Optimizing Ontology Alignment through Improved NSGA-II by Yikun Huang, Xingsi Xue, Chao Jiang

“…Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. …”
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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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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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An improved electrocardiogram arrhythmia classification performance with feature optimization by Annisa Darmawahyuni, Siti Nurmaini, Bambang Tutuko, Muhammad Naufal Rachmatullah, Firdaus Firdaus, Ade Iriani Sapitri, Anggun Islami, Jordan Marcelino, Rendy Isdwanta, Muhammad Ikhwan Perwira

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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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Simulated Annealing-Based Krill Herd Algorithm for Global Optimization by Gai-Ge Wang, Lihong Guo, Amir Hossein Gandomi, Amir Hossein Alavi, Hong Duan

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Simple gravitational particle swarm algorithm for multimodal optimization problems. by Yoshikazu Yamanaka, Katsutoshi Yoshida

“…Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). …”
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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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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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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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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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A Review of Stochastic Optimization Algorithms Applied in Food Engineering by Laís Koop, Nadia Maria do Valle Ramos, Adrián Bonilla-Petriciolet, Marcos Lúcio Corazza, Fernando Augusto Pedersen Voll

“…It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. …”
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A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition by Cai Dai, Xiujuan Lei

“…Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO/D) to improve the search efficiency. …”
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An Optimal Algorithm for Renewable Energy Generation Based on Neural Network by Weihua Zhao, Imran Khan, Shelily F. Akhtar, Mujahed Al-Dhaifallah

“…The results show that the proposed algorithm has technological applications and may greatly improve prediction accuracy.…”
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Detection of Plants Leaf Diseases using Swarm Optimization Algorithms by Saud Abdul Razzaq, Baydaa Khaleel

“…In this paper, a new method is proposed to classify and distinguish a group of eight different plants to healthy and unhealthy based on the leaf images of these plants They are apples, cherries, grapes, peaches, peppers, potatoes, strawberries, and tomatoes using a hybrid optimization algorithm. In the first stage, the plant leaf images were collected and pre-processed to remove noise and improve contrast. …”
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