Instructor-Researcher | Afagh Higher Education Institute | Iran
Dr. Saeid Barshandeh is a computational intelligence researcher specializing in metaheuristic optimization, machine learning, and advanced algorithm design, with a research portfolio comprising 14 scientific publications, 12 h-index and over 885 citations. His work focuses on developing innovative, nature-inspired optimization techniques capable of addressing complex, nonlinear problems prevalent in engineering, data science, and industrial systems. A key achievement is the development of the Puma Optimizer (PO), a novel metaheuristic algorithm that has rapidly gained global academic attention and demonstrates his expertise in algorithmic modeling, performance tuning, and machine-learning integration. His research activities are enriched by collaborations with more than 30 co-authors, reflecting active engagement in interdisciplinary networks spanning intelligent systems, evolutionary computation, and data-driven decision-making. Collectively, his contributions enhance the efficiency, scalability, and applicability of optimization methodologies across diverse domains such as energy management, automation, predictive analytics, and industrial process optimization. Through impactful publications and growing scholarly influence, Dr. Barshandeh advances the theoretical and practical foundations of intelligent optimization, contributing to technological innovation and the broader scientific community.
Profiles: Scopus | ORCID | Google Scholar
Featured Publications
1. Abdollahzadeh, B., Khodadadi, N., Barshandeh, S., Trojovský, P., & … (2024). Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning. Cluster Computing, 27(4), 5235–5283.
Cited by: 661
2. Barshandeh, S., & Haghzadeh, M. (2021). A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with Computers, 37(4), 3079–3122.
Cited by: 129
3. Gharehchopogh, F. S., Abdollahzadeh, B., Barshandeh, S., & Arasteh, B. (2023). A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT. Internet of Things, 24, 100952.
Cited by: 128
4. Gharehchopogh, F. S., Nadimi-Shahraki, M. H., Barshandeh, S., & … (2023). Cqffa: A chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. Journal of Bionic Engineering, 20(1), 158–183.
Cited by: 89
5. Barshandeh, S., Piri, F., & Sangani, S. R. (2022). HMPA: An innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Engineering with Computers, 38(2), 1581–1625.
Cited by: 78
Dr. Barshandeh’s research advances the science of optimization by providing robust, efficient algorithms that strengthen machine-learning performance and industrial decision systems. His work contributes to global technological innovation, enabling smarter, more adaptive solutions for complex societal and engineering challenges.
