Guest Editors
Dr. Mohammad Shokouhifar, Shahid Beheshti University, Iran
Dr. Alireza Goli,University of Isfahan, Iran
Dr. Mohammed A. A. Al-qaness, Zhejiang Normal University, China
Prof. Dr. Frank Werner, Otto-Von-Guericke-University, Germany
Summary
Mathematical problems in engineering (MPE) suffer from different complexities degrading the performance of the optimization algorithms that may perform well on benchmark test functions and simple case studies. Generally, solution search strategies for MPEs can be categorized into exact, heuristic, and random techniques. Although exact methods consistently achieve the optimal solution, such practices are not applicable for real-world NP-complete and NP-hard problems in the aspect of required running time. In recent years, a great deal of effort has been invested in applying knowledge-based heuristics and nature-inspired metaheuristics as the most practical algorithms for solving NP-complete and NP-hard MPEs. Moreover, various soft computing techniques such as artificial neural networks, big data analytics, fuzzy sets and systems, fuzzy arithmetic, machine learning, deep learning, and reinforcement learning, have been applied to solve MPEs.
Over the past years, heuristic- and metaheuristic-based optimization algorithms and their combinations with other soft computing techniques have attracted more and more attention from researchers and engineers in solving complex MPEs. As an appealing solution by exploiting the knowledge available in the problem model, ensemble heuristic-metaheuristic algorithms integrate the advantages of heuristics and metaheuristics to achieve a better complexity-efficiency trade-off than both techniques when applied separately. Although various soft computing-empowered optimization algorithms have been presented, there is still an open research area in the smart design of these techniques to efficiently tackle the complexities of real-world MPEs.
This special issue focuses on high-quality papers to report developments and applications of research topics related to smart optimization methods for solving real-world NP-complete and NP-hard engineering problems from the applied mathematics point of view. Authors are welcome to submit their research articles related to the optimization of MPEs in various engineering fields such as industrial engineering, electrical and computer engineering, chemical and mechanical problems, information sciences, business intelligence, operations research, logistics, and supply chain management. Topics of interest include, but are not limited to:
Exact mathematical methods for obtaining optimal solutions in MPEs
Knowledge-based heuristic and hyper-heuristic design methods for real-time MPEs
Nature-inspired evolutionary and swarm intelligence algorithms for MPEs
Machine learning techniques for learning engineering processes
Ensemble of optimization algorithms with soft computing techniques
Multi- and many-objective algorithms for constrained MPEs
Fuzzy arithmetic and robust optimization for uncertain mathematical models
Intelligent fuzzy systems for control engineering processes
Intelligent optimization for signal, image, and video processing
Intelligent optimization for scheduling and routing problems
Intelligent optimization for logistics and supply chain management
Keywords
Artificial Intelligence, Soft Computing, Intelligent Optimization, Mathematical Problems in Engineering, Heuristic and Metaheuristic Algorithms, Fuzzy Sets & Systems, Machine Learning, Deep Learning
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