Diversity Metrics in Combinatorial Problems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 2
Special Issue Editors
Interests: metaheuristics; combinatorial optimization; swarm intelligence; hybrid algorithms; reinforcement learning; machine learning; civil engineering; infrastructure optimization; UAV
Interests: metaheuristics; combinatorial optimization; swarm intelligence; hybrid algorithms; reinforcement learning; machine learning; discretization methods; set covering problem; applied artificial intelligence; soft computing
Interests: applied artificial intelligence; predictive and prescriptive analytics; hospital operations optimization; surgical scheduling; resource allocation; renewable energy forecasting; AI for healthcare management; smart infrastructure; data integration platforms
Special Issue Information
Dear Colleagues,
Combinatorial optimization problems remain at the core of modern optimization, where balancing exploration and exploitation determines the efficiency and robustness of solutions. In this context, diversity metrics have become essential tools for understanding and enhancing the behavior of metaheuristic algorithms, helping prevent premature convergence and promote more adaptive and efficient search processes.
This Special Issue invites researchers to contribute theoretical, methodological, or applied studies that explore the role of diversity in combinatorial optimization. Contributions may include new diversity measures, hybrid strategies, integration with reinforcement learning, as well as applications in engineering, planning, or complex systems.
Topics of interest include, but are not limited to, the following:
- Diversity metrics in metaheuristic algorithms;
- Diversity in combinatorial optimization;
- Reinforcement learning integrated with metaheuristics;
- Adaptive parameter control driven by diversity;
- Population-based and swarm-intelligence diversity;
- Hybridization of heuristic and exact methods;
- Exploration-exploitation balance and theoretical analysis;
- Applications in civil engineering, transportation, energy, and intelligent systems.
We warmly invite the research community to share advances that foster more diverse, robust, and sustainable optimization approaches.
Dr. José Lemus-Romani
Prof. Dr. Gino Astorga
Dr. Marcelo Becerra
Guest Editors
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Keywords
- diversity metrics
- combinatorial optimization
- metaheuristics
- swarm intelligence
- evolutionary algorithms
- population diversity
- exploration–exploitation balance
- adaptive search
- reinforcement learning
- hybrid algorithms
- diversity control
- adaptive parameter tuning
- diversity-based selection
- algorithmic diversity
- diversity-guided optimization
- stochastic search
- diversity indicators
- fitness landscape analysis
- diversity quantification
- diversity in reinforcement learning
- combinatorial landscapes
- search space exploration
- diversity-driven convergence
- algorithm robustness
- engineering optimization
- intelligent systems
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