Intelligent Algorithm Scheduler

genetic algorithm for Cloud Scheduler:

simulate.py
./schedulers/GAScheduler.py

genetic algorithm for FJSP

JSSP_executor.py
./fjspkits/Genetic4FJSP.py

Introduction

This repository provides two models currently.

Solving FJSP using Intelligent Algorithm(Genetic Algorithm), and draw gantt plot.

python ./JSSP_executor.py

This project is dedicated to solving cloud computing scheduling problems using intelligent algorithms. It can replace evaluation functions, plot and store results and charts. It should be noted that all are discrete scheduling problems, which means that all particle swarm optimization algorithms are discrete particle swarm optimization algorithms. In addition, the Optimization on Graph and Flexible Job-Shop Scheduling Problem are developing.

python ./simulate.py

Code Structure

root
└─JSSP_executor.py
└─simulate.py: For Cloud Scheduling, This code can be run to obtain results, and algorithms, data, and parameters can be changed within it.
└─schedulers/
│    └─GAScheduler.py, Genetic Algorithms to Cloud Service Scheduling.
│    └─DPSOTaskScheduler.py, Particle Swarm Algorithms to simulating.
│    └─SAScheduler.py, Simulating Anneal Algorithms to simulating.
│    └─ACScheduler.py, Ant Colony Algorithms to simulating.
│    └─TabooSearchScheduler.py, Taboo Search Algorithms to simulating.
│    └─*.py, Support Algorithms to simulating.
└─utils/
│    └─Entities : This file includes some entities that could tasks need. such as Cloudlet(cloud tasks to allocated), VM(containers Virtual Machines to execute tasks(cloudlets)).
│    └─plottools.py: some functions for plotting, such gantt plot.
└─SchedulerScaleandFitness.py: This file is used to compare the optimal solutions of multiple algorithms and their convergence curves.
└─Schedulers.py: Similar to the above, it includes comparative experiments for different groups.
└─chaosTest.py: Similar to the above, it includes comparative experiments for different groups.

Not every algorithm has a paper, as some are my own improvement attempts.

GA: Genetic Algorithm
SA: Simulated Annealing Algorithm
ACO: Ant Colony Optimization Algorithm
PSO: Particle Swarm Optimization Algorithm
CRPSO: Chaotic Hierarchical Gene Replication
DPSO: discrete PSO
CDPSO: Chaotic PSO
TSA: Taboo Search Algorithm
others
ChaosDPSO(DPSO based on Chaos)
ChaosHPSO(DPSO based on Chaos and hierarchical)
newPSO(modified PSO)

utils/*.py: tools The remaining codes that have not been introduced are utility classes or abandoned code from that year.

Example