Evolutionary large-scale multi-objective optimization: A survey
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …
solving various optimization problems, but their performance may deteriorate drastically …
A review on constraint handling techniques for population-based algorithms: from single-objective to multi-objective optimization
Most real-world problems involve some type of optimization problems that are often
constrained. Numerous researchers have investigated several techniques to deal with …
constrained. Numerous researchers have investigated several techniques to deal with …
A survey on evolutionary constrained multiobjective optimization
Handling constrained multiobjective optimization problems (CMOPs) is extremely
challenging, since multiple conflicting objectives subject to various constraints require to be …
challenging, since multiple conflicting objectives subject to various constraints require to be …
A coevolutionary framework for constrained multiobjective optimization problems
Constrained multiobjective optimization problems (CMOPs) are challenging because of the
difficulty in handling both multiple objectives and constraints. While some evolutionary …
difficulty in handling both multiple objectives and constraints. While some evolutionary …
An evolutionary multitasking optimization framework for constrained multiobjective optimization problems
When addressing constrained multiobjective optimization problems (CMOPs) via
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
Utilizing the relationship between unconstrained and constrained Pareto fronts for constrained multiobjective optimization
Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be
optimized and various constraints to be satisfied, which challenges the evolutionary …
optimized and various constraints to be satisfied, which challenges the evolutionary …
Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization
Both objective optimization and constraint satisfaction are crucial for solving constrained
multiobjective optimization problems, but the existing evolutionary algorithms encounter …
multiobjective optimization problems, but the existing evolutionary algorithms encounter …
Dynamic auxiliary task-based evolutionary multitasking for constrained multiobjective optimization
When solving constrained multiobjective optimization problems (CMOPs), the utilization of
infeasible solutions significantly affects algorithm's performance because they not only …
infeasible solutions significantly affects algorithm's performance because they not only …
Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces
Constrained multiobjective optimization problems (CMOPs) are frequently encountered in
real-world applications, which usually involve constraints in both the decision and objective …
real-world applications, which usually involve constraints in both the decision and objective …
A dual-population-based evolutionary algorithm for constrained multiobjective optimization
The main challenge in constrained multiobjective optimization problems (CMOPs) is to
appropriately balance convergence, diversity and feasibility. Their imbalance can easily …
appropriately balance convergence, diversity and feasibility. Their imbalance can easily …