Evolutionary Multi-Objective Optimizations and Their Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 20 October 2025 | Viewed by 102
Special Issue Editors
Interests: model-based evolutionary algorithms; multiobjective optimization; large-scale optimization; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: evolutionary computation and its applications in machine learning; data mining; network science; scheduling
Special Issues, Collections and Topics in MDPI journals
Interests: evolutionary computing; multi-objective optimization; machine learning; data-driven optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the development of simulation and modeling techniques, optimization problems in industy, manufacturing, logistics, electricity, and many other fields are revealed to be complex and challenging, and are often characterized by black-box functions, a large number of variables, multiple conflicting objectives, and strict constraints. Such complex optimiation problems pose signigicant challenges to existing optimizers in obtaining a set of well-converged and diversified solutions. In the last decade, evolutionary multi-objective methods have emerged as effective technologies for tackling such complex optimization problems, mainly due to their population-based stochastic search paradigms. Although multi-objective evolutionary algorithms exhibit superiority in optimizing black-box functions and trading off between conflicting objectives, they still suffer from several issues, such as the low search efficiency, the curse of dimensionality, the satisfaction of strict constraints, and so on.
Dr. Cheng He
Dr. Ye Tian
Prof. Dr. Handing Wang
Guest Editors
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Keywords
- evolutionary computation
- multi-objective optimization
- computational intelligence
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