Applications of Optimization Algorithms and Evolutionary Computation

Dear Colleagues,

This Special Issue aims to bring together original research and comprehensive review articles that explore the mathematical foundations, theoretical modeling, algorithmic developments, and wide-ranging applications of optimization algorithms and evolutionary computation. A central focus is on contributions that bridge the gap between advanced machine learning methodologies such as deep learning, reinforcement learning, generative adversarial networks, transformer-based architectures, and evolutionary or nature-inspired algorithms, including but not limited to genetic algorithms, differential evolution, swarm intelligence, and bio-inspired metaheuristics. We particularly welcome submissions that propose novel hybrid frameworks, enhance convergence behavior, improve generalization, or offer mathematically grounded performance guarantees. The integration of data-driven learning models with mathematically robust optimization techniques opens new frontiers for addressing non-convex, multi-objective, dynamic, and high-dimensional problems. Relevant application areas include the Internet of Things, intelligent and predictive healthcare, vehicular and transportation systems, wireless and mobile communications, geospatial data processing, cyber–physical systems, energy-efficient computing, and decision-making in uncertain or resource-constrained environments. This Special Issue is intended as a platform to showcase cutting-edge advances in intelligent optimization and to foster cross-disciplinary research that advances both theoretical insight and practical impact in real-world computational systems.

Prof. Dr. Diego Martín de Andrés
Guest Editor

Dr. Masoud Kaveh
Guest Editor Assistant

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