TSEL IECE IECE Transactions on Swarm and Evolutionary Learning pending pending Institute of Emerging and Computer Engineering Inc 522 W RIVERSIDE AVE STE N, SPOKANE, WA, 99201-0508, UNITED STATES 10.62762/TSEL.2025.550341 Editorial Inaugural Editorial of the Transactions on Swarm and Evolutionary Learning https://orcid.org/0000-0001-8781-7993 Oliva Diego 1 Depto. de Ingeniería Electro-Fotónica, Universidad de Guadalajara, CUCEI, Guadalajara, Mexico Corresponding Author: Diego Oliva. Email: diego.oliva@cucei.udg.mx 19 2 2025 1 1 1 2 18 2 2025 18 2 2025 © 2025 by the Author. Published by Institute of Emerging and Computer Engineers. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). 2025 Institute of Emerging and Computer Engineering Inc This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is available from https://www.iece.org/article/abs/tsel.2025.550341

Dear Readers,

The IECE Transactions on Swarm and Evolutionary Learning (TSEL) is a new journal focused on nature-inspired computation. This journal is launched at a time when swarm and evolutionary algorithms have impacted different areas of research. Their progress is not only theoretical but also applications in complex tasks, supporting their adaptability and popularity. In this sense, TSEL aims to be at the forefront of the rapidly evolving landscape of these interdisciplinary fields. On behalf of the editorial team, I warmly welcome scholars, experts, researchers, and readers who support and follow our journal.

Purpose of the Journal

The Transactions on Swarm and Evolutionary Learning aims to promote the development of swarm and evolutionary computation topics in theory and practical implementations. The idea is to publish the best research proposals that provide innovative solutions to global challenges. The TSEL encourages the search for a bridge between swarm and evolutionary intelligence and other fields of science and technology. Besides, we promote their application and hybridization with emerging technologies to address complex optimization problems.

 Scope and directions

Swarm intelligence, inspired by the collective behaviors of decentralized systems in nature, and evolutionary learning, grounded in principles of natural selection and adaptation, have opened up a wealth of possibilities for solving complex problems across various domains. From optimization challenges to robotics, from machine learning to bioinformatics, the versatility and power of these techniques have transformed the way we approach problem-solving, modeling, and decision-making.

The scope of IECE Transactions on Swarm and Evolutionary Learning includes, but is not limited to, the following areas:

Swarm Intelligence

Particle swarm optimization (PSO), ant colony optimization (ACO), bee algorithms, and other bio-inspired swarm models.

Applications of swarm intelligence in robotics, networks, and real-world optimization problems.

Hybridization of swarm algorithms with machine learning and deep learning techniques.

Evolutionary Learning and Computation

Genetic algorithms, genetic programming, evolutionary strategies, and memetic algorithms.

Advanced evolutionary learning frameworks, including co-evolutionary systems and multi-objective optimization.

Theoretical analysis, convergence properties, and parameter tuning in evolutionary systems.

Hybrid and Emerging Techniques

Integration of swarm intelligence with deep learning, reinforcement learning, and neural networks.

Hybrid meta-heuristics and optimization frameworks for large-scale problems.

Nature-inspired systems and algorithms for big data analytics and AI-driven applications.

Applications and Case Studies

Real-world applications in engineering design, smart cities, healthcare systems, transportation, and logistics.

Applications of swarm and evolutionary learning in energy systems, IoT, robotics, and industrial automation.

Benchmark studies, software, and hardware implementations for performance evaluation.

Future Directions and Emerging Challenges

Explainable and interpretable swarm intelligence and evolutionary algorithms.

Quantum-inspired optimization and learning paradigms.

Ethical considerations and environmental impact of bio-inspired computation.

Vision and Commitment

At TSEL, we believe that the synergy between swarm intelligence and evolutionary algorithms offers an unparalleled opportunity for solving real-world problems that traditional methods may struggle with. As we embark on this exciting journey, we remain committed to the highest standards of academic rigor and to maintaining a platform that is both inclusive and dynamic. Our goal is to inspire new ideas, challenge conventional wisdom, and contribute to the ongoing evolution of this vibrant field.

We look forward to engaging with the community and hope that this journal will become an essential resource for researchers and practitioners alike. Through the exchange of ideas and the publication of transformative research, TSEL aims to shape the future of swarm and evolutionary learning, making meaningful contributions to science and technology.

Thank you for joining us at the outset of this new and exciting venture. Together, we look forward to exploring the ever-expanding horizon of swarm and evolutionary learning.

 Acknowledgments

This work was supported without any funding.

 Conflict of interest

The author declare no conflicts of interest.