Abstract
The profound transformation of the agricultural sector is imperative to meet the dual challenges of ensuring global food security and adhering to the principles of environmental sustainability. Smart Agriculture, leveraging the Internet of Things (IoT), big data, robotics, and advanced sensing, generates complex, high-dimensional data and presents multifaceted optimization problems that are often dynamic, multi-objective, and constrained. Classical optimization techniques frequently struggle with these real-world agricultural systems' non-linearity, uncertainty, and scale.
This special session aims to bridge this gap by providing a dedicated platform for showcasing cutting-edge research at the intersection of computational intelligence and modern agriculture. We seek to explore how evolutionary algorithms and swarm intelligence, including but not limited to Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Differential Evolution, and Evolutionary Strategies, can provide robust, efficient, and adaptive solutions for the entire agricultural value chain. The core objective is to demonstrate how these bio-inspired algorithms can translate data into actionable intelligence, optimizing for productivity, resource efficiency, economic viability, and ecological resilience.
We invite contributions that present novel solutions and compelling case studies that clearly articulate the unique advantages of evolutionary and swarm-based methods in tackling the intricate challenges of building a smarter and more sustainable agricultural future.
Topics of Interest
The scope of this session includes both theoretical advancements and practical applications, with topics of interest including, but not limited to:
- Multi-objective optimization for site-specific irrigation, fertilization, and pesticide application
- Sensor placement and deployment strategies for optimal field monitoring
- Yield prediction and spatial data analysis using evolutionary machine learning
- Path planning and task allocation for autonomous agricultural robots (Agribots) and UAV (drone) fleets
- Evolutionary optimization of robotic harvesting and weeding maneuvers
- Optimization of harvesting schedules, storage allocation, and distribution logistics under uncertainty
- Swarm-based models for sustainable and resilient agricultural supply chains
- Feature selection and hyperparameter tuning for agricultural forecasting models
- Evolutionary algorithms for climate-resilient crop planning and genotype selection
- Constrained optimization for water-energy-food nexus management
- Swarm-based techniques for monitoring and minimizing carbon footprint and agrochemical runoff
Submission Information
Authors are invited to submit full papers via the IEEE CEC 2026 submission system following conference guidelines. Select "SS: Evolutionary and Swarm-Based Optimization in Smart and Sustainable Agriculture" as your primary topic.
- Submission deadline: January 31, 2026
- Conference dates: June 21–26, 2026
Accepted and presented papers will be published in the official IEEE CEC 2026 proceedings and submitted for inclusion in the IEEE Xplore Digital Library.
Submit Paper via IEEE CEC 2026 PortalOrganizers
Prof. Absalom El-Shamir Ezugwu
Unit for Data Science and Computing, North-West University, South Africa
Absalom.ezugwu@nwu.ac.za
Absalom El-Shamir Ezugwu is a full Professor of Computer Science in the Unit for Data Science and Computing at North-West University, South Africa, and an NRF-rated researcher. He holds a PhD in Computer Science from Ahmadu Bello University, Nigeria. With a research focus on computational intelligence, his work leverages evolutionary computation, swarm intelligence, and deep learning to solve complex global optimization problems in engineering, logistics, agriculture, and bioinformatics. Prof. Ezugwu has a distinguished record of high-impact publications and has successfully graduated numerous MSc and PhD students. He brings significant experience in organizing special sessions, having done so for premier conferences including ICONIP, IEEE CEC, and IWANN.
Prof. Dr. Diego Oliva
Universidad de Guadalajara (CUCEI), Mexico
diego.oliva@cucei.udg.mx
Diego Oliva, in 2007, obtained an Electronics and Computer Engineering degree from the Centro de Enseñanza Técnica Industrial (CETI), the Industrial Technical Education Center (CETI) of Guadalajara, Mexico, and the M.Sc. in Electronic Engineering and Computer Sciences from the Universidad de Guadalajara, Mexico in 2010. In 2015, he obtained a PhD in Informatics from the Universidad Complutense de Madrid (UCM) in Spain. Since 2008, he has focused his research on developing, implementing, and designing metaheuristic algorithms. He has published more than 100 papers in international journals on topics related to optimization and its implementations. Since 2015, he has been a member of the Academia Mexicana de Computacion (AMEXCOMP), and since 2022, he has been a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). Since 2017, he has been a member of the Sistema Nacional de Investigadoras e Investigadores (SNII) in Mexico. In 2022, he obtained the distinction of Highly Cited Researcher by Clarivate-Web of Science. He is among the 2% most influential researchers worldwide, according to a report published by Stanford University and Elsevier in 2024. He has been the editor and author of several books in international publishing houses, and he is the associate editor and guest editor for several specialized journals with high impact factors. He is currently a professor and researcher at the Universidad de Guadalajara (CUCEI). He also collaborates with Mexican and foreign universities in several research projects. His main research interests are artificial intelligence, metaheuristic optimization algorithms, multiobjective optimization, parameter estimation in engineering, and image and digital signal processing.
Program Committee
- Prof. Abduallahi Mohammed, Ahmadu Bello University, Nigeria
- Dr. Olaide Oyelade, North Carolina A&T University, USA
- Prof. Seyedali Mirjalili, Torrens University, Australia
- Prof. Amir H. Gandomi, University of Technology Sydney, Australia
- Prof. Apu K. Saha, NIT Agartala, India
- Dr. Andronicus Akinyelu Ayobami, University of KwaZulu-Natal, South Africa
- Prof. Samarjit Kar, NIT Durgapur, India
- Dr. Mario A. Navarro, Universidad de Guadalajara, Mexico
- Dr. Seyed Jalaleddin Mousavirad. Mid Sweden University, Sweden
- Dr. Folasade O. Isinkaye, Ekiti State University, Nigeria
- Prof. Erik Cuevas, Universidad de Guadalajara, Mexico
- Dr. Ikoutn M. Abiodun, University of KwaZulu-Natal, South Africa