Population-Based Algorithms for Multi-Objective Optimization and Decision-Making Problems in Engineering Sciences
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (17 April 2024) | Viewed by 371
Special Issue Editor
Interests: multiobjective optimization; computational intelligence; optimization; mechatronics; algorithms; heuristics; control systems engineering
Special Issue Information
Dear Colleagues,
Population-based algorithms are helpful tools for handling complex engineering optimization problems. While a traditional approach for determining a solution with the desired trade-off states a single-objective optimization statement, it is clear that most of the time, those engineering problems are generally multi-objective: several specifications and requirements must be fulfilled, often in conflict. In that sense, population-based algorithms have been reconfigured to deal with this instance, sometimes via a multi-objective optimization approach and sometimes via assisting a multi-criteria decision-making process. This Special Issue aims to bring together recent research efforts and new techniques to handle such instances within the scope of the Entropy journal, that is, how information theory and entropy measures can be used as design objectives for multi-objective optimization or multi-criteria decision making in engineering sciences.
Dr. Gilberto Reynoso-Meza
Guest Editor
Manuscript Submission Information
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Keywords
- physics and engineering
- information theory
- complex systems
- inquiry
- computing
- chemistry and biology
- machine learning and systems theory
- economics
