Guest Editors
Dr. Mohammad Shokouhifar
Email: mohammadshokouhifar@duytan.edu.vn
Affiliation: Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
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Research Interests: artificial intelligence, optimization, heuristics, metaheuristics, hyper-heuristics, just-in-time optimization, fuzzy systems, knowledge-based metaheuristics, hybrid algorithm design
Prof. Frank Werner
Email: frank.werner@ovgu.de
Affiliation: Faculty of Mathematics, Otto-von-Guericke University, P.O. Box 4120, 39016 Magdeburg, Germany
Homepage:
Research Interests: scheduling, discrete optimization, mathematical problems of operations research, exact optimization, and metaheuristic algorithms
Summary
Optimization is at the heart of solving complex problems in modern engineering, science, and technology. However, as problem complexity grows, exact optimization techniques often become impractical. In this regard, metaheuristic-driven optimization algorithms have proven to be powerful alternatives, capable of producing high-quality solutions within reasonable computational limits. Their adaptability and problem-independent nature make them ideal for tackling a wide variety of real-world, large-scale, and NP-hard problems.
Following the success of the first edition, this Second Edition of the Special Issue on "Metaheuristic-Driven Optimization Algorithms: Methods and Applications" aims to highlight the latest advances in the design, development, and application of metaheuristic algorithms. We invite researchers to contribute original studies, reviews, and practical applications that demonstrate the role of metaheuristics in solving complex optimization problems.
Topics of interests include, but are not limited to:
· Development of new single-solution metaheuristics
· Development of new population-based metaheuristics inspired by natural, physical, or social phenomena
· Development of multi-objective and many-objective optimization algorithms
· Development of hybrid heuristic–metaheuristic frameworks combining expert knowledge and intelligent search
· Integration of fuzzy logic, rough sets, or uncertainty modeling with metaheuristics
· Fusion of machine learning and metaheuristics for data-driven and adaptive optimization
· Metaheuristic-assisted heuristics and hyperheuristics for Just-in-Time (JIT) optimization
· Applications in healthcare and biomedical analytics, including big data and medical image-based optimization
· Applications in industrial and service systems: scheduling, planning, and resource allocation
· Applications in renewable energy system optimization
· Applications in smart manufacturing, production planning, and supply chain optimization
· Applications in IoT, smart cities, and cyber–physical infrastructure optimization
· Applications in computer and communication network optimization
· Survey of existing metaheuristic algorithms
· Survey of applications of metaheuristic algorithms in a specific domain
Keywords
Artificial Intelligence, Optimization, Engineering Problems, Heuristics, Metaheuristics, Multi-Objective Optimization, Hybrid Metaheuristic Algorithms, Hybrid Heuristic-Metaheuristic Algorithms.
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