Addressing model errors in UAV altitude control using compensator by Gilang Nugraha Putu Pratama, Alfian Maarif, Iswanto Iswanto, Evi Wahyu Pratiwi

“…This study proposes a model error compensator designed to mitigate errors arising from parametric uncertainties using the Particle Swarm Optimization (PSO) algorithm. Simulation results confirm that the proposed compensator effectively reduces response variations caused by uncertainties and time delays, demonstrating its potential to enhance the reliability of altitude control in UAV quadrotors.…”
Get full text

Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks by Davide Caputo, Francesco Grimaccia, Marco Mussetta, Riccardo E. Zich

“…In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). …”
Get full text

Approach of glowworm swarm optimization based virtual machine placement by Shengchao XU, Maohua XIONG, Tianqi ZHOU

Get full text

Resilience-Improving Based Optimization of Post-Disaster Emergency Maintenance Strategy for Transmission Networks by Haiping LIANG, Haoyan SHI, Yan WANG, Yingpei LIU, Xinming WANG

Subjects: Get full text

A genetic programming-based ensemble method for long-term electricity demand forecasting by Hayat Ahmed Issa, Hasan Hüseyin Çevik, Ahmet Yilmaz, Mehmet Cunkas

Subjects: Get full text

Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm by Lilin Li, Qing He, Hua Wang, Wensheng Wang

“…This study proposes a novel hybrid model, DIWPSO-SVM, which integrates dynamic inertia weight particle swarm optimization (DIWPSO) with support vector machines (SVMs) to enhance the prediction accuracy of midspan deflection. …”
Get full text

Energy Efficiency Optimization in Swarm Robotics for Smart Photovoltaic Monitoring by Dimitris Ziouzios, Nikolaos Baras, Minas Dasygenis, Vayos Karayannis, Constantinos Tsanaktsidis

Get full text

Public security patrol path planning recommendation method based on wolf-pack optimization algorithm using DAF and BRS by Dongxing Wang, Gaomin Zhang, Fangbo Wu, Zhiyang Huang, Yiping Chen

Get full text

Two-Stage Uncertain UAV Combat Mission Assignment Problem Based on Uncertainty Theory by Haitao Zhong, Rennong Yang, Aoyu Zheng, Mingfa Zheng, Yu Mei

Subjects: Get full text

A new method for recognizing geometric parameters of industrial robots by Bin Kou, Yi Zhang

“…Following this, we improve the accuracy of global optimization of the particle swarm optimization (PSO) algorithm by drawing on the wandering behavior of the wolf pack algorithm and hybridization behavior of the genetic algorithm. …”
Get full text

Wireless sensor node localization based on IPSO-MC by Yongyan LI, Jianping WU

“…To solve the problem of insufficient node positioning accuracy in wireless sensor networks,an algorithm based on improved particle swarm optimization by membrane computing (IPSO-MC) was proposed.Kent mapping was used to initialize the population and domain particles were introduced to improve the global optimization of the particle swarm.The weight factor and nonlinear extreme value perturbation were used to improve the local optimization ability of the particle swarm,and the Levy flight mechanism was used to optimize the individual position.Finally,the optimal solution of the particle swarm algorithm was obtained by the evolutionary rules of the membrane computing.Simulation experiments show that compared with the chicken flock algorithm,the improved particle swarm algorithm and the membrane computing,the proposed algorithm improves 3.24%,5.12% and 8.15% in the comparison of reference node ratio indicators,and the increase in the number of nodes indicators by 2.26%,7.82% and 9.81%,and the comparison of communication radius indicators increased by 2.15%,5.5% and 7.5%,respectively.This indicates that the algorithm has a good effect in node localization.…”
Get full text

Parameter Optimization Design of Light induction Power-taking Device for Transmission Cable by LIN Jin-shu, ZHOU Mao-xi, CHEN Dao-mo, WANG Lu-yang, SUI Li-cheng

“…Increased stress and longterm operation will endanger the safety of the power system Therefore, based on the premise of reducing the weight of the magnetic core, this paper adopts the particle swarm algorithm to globally optimize the magnetic core size, air gap size and the number of turns of the coil. …”
Get full text

Multi-objective operation optimization method of microgrid considering the influence of electric vehicle by Tiefeng Xu, Xiaofang Meng, Fangfang Zheng, Yiduo Zhang, Xin Wu, Mingyang Li

“…Taking the minimum total operating cost and the minimum peak-valley difference of the microgrid in one day as the optimization objective, and considering many constraints such as power balance constraints and output constraints of distributed generation units, the multi-objective optimization function is transformed into a single-objective optimization function by linear weighting method, and the model is solved by particle swarm optimization algorithm. …”
Get full text

Sensor Node Deployment Optimization for Continuous Coverage in WSNs by Haris Muhammad, Haewoon Nam

“…To address these issues, this paper presents a novel velocity-scaled adaptive search factor particle swarm optimization (VASF-PSO) algorithm that integrates dynamic mechanisms to enhance population diversity, guide the search process more effectively, and reduce uncovered areas. …”
Get full text

Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems by Sunilkumar P. Agrawal, Pradeep Jangir, Arpita, Sundaram B. Pandya, Anil Parmar, Mohammad Khishe, Bhargavi Indrajit Trivedi

“…Flexible AC Transmission System (FACTS) devices, including Static VAR Compensator (SVC), Thyristor‐Controlled Series Compensator (TCSC), and Thyristor‐Controlled Phase Shifter (TCPS), enhance system stability, reduce losses, and lower operational costs when optimally placed. Conventional optimization techniques like Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) struggle to balance exploration and exploitation in complex OPF problems, leading to suboptimal solutions. …”
Get full text

Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization by Lei CHEN, Shi-zhong GAN, Li-yi ZHANG, Guang-yan WANG

Get full text

Enhancing Support Vector Classification for Diabetes Prediction with Novel Optimization Algorithms of Intelligent Health Services by Debojani Paul Chowdhury, Aditi Paul Chowdhury, Apurba Das, Pinki Pinki

Get full text

Deephive: A Reinforcement Learning Approach for Automated Discovery of Swarm-Based Optimization Policies by Eloghosa Ikponmwoba, Opeoluwa Owoyele

Get full text

Viewpoint Selection for 3D Scenes in Map Narratives by Shichuan Liu, Yong Wang, Qing Tang, Yaoyao Han

“…Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. …”
Get full text

A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques by Peng Chen, Yonghong Xie, Pei Jin, Dezheng Zhang

“…Due to the fact that the “negative samples” of coal mine safety data are scarce, least squares support vector machine is introduced to deal with this problem. In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. …”
Get full text