Intelligent and Nature-Inspired Heuristics for Optimization and Decision-Making Problems
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 August 2026
Special Issue Editor
Interests: metaheuristic optimization; ant colony optimization; swarm intelligence; multi-agent systems; UAV trajectory planning; operations research; combinatorial optimization; decision-making under uncertainty
Special Issue Information
Dear Colleagues,
Many real-world optimization and decision-making problems are highly complex, dynamic, and often NP-hard, making exact solutions computationally intractable. In such contexts, intelligent and nature-inspired heuristics provide powerful alternatives that deliver high-quality solutions within reasonable computational times.
This Special Issue aims to highlight recent advances in intelligent and nature-inspired heuristic methods for solving complex, multi-objective, or dynamic optimization and decision-making problems. We welcome contributions focused on the design, analysis, and application of metaheuristics such as ant colony optimization, particle swarm optimization, evolutionary algorithms, and other swarm- or population-based techniques.
In addition, we encourage works that explore hybrid approaches combining heuristics with machine learning, mathematical programming, or problem-specific heuristic design to improve performance, adaptability, or interpretability.
Applications may include vehicle routing and scheduling, multi-agent coordination, resource allocation, logistics and transportation, energy systems, and other real-world optimization challenges. Both theoretical developments and practical implementations are welcome.
We particularly welcome contributions that advance the understanding, design, and application of intelligent and nature-inspired heuristics in optimization and decision-making.
Dr. Sara Pérez-Carabaza
Guest Editor
Manuscript Submission Information
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Keywords
- metaheuristics
- nature-inspired optimization
- ant colony optimization
- swarm intelligence
- real-world applications
- matheuristics
- combinatorial optimization
- optimization under uncertainty
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