Algorithm Engineering for Complex Optimization Problems: Theory and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 18
Special Issue Editor
Special Issue Information
Dear Colleagues,
Optimization offers a robust set of tools and methodologies for resolving problems in science, engineering, and industry. Despite improvements in their convergence speed, traditional optimization methods often struggle with complex, high-dimensional problems, particularly those involving large and non-convex search spaces. The aim of this Special Issue is to explore the engineering of algorithms specifically developed to handle this complexity, effectively bridging the gap between theoretical knowledge and practical application.
This call for papers seeks high-quality research that explores the design principles, theoretical analysis, and empirical validation of optimization algorithms. We encourage submissions that cover a broad range of methodologies, including, but not limited to, evolutionary algorithms, swarm intelligence, hyper-heuristics, machine learning-based methods, and hybrid techniques. Submissions should place a strong emphasis on algorithmic novelty, demonstrable scalability, robustness across diverse instances of a problem, and the ability to adapt effectively to the challenges posed by complex problems.
We warmly invite researchers, academics, and industry experts to submit original, unpublished research papers to this Special Issue. Should you require additional time or have any questions regarding the Special Issue, please do not hesitate to get in touch by replying to this email.
Dr. Asma Ladj
Guest Editor
Manuscript Submission Information
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Keywords
- complex optimization problems
- evolutionary algorithms
- swarm intelligence
- heuristics, metaheuristics, hyper-heuristics, and hybrid algorithms
- adaptive, robust, and scalable algorithms
- machine learning for optimization
- high-dimensional optimization
- multi-objective optimization
- stochastic optimization
- optimization under uncertainty
- algorithm design, analysis, tuning, and benchmarking
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