TRIDENT-DE: Triple-Operator Differential Evolution with Adaptive Restarts and Greedy Refinement

Vasileios Charilogis, Ioannis G. Tsoulos () and Anna Maria Gianni
Additional contact information
Vasileios Charilogis: Department of Informatics and Telecommunications, University of Ioannina, Kostaki, 47150 Artas, Greece
Ioannis G. Tsoulos: Department of Informatics and Telecommunications, University of Ioannina, Kostaki, 47150 Artas, Greece
Anna Maria Gianni: Department of Informatics and Telecommunications, University of Ioannina, Kostaki, 47150 Artas, Greece

Future Internet, 2025, vol. 17, issue 11, 1-30

Abstract: This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/bin, current-to-best/1/bin, and pbest/1/bin executed within a self-adaptive framework that employs jDE parameter control. To prevent stagnation and premature convergence, TRIDENT-DE incorporates adaptive micro-restart mechanisms, which periodically reinitialize a fraction of the population around the elite solution using Gaussian perturbations, thereby sustaining exploration even in rugged landscapes. Additionally, the algorithm integrates a greedy line-refinement operator that accelerates convergence by projecting candidate solutions along promising base-to-trial directions. These mechanisms are coordinated within a mini-batch update scheme, enabling aggressive iteration cycles while preserving diversity in the population. Experimental results across a diverse set of benchmark problems, including molecular potential energy surfaces and engineering design tasks, show that TRIDENT-DE consistently outperforms or matches state-of-the-art optimizers in terms of both best-found and mean performance. The findings highlight the potential of multi-operator, restart-aware DE frameworks as a powerful approach to advancing the state of the art in global optimization.

Keywords: Differential Evolution; metaheuristics; regenerative computing; hybridization; evolutionary algorithms; global optimization; mutation strategies (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/11/488/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/11/488/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:11:p:488-:d:1779367

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().