AI-Driven Intelligent Optimization Algorithms and Applications, 2nd Edition

Dear Colleagues,

With the rapid advancement of artificial intelligence (AI), intelligent optimization algorithms have undergone a profound transformation, evolving from theoretical innovations to widespread practical applications as core tools for solving complex, high-dimensional, nonlinear, and dynamic optimization problems. This Special Issue aims to systematically review and showcase the latest progress in AI-driven intelligent optimization algorithms and their diverse applications across science and engineering domains. 

We focus primarily on the following cutting-edge directions:

(1) Heuristic algorithm improvements based on deep learning and reinforcement learning, including neural network-accelerated search processes, adaptive parameter tuning, and hybrid optimization strategies;

(2) Innovations in AI-assisted decision-making mechanisms for multi-objective and constrained optimization, enhancing algorithm robustness and convergence efficiency in complex real-world scenarios;

(3) The reverse empowerment of intelligent optimization algorithms in typical AI application scenarios, such as hyperparameter tuning, feature selection, neural architecture search (NAS), path planning, resource scheduling, energy management, financial portfolio optimization, and bioinformatics;

(4) Optimization algorithm design and its applications in industrial fault diagnosis;

(5) Dynamic multi-objective optimization and its applications;

(6) Automatic algorithm design and its applications;

(7) Design and application of UAV path planning and task allocation algorithms.

This Special Issue invites high-quality original research papers, review articles, and short communications covering theoretical analysis, algorithm design, empirical studies, case applications, and interdisciplinary integration. We particularly welcome research findings that reveal the synergistic effects between AI and optimization algorithms, promoting the intelligence, automation, and scalability of optimization techniques.

By gathering the wisdom of global scholars, this Special Issue seeks to establish a high-level platform for both the theoretical deepening and engineering implementation of intelligent optimization algorithms. We aim to foster collaborative innovation between AI and optimization technologies, thereby driving intelligent transformation in fields such as smart manufacturing, smart cities, green energy, and healthcare.

Dr. Haibin Ouyang
Prof. Dr. Yu Xue
Guest Editors

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