MTDE: Multi-trial vector-based differential evolution

MTDE uses an adaptive movement step designed based on a new multi-trial vector approach named MTV, which combines different search strategie
453 téléchargements
Mise à jour 2 nov. 2020

Afficher la licence

Multi-trial vector-based differential evolution (MTDE) is distinguished by introducing an adaptive movement step designed based on a new multi-trial vector approach named MTV, which combines different search strategies in the form of trial vector producers (TVPs). In the developed MTV approach, the TVPs are applied on their dedicated subpopulation, which are distributed by a winner-based distribution policy, and share their experiences efficiently by using a life-time archive. The MTV can be deployed by different types of TVPs, particularly, we use the MTV approach in the MTDE algorithm by three TVPs: representative based trial vector producer, local random based trial vector producer, and global best history-based trial vector producer.

The source code has been developed in Prof. Nadimi's research group and belongs to the following paper:

Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., & Faris, H. (2020). MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems. Applied Soft Computing, 106761, doi: https://doi.org/10.1016/j.asoc.2020.106761

More information can be found here: https://seyedalimirjalili.com/de

Citation pour cette source

Seyedali Mirjalili (2024). MTDE: Multi-trial vector-based differential evolution (https://www.mathworks.com/matlabcentral/fileexchange/82149-mtde-multi-trial-vector-based-differential-evolution), MATLAB Central File Exchange. Extrait(e) le .

Nadimi-Shahraki, Mohammad H., et al. “MTDE: An Effective Multi-Trial Vector-Based Differential Evolution Algorithm and Its Applications for Engineering Design Problems.” Applied Soft Computing, vol. 97, Elsevier BV, Dec. 2020, p. 106761, doi:10.1016/j.asoc.2020.106761.

Afficher d’autres styles
Compatibilité avec les versions de MATLAB
Créé avec R2020b
Compatible avec toutes les versions
Plateformes compatibles
Windows macOS Linux

Community Treasure Hunt

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

Start Hunting!
Version Publié le Notes de version
1.0.2

Link updated

1.0.1

Paper included: https://doi.org/10.1016/j.asoc.2020.106761

1.0.0