liuyifei0615 Goto Github PK

3y

3y原创技术文章导航

910conveniencesite

910便利网源码

ant4cloud

蚁群算法相关，cloudsim和workflowsim的源码

antcolonyalgorithm

蚁群算法的JS实现

best-websites-a-programmer-should-visit-zh

程序员应该访问的最佳网站中文版

bp-tabu-for-cloudsim

cloud_simulation_project

WorkFlow Scheduling Using Hybrid Genetic Algorithm - Particle Swarm Optimization Algorithm.

cloudanalyst

cloudsim

cloudsim for Energy Efficient Resource Scheduling In Big Data Computing Environments

cloudsim-plus

CloudSim Plus: A Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services that focuses on reusability principles and other ones such as DRY, KISS, SOLID and Design Patterns

cloudsim-plus-automation

Human Readable Scenario Specification for Automated Creation of Simulations on CloudSim Plus and CloudSim

cloudsim_origin_annotation

cloudsimdisk

A new storage modeling for CloudSim simulator.

cloudsimsec

CloudSimSEC is an extension of Cloudsim specifically designed to simulate the energy consumption of cloud data centers.

cs249_workflow_project

CS249 group project. Implementing PSO scheduling algorithms on CloudSim enviornment

docker-schedule-simula

基于遗传算法的docker集群调度策略的模拟

easy-springboot

springboot2简化版项目

evolopy

fdo-java

this is a MATLAB implementation of a novel swarm intelligent algorithm, known as the fitness dependent optimizer (FDO). The bee swarming reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. It is worth mentioning that FDO is considered a particle swarm optimization (PSO)-based algorithm that updates the search agent position by adding velocity (pace). However, FDO calculates velocity differently; it uses the problem fitness function value to produce weights, and these weights guide the search agents during both the exploration and exploitation phases. FDO is tested on a group of 19 classical benchmark test functions, and the results are compared with three well-known algorithms: PSO, the genetic algorithm (GA), and the dragonfly algorithm (DA), additionally, FDO is tested on IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC-C06, 2019 Competition) The results are compared with three modern algorithms: (DA), the whale optimization algorithm (WOA), and the salp swarm algorithm (SSA). The FDO results show better performance in most cases and comparative results in other cases. implementation of the mentioned Benchmark function is exist in the code.

forestblog

一个简单漂亮的SSM(Spring+SpringMVC+Mybatis)博客系统

fullstack-tutorial

🚀 fullstack tutorial 2019，后台技术栈/架构师之路/全栈开发社区，春招/秋招/校招/面试

ga-pso

Implementation of the hybrid algorithm GA-PSO

ga_pso

MATLAB

generator-workflow

Yeoman generator for tmt-workflow

geneticalgorithm

遗传算法的JS实现

gwo

Grey Wolf Optimizer (+ PSO for comparison) algorithm implementation in C++ with Python bindings

gwo-1

Grey Wolf Optimizer Matlab

ifogsim

The iFogSim Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments

ifogsimpopularityplacement

ifogsimwithdataplacement