A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts
Evolutionary algorithms have been shown to be very successful in solving multi-objective
optimization problems (MOPs). However, their performance often deteriorates when solving …
optimization problems (MOPs). However, their performance often deteriorates when solving …
An adaptive reference vector-guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems
Most reference vector-based decomposition algorithms for solving multiobjective
optimization problems may not be well suited for solving problems with irregular Pareto …
optimization problems may not be well suited for solving problems with irregular Pareto …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
A survey of normalization methods in multiobjective evolutionary algorithms
A real-world multiobjective optimization problem (MOP) usually has differently scaled
objectives. Objective space normalization has been widely used in multiobjective …
objectives. Objective space normalization has been widely used in multiobjective …
A constrained many-objective evolutionary algorithm with learning vector quantization-based reference point adaptation
C Wang, H Huang, H Pan - Swarm and Evolutionary Computation, 2023 - Elsevier
Constrained many-objective optimization problems (CMaOPs) are frequently encountered in
real-world applications, generally having constrained Pareto fronts (PFs) that are often …
real-world applications, generally having constrained Pareto fronts (PFs) that are often …
ACDB-EA: Adaptive convergence-diversity balanced evolutionary algorithm for many-objective optimization
Recently, evolutionary algorithms (EAs) have shown their strong competitiveness in
handling many-objective optimization problems (MaOPs) with different Pareto fronts (PFs) …
handling many-objective optimization problems (MaOPs) with different Pareto fronts (PFs) …
A fuzzy decomposition-based multi/many-objective evolutionary algorithm
Performance of multi/many-objective evolutionary algorithms (MOEAs) based on
decomposition is highly impacted by the Pareto front (PF) shapes of multi/many-objective …
decomposition is highly impacted by the Pareto front (PF) shapes of multi/many-objective …
Coordinated adaptation of reference vectors and scalarizing functions in evolutionary many-objective optimization
It is highly desirable to adapt the reference vectors to unknown Pareto fronts (PFs) in
decomposition-based evolutionary many-objective optimization. While adapting the …
decomposition-based evolutionary many-objective optimization. While adapting the …
Surrogate-assisted evolutionary algorithm with decomposition-based local learning for high-dimensional multi-objective optimization
J Shen, P Wang, H Dong, W Wang, J Li - Expert Systems with Applications, 2024 - Elsevier
When the evolutionary algorithm is applied to handle high-dimensional expensive multi-
objective optimization problems (MOPs), population evolution is crucial since it controls …
objective optimization problems (MOPs), population evolution is crucial since it controls …
Micro many-objective evolutionary algorithm with knowledge transfer
Computational effectiveness and limited resources in evolutionary algorithms are
interdependently handled during the working of low-power microprocessors for real-world …
interdependently handled during the working of low-power microprocessors for real-world …