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.
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.
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
Hybrid and Emerging Techniques
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
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.
Copyright © 2025 by the Author(s). Published by Institute of Emerging and Computer Engineers. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
IECE Transactions on Swarm and Evolutionary Learning
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