht T., Chakravorty S.

“…The present study focuses on a new application of the TOPSIS method for the optimization of machine learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for proteomic analysis. …”
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GNN-based optimization algorithm for joint user scheduling and beamforming by Shiwen HE, Jun YUAN, Zhenyu AN, Min ZHANG, Yongming HUANG, Yaoxue ZHANG

“…The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer, respectively.Under the consideration of user service quality requirements, the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information, a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead.…”
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Ensemble machine learning algorithm for cost-effective and timely detection of diabetes in Maiduguri, Borno State by Emmanuel Gbenga Dada, Aishatu Ibrahim Birma, Abdulkarim Abbas Gora

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Robust reinforcement learning algorithm based on pigeon-inspired optimization by Mingying ZHANG, Bing HUA, Yuguang ZHANG, Haidong LI, Mohong ZHENG

“…Reinforcement learning(RL) is an artificial intelligence algorithm with the advantages of clear calculation logic and easy expansion of the model.Through interacting with the environment and maximizing value functions on the premise of obtaining little or no prior information, RL can optimize the performance of strategies and effectively reduce the complexity caused by physical models .The RL algorithm based on strategy gradient has been successfully applied in many fields such as intelligent image recognition, robot control and path planning for automatic driving.However, the highly sampling-dependent characteristics of RL determine that the training process needs a large number of samples to converge, and the accuracy of decision making is easily affected by slight interference that does not match with the simulation environment.Especially when RL is applied to the control field, it is difficult to prove the stability of the algorithm because the convergence of the algorithm cannot be guaranteed.Considering that swarm intelligence algorithm can solve complex problems through group cooperation and has the characteristics of self-organization and strong stability, it is an effective way to be used for improving the stability of RL model.The pigeon-inspired optimization algorithm in swarm intelligence was combined to improve RL based on strategy gradient.A RL algorithm based on pigeon-inspired optimization was proposed to solve the strategy gradient in order to maximize long-term future rewards.Adaptive function of pigeon-inspired optimization algorithm and RL were combined to estimate the advantages and disadvantages of strategies, avoid solving into an infinite loop, and improve the stability of the algorithm.A nonlinear two-wheel inverted pendulum robot control system was selected for simulation verification.The simulation results show that the RL algorithm based on pigeon-inspired optimization can improve the robustness of the system, reduce the computational cost, and reduce the algorithm’s dependence on the sample database.…”
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Optimization of distribution networks using quantum annealing for loss reduction and voltage improvement in electrical vehicle parking management by Naser Rashnu, Babak Mozafari, Reza Sharifi

“…Traditional optimization techniques like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) often struggle with the nonlinear, high-dimensional nature of EV-grid interaction problems. …”
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DEVELOPMENT OF THE ALGORITHM FOR CHOOSING THE OPTIMAL SCENARIO FOR THE DEVELOPMENT OF THE REGION'S ECONOMY by I. S. Borisova

“…It was found that the rationale and choice of the optimal scenario is an important stage in the development of the sustainable development program of the regional economy, since it helps to quantify the most probable trajectories of changes in the activities of all participants in the region's economy.Conclusions and Relevance: the practical significance of the developed algorithm lies in the possibility of using it to improve the stability of the development of the economy of specific regions. …”
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Applying Genetic Algorithm for test pattern generation process optimization by V. I. Kuraedov

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Ship’s Trajectory Planning Based on Improved Multiobjective Algorithm for Collision Avoidance by Jinxin Li, Hongbo Wang, Wei Zhao, Yuanyuan Xue

“…In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain under the condition of a wide sea area without any external disturbances. …”
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Design of improved JAYA algorithm for cigarette finished product logistics delivery by Jun Wen, Yewei Hu, Le Li, Zongrui Wu, Guangwei Xiao, Kai Guo, Lei Li

“…In response to these challenges, this study proposes an improved Jaya algorithm that integrates a reverse learning mechanism and a cosine similarity strategy to enhance optimization performance. …”
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A binary grasshopper optimization algorithm for solving uncapacitated facility location problem by Ahmet Babalik, Aybuke Babadag

“…The Uncapacitated Facility Location Problem (UFLP) is a real-world binary optimization problem that aims to find the number of facilities to open, minimizing the total cost of exchange between customers and facilities, as well as the opening costs of these facilities. …”
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Path Planning of Quadrupedal Robot Based on Improved RRT-Connect Algorithm by Xiaohua Xu, Peibo Li, Jiangwu Zhou, Wenzhuo Deng

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An Improved Hybrid Genetic Algorithm with a New Local Search Procedure by Wen Wan, Jeffrey B. Birch

“…One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the tradeoff between global and local searching (LS) as it is the case that the cost of an LS can be rather high. …”
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