Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces
By Zhi-Zhong Liu, Yong Wang. IEEE Transactions on Evolutionary Computation, 2019
399 citations recorded by the source corpus.
Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces. However, current artificial CMOPs never consider constraints in the decision space (i.e., decision constraints) and constraints in the objective space (i.e., objective constraints) at the same time. As a result, they have a limited capability to simulate practical scenes. To remedy this issue, a set of CMOPs, named DOC, is constructed in this paper. It is the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs. Specifically, in DOC, various decision constraints (e.g., inequality constraints, equality constraints, linear constraints, and nonlinear constraints) are collected from real-world applications, thus making the feasible region in the decision space have different properties (e.g., nonlinear, extremely small, and multimodal). On the other hand, some simple and controllable objective constraints are devised to reduce the feasible region in the objective space and to make the Pareto front have diverse characteristics (e.g., continuous, discrete, mixed, and degenerate). As a whole, DOC poses a great challenge for a constrained multiobjective evolutionary algorithm (CMOEA) to obtain a set of well-distributed and well-converged feasible solutions. In order to enhance current CMOEAs’ performance on DOC, a simple and efficient two-phase framework, named ToP, is proposed in this paper. In ToP, the first phase is implemented to find the promising feasible area by transforming a CMOP into a constrained single-objective optimization problem. Then in the second phase, a specific CMOEA is executed to obtain the final solutions. ToP is applied to four state-of-the-art CMOEAs, and the experimental results suggest that it is quite effective.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/e66f7bd2bdf14914902a728bc213fcb6d4ec639f
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Citations in the corpus
- Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, background): “However, most existing CMOEAs encounter difficulties in striking a good balance between objectives and constraints when solving CMOPs with complex feasible regions [13], [14].”
- A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “For CMOPs with constraints in both decision and objective spaces, Liu and Wang [96] suggested a two-stage optimization idea.”
- A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, methodology): “straints in both the decision and objective spaces [10].”