Skip to content
/ DMOES Public
forked from MaOEA/DMOES

Multi-Objective Evolution Strategy for Dynamic Multi-objective Optimization

License

Notifications You must be signed in to change notification settings

JaywayXu/DMOES

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

18 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DMOES

Multi-Objective Evolution Strategy for Dynamic Multi-objective Optimization

This paper presents a novel evolution strategy based evolutionary algorithm, named DMOES, which can efficiently and effectively solve multi-objective optimization problems in dynamic environments. DMOES can track the new approximate Pareto set and approximate Pareto front as quickly as possible when the environment changes.

Alt text

Alt text

In addition, DMOES can obtain a well-converged and well-diversified Pareto front with much less population size and far lower computational cost. The larger the number of individuals, the sharper the contour of the resulted approximate Pareto front will be.

Alt text

Copyright (c) 2018-2020 MaOES Group. You are free to use the Programe for research purposes.

All publications which use this Programe or any code in the Programe should acknowledge the use of "MaOES" and reference "Kai Zhang, Chaonan Shen, Xiaoming Liu, Gary G. Yen, Multi-Objective Evolution Strategy for Dynamic Multi-objective Optimization, IEEE Transactions on Evolutionary Computation, 2020, DOI: 10.1109/TEVC.2020.2985323"

https://ieeexplore.ieee.org/document/9055401

About

Multi-Objective Evolution Strategy for Dynamic Multi-objective Optimization

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • PostScript 100.0%