Multi-Objective Optimization and Evolutionary Computing with Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 102
Special Issue Editor
Interests: Bayesian optimization; multi-objective optimization; variational quantum algorithms; benchmarking; machine learning
Special Issue Information
Dear Colleagues,
In many real-world scenarios, we have to deal with multiple conflicting objectives. For such optimization scenarios, the goal is to find a set of pareto-optimal solutions representing the optimal trade-offs between different objectives. Solving this multi-objective optimization task is highly challenging when each objective function is nonlinear, discontinuous, or subject to constraints or when the decision vector consists of mixed-integer/categorical variables. Practically, evolutionary algorithms have been developed for multi-objective optimization problems, which, empirically, can find near-optimal solutions with acceptable running times. As an alternative, Bayesian optimization has been generalized to multi-objective optimization problems for expensive-to-evaluate objective functions, focusing on reducing the queries to the objective function.
This Special Issue aims to provide a platform for researchers and practitioners to share their advancements and applications in this area. We particularly welcome the authors to contribute to the following aspects: development of new evolutionary or Bayesian optimization algorithms, mathematical programming, theoretical foundations, novel formulation of real-world problems, constraint handling approaches, machine learning for multi-objective optimization, innovative applications of multi-objective optimization algorithms, benchmarking, and performance measures/assessment.
Dr. Hao Wang
Guest Editor
Manuscript Submission Information
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Keywords
- multi-objective optimization
- Bayesian optimization
- evolutionary algorithms
- evolution strategies
- black-box optimization
- performance measures/indicators
- benchmarking
- metaheuristics
- mathematical optimization
- machine learning
