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This toolbox offers advanced feature selection tools. Several modifications, variants, enhancements, or improvements of algorithms such as GWO, FPA, SCA, PSO and SSA are provided.
Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.
All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.
Abdel-Basset, M., El-Shahat, D., El-henawy, I., de Albuquerque, V. H. C., & Mirjalili, S. (2019). A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Systems with Applications, 112824. doi:10.1016/j.eswa.2019.112824
贵校课程资料民间整理
MATLAB sample codes for mobile robot navigation
Solves collision free shortest path planning problem for a mobile robot in a 2D static environment using Genetic Algorithm
Yuan, X., Miao, Z., Liu, Z., Yan, Z., & Zhou, F. (2020). Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis. Applied Sciences, 10(11), 3667. doi:10.3390/app10113667
东北大学机器人课程攻略 共享计划
📝 南哪课程复习资料 Review materials for NJU
学习强国 xuexiqiangguo 全网最好用学习强国助手:Panda_Learning 萌萌的熊猫帮你搞定学习强国
路径规划算法
A multi-objective problem based on MOEA/D algorithm.
Common used path planning algorithms with animations.
Field oriented Controller has been designed for Permanent Magnet Synchronous Motor.
FOC control of a PMSM motor with Luenberger estimator and Kalman filter. The purpose of these is to estimate the load torque.
Great repository to learn PMSM control and modelling. It provides the basic tools to generate control calibrations based on the motor parameters, to tune the PID controllers within the controller, and to simulate both the controller and the plant.
Python sample codes for robotics algorithms.
path规划
上海交通大学课程资料分享
Examples programs for STM32F4Discovery. These examples were written while I was exploring STM32F407VGT microcontroller. I think this is may be helpful somebody.
STM32F407 F103ZGT6的核心板 淘宝太贵所以自己抄了一份 欢迎使用
Keil projects and libraries for STM32F4xx devices
收集整理SYSU期末考试卷子、资料
2D and 3D Environment in UAV path planning problems using MATLAB R2016a
**科学院大学研一课程课件共享项目University of Chinese Academy of Sciences postgraduate course textbook sharing project
:heart:**科学技术大学课程资源
This repository includes the same codes for my paper "Whale Optimization Algorithm with Applications to Resource Allocation in Wireless Networks"
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Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
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Data-Driven Documents codes.
China tencent open source team.