Dynamic Switched Crowding and New Multiobjective DSC-MOAGDE

Unified Space Approach-based Dynamic Switched Crowding (DSC): A New Method for Designing Pareto-based Multi/Many-Objective Algorithms
143 Downloads
Aktualisiert 17. Jan 2024

Lizenz anzeigen

DSC (Dynamic Switched Crowding)- is based on a new theory developed for the design of multi-objective optimization algorithms. DSC-MOAGDE is the first algorithm designed based on this theory. Three powerful versions of DSC-MOAGDE have been designed specifically for Real World Engineering Optimization Problems. DSC-MOAGDE can effectively find the Pareto-optimal solution set for Real World Engineering Design Problems where objective functions are in conflict.
------------------------------------------------------------------------------------------------------------------------------------------
The first implementation of DSC-based archive handling method is the DSC-MOAGDE
1) DSC-MOAGDE :
Unified space approach-based Dynamic Switched Crowding (DSC): A new method for designing Pareto-based multi/many-objective algorithms
------------------------------------------------------------------------------------------------------------------------------------------
The second implementation of DSC-based archive handling method is the DSC-MOSOS
2) DSC-MOSOS:
Combined heat and power economic emission dispatch using dynamic switched crowding based multi-objective symbiotic organism search algorithm
------------------------------------------------------------------------------------------------------------------------------------------
Dear researchers, please check this link to learn milestone methods for the design of meta-heuristic optimization algorithms and to review competitive and state-of-the-art optimization algorithms.

Zitieren als

KAHRAMAN, H. T., AKBEL, M., DUMAN, S., KATI, M., SAYAN, H. H. (2022). Unified Space Approach-based Dynamic Switched Crowding (DSC): A New Method for Designing Pareto-based Multi/Many-Objective Algorithms, Swarm and Evolutionary Computation, 101196, https://doi.org/10.1016/j.swevo.2022.101196

Kompatibilität der MATLAB-Version
Erstellt mit R2022b
Kompatibel mit allen Versionen
Plattform-Kompatibilität
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Veröffentlicht Versionshinweise
1.0.8

website url updated

1.0.7

image added

1.0.6

updated

1.0.5

link

1.0.4

description updated

1.0.3

Description was updated

1.0.2

title updated

1.0.1

title update

1.0.0