Computational Intelligence for Large-Scale and Data-Driven Optimization
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 58
Special Issue Editor
Special Issue Information
Dear Colleagues,
The rapid growth of large-scale, high-dimensional, and data-intensive applications has created an urgent need for advanced computational intelligence techniques capable of addressing complex optimization challenges. Traditional optimization approaches often fail to scale efficiently, lack robustness in noisy or uncertain environments, or struggle to integrate heterogeneous data from real-world systems. Emerging computational intelligence methods—including evolutionary computation, swarm intelligence, hybrid metaheuristics, machine learning-assisted optimization, and adaptive search strategies—offer powerful tools for solving these demanding problems.
This Special Issue, ‘Computational Intelligence for Large-Scale and Data-Driven Optimization’, aims to bring together innovative research that advances the theory, algorithms, and applications in this rapidly evolving field. We welcome contributions that address large-scale global optimization, high-dimensional feature selection, multi-objective search, real-world engineering problems, intelligent data-driven decision-making, and hybrid frameworks that combine machine learning with optimization. Both methodological advancements and impactful applications—including engineering design, bioinformatics, healthcare analytics, logistics, energy systems, and smart technologies—are highly encouraged.
We look forward to receiving high-quality submissions that will expand the boundaries of computational intelligence and demonstrate its transformative potential in large-scale and data-rich environments.
Dr. Mohammed Qaraad
Guest Editor
Manuscript Submission Information
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Keywords
- computational intelligence
- large-scale optimization
- evolutionary computation
- swarm intelligence
- machine learning–assisted optimization
- data-driven decision-making
- hybrid metaheuristics
- feature selection
- multi-objective optimization
- high-dimensional data analytics
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