Emerging Research in Optimization Algorithms in the Era of Big Data

Dear Colleagues,

In the era of big data, where the volume, velocity, and variety of data are increasing exponentially, the development of efficient optimization algorithms is an important goal. This Special Issue explores the dynamic landscape of optimization algorithms tailored to the challenges of big data analytics and presents the latest advances and emerging trends. From traditional optimization techniques to cutting-edge machine learning algorithms and metaheuristic approaches, the range of contributions highlights the diversity and richness of current research efforts. The convergence of computing power, advanced algorithms, and huge datasets has led to a renaissance of optimization methods and produced innovative approaches for solving complex optimization problems in big data applications. This collection covers a wide range of topics, including evolutionary algorithms, genetic programming, swarm intelligence, nature-inspired optimization techniques, parallel and distributed optimization algorithms optimized for the Cloud, deep learning-based optimization strategies, hybrid optimization frameworks, and optimization algorithms for real-time processing of big data and streaming analytics. In addition, applications of optimization algorithms are explored in various areas, such as healthcare, finance, transportation, and cybersecurity, incorporating advances in generative AI to improve optimization capabilities in cloud-based environments. Through this compilation, researchers and practitioners will gain insights into the latest methodologies, challenges, and opportunities in the field of optimization algorithms for big data analytics that drive innovation and enable transformative breakthroughs in data-driven decision making. The interdisciplinary nature of these contributions emphasizes the collaboration between computer science, mathematics, engineering, and various domain-specific disciplines. This Special Issue is a testament to the vibrant research community dedicated to advancing optimization algorithms in the context of big data analytics.

Dr. Marko Gulić
Prof. Dr. Heming Jia
Prof. Dr. Antonio Jiménez-Martín
Guest Editors

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• optimization algorithms
• big data analytics
• machine learning algorithms
• metaheuristic approaches
• evolutionary algorithms
• genetic programming
• swarm intelligence
• nature-inspired optimization techniques
• parallel and distributed optimization algorithms