Emerging Topics in Evolutionary Multiobjective Optimization
Most real-world optimization problems involve multiple (conflicting) objectives to be achieved simultaneously, which are known as the multiobjective optimization problems (MOPs). To solve MOPs, the area of evolutionary multiobjective optimization (EMO) has witnessed rapid development since the late 90s. However, the complexity of real-world problems has substantially increased over the last two decades. Many emerging problems involve dealing with many (usually conflicting) objectives, a huge number of variables, nonlinear relationships, variety of soft and hard constraints, and/or in a dynamic and uncertain environment. Such problems have posed great challenges to the EMO area. For example, as the number of objectives increases, classical EMO methods, which are based on the standard Pareto dominance criterion, struggle to approach the optimal front; as the problem scale (the number of decision variables) becomes large, traditional EMO methods, which work well in test suites, may perform rather poorly; when dealing with a dynamic real-world problem, EMO algorithms which are designed specifically for dynamic benchmark functions may completely fail as they do not share the same pattern of the environmental change; as the optimization models become increasingly complicated, traditional EMO algorithms without the assistance of surrogate models are no longer applicable due to the prohibitively expensive fitness evaluations.
In light of those emerging topics in EMO, this special issue aims at promoting first-class research outputs, and offers a timely collection of information to benefit the researchers and practitioners. To be specific, it is of particular interest in terms of how to perform interdisciplinary research of EMO using state-of-the-art computational intelligence (CI) and multi-criteria decision-making (MCDM) theories, methods and techniques. Topics of interest include, but are not limited to:
- Many-objective optimization
- Large-scale multi-/many-objective optimization
- Multiobjective optimization in dynamic and uncertain environment
- Preference handling techniques
- Constraint handling techniques
- Visualization techniques
- Surrogate-assisted techniques
- Performance indicators
- Test functions and benchmark problems
- Theoretical analysis of convergence and scalability
- Real-world applications
- Conference: March 28-31, 2021
- Submission opens on: April 1, 2021
- Final Paper Submission: August 1, 2021
Cheng He, Research Assistant Professor (Email: email@example.com) (Confirmed)
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
Dunwei Gong, Professor (Email: firstname.lastname@example.org) (Confirmed)
Computational Intelligence and the Director of the Centre for Intelligent Optimization and Control, School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
Tan Kay Chen, Professor (Email: email@example.com) (Confirmed)
City University of Hong Kong, Hong Kong