Guest Editors
Dr. S. A. Edalatpanah, Ayandegan Institute of Higher Education, Iran.
Dr. Predrag S. Stanimirovic, University of Niš, Serbia.
Dr. Li-Tao Zhang, Zhengzhou University of Aeronautics, China.
Summary
Optimization is one of today's most interesting fields of research in all areas of applied mathematics and engineering. In the field of scientific and technical computation, various equations which describe realistic problems like natural phenomena, or engineering problems such as cryptography, natural language processing, MRI reconstruction, wireless sensor networks, intrusion detection systems, financial portfolios, economic modelling, or uncertainty problems, can often be reduced to solving an optimization problem. However, some of the aforementioned problems, particularly in various challenging real-world scenarios, are very difficult to solve using a mathematical analysis approach and must be solved numerically. In this case, numerical optimization has become one of the most promising tools for different applications and has rapidly spread into many other disciplines. Along with the development of basic optimization algorithms, in the last several decades there is a trend in the scientific community to solve complex and global optimization problems by using meta-heuristic algorithms, such as genetic and swarm-intelligence optimization algorithms. These algorithms use some randomness and imitate the evolution process in nature to avoid local optima. Traditional numerical algorithms aim to solve static optimization problems which do not change in time. On the other hand, the dynamical approach in time-varying optimization has several potential advantages, such as self-adaptation, parallel processing, and convenience for hardware implementation. This Special Issue aims to portray an overview of recent research trends on this matter, with results opening pathways for future work. This Special Issue encourages original research papers of high quality that focus on new and recent developments in methodologies, techniques, and applications of numerical optimization for solving various practical problems. In addition to original research, we also welcome review articles.
Potential topics include but are not limited to the following:
• Approximation and complexity in numerical optimization
• Constrained and unconstrained optimization
• Numerical PDE-constrained optimization
• Linear and nonlinear complementarity problems
• Fuzzy optimization
• Data envelopment Analysis
• Global optimization
• Meta-heuristic algorithms
• Time-varying nonlinear optimization
• Intelligent decision making
• Neural networks
• Deep learning algorithms
Keywords
Computational modeling, Numerical optimization, Constrained optimization, Meta-heuristic algorithms, Fuzzy optimization.
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