Intelligent Computing & Optimization
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 260
Special Issue Editors
Interests: optimization; data mining; intelligent optimization algorithm; risk management
Special Issues, Collections and Topics in MDPI journals
Interests: fuzzy decision-making; emergency management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid development of intelligent computing techniques—such as evolutionary computation, swarm intelligence, and metaheuristics—has significantly expanded the frontier of optimization research. Intelligent optimization algorithms, often inspired by natural processes or learning mechanisms, have demonstrated strong adaptability, robustness, and scalability across a wide range of problem domains. These methods offer powerful search capabilities that can effectively handle complex, high-dimensional, dynamic, and multi-objective optimization problems, which are often intractable for traditional mathematical programming techniques.
This Special Issue aims to bring together cutting-edge research at the intersection of intelligent computing and optimization, with a strong emphasis on algorithm design, theoretical modeling, computational efficiency, and real-world applicability. We particularly encourage contributions that propose novel algorithmic frameworks, integrate machine learning technologies, or demonstrate impactful applications in domains such as transportation, energy systems, healthcare, logistics, smart manufacturing, and sustainable development.
Topics of interest include, but are not limited to, evolutionary computation, swarm intelligence, reinforcement learning, machine learning, neural architecture search, multi-objective optimization, constrained optimization, multimodal optimization, large-scale optimization, surrogate-assisted optimization, and hybrid metaheuristics.
The goal of this Special Issue is to provide a comprehensive platform for researchers and practitioners to exchange innovative ideas, promote interdisciplinary collaboration, and inspire novel approaches to tackling the increasingly sophisticated optimization problems faced by modern society.
Prof. Dr. Xiaobing Yu
Prof. Dr. Mei Cai
Guest Editors
Manuscript Submission Information
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Keywords
- genetic algorithm
- differential evolution
- particle swarm optimization
- ant colony optimization
- gray wolf optimizer
- metaheuristic algorithms
- machine learning
- multi-objective optimization
- constrained optimization
- multimodal optimization
- large-scale optimization
- surrogate-assisted optimization
- real-world applications
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