glory-ppx Goto Github PK

adl-ksvd

This source code is for our paper Qianyu Wang, Yanqing Guo, Jun Guo, and Xiangwei Kong, "Synthesis K-SVD Based Analysis Dictionary Learning for Pattern Classification," in Multimedia Tools and Applications, volume 77, issue 13, pages 17023-17041, July 2018.

algorithms_mathmodels

【国赛】【美赛】数学建模相关算法 MATLAB实现（2018年初整理）

chaotic-gsa-for-engineering-design-problems

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.

commonjavabasic

beginner java programs

cykd2

function

ziji

grey-wolf-optimizer

gwo_rbf_svm

It is a project of SVM optimization algorithm which use the Grey Wolf Optimizer

h_hydro

harris-hawks-optimization-algorithm-and-applications-

Source codes for HHO paper: Harris hawks optimization: Algorithm and applications: https://www.sciencedirect.com/science/article/pii/S0167739X18313530. In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO).

hybrid-gwopso-optimization

This script implements the hybrid of PSO and GWO optimization algorithm.

improve-deep-learning-with-pso-for-classification

Improve Deep Learning based CNN & Extreme Learning Machine With Particle Swarm Optimization (Deep PSO) For Classification

ksvd-denoising

Filtering out noise from images using K-SVD and OMP

ksvd_reg

Regularized K-SVD Algorithm

machine-learnning1

机器学习

matlabcases

Matlab Cases

mnr-adl-sr

multi-strategy-ensemble-whale-optimization-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

ppso

Source codes of Phasor Particle Swarm Optimization based on Soft Computing paper

pso-vs-woa

The Matlab/Octave code contains codes of Whale Optimization Algorithm and Particle Swarm Optimization.

radar_project_udacity

slime-mould-algorithm-a-new-method-for-stochastic-optimization-

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html

src

Main Madagascar source

useful-utilities

vocal_seperation_project_code

Vocal_seperation