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 comprehensive competitive swarm optimizer for large-scale multiobjective optimization
Competitive swarm optimizers (CSOs) have shown very promising search efficiency in large-
scale decision space. However, they face difficulties when solving large-scale multi-/many …
scale decision space. However, they face difficulties when solving large-scale multi-/many …
Evolutionary large-scale multiobjective optimization: Benchmarks and algorithms
Evolutionary large-scale multiobjective optimization (ELMO) has received increasing
attention in recent years. This study has compared various existing optimizers for ELMO on …
attention in recent years. This study has compared various existing optimizers for ELMO on …
A variable importance-based differential evolution for large-scale multiobjective optimization
Large-scale multiobjective optimization problems (LMOPs) bring significant challenges for
traditional evolutionary operators, as their search capability cannot efficiently handle the …
traditional evolutionary operators, as their search capability cannot efficiently handle the …
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 …
DLEA: A dynamic learning evolution algorithm for many-objective optimization
For many-objective problems, how to maintain the diversity and convergence of the
distribution of the solution set over Pareto front (PF) has always been the research …
distribution of the solution set over Pareto front (PF) has always been the research …
A decomposition-based evolutionary algorithm with clustering and hierarchical estimation for multi-objective fuzzy flexible jobshop scheduling
X Zhang, S Liu, Z Zhao, S Yang - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
As an effective approximation algorithm for multi-objective jobshop scheduling, multi-
objective evolutionary algorithms (MOEAs) have received extensive attention. However …
objective evolutionary algorithms (MOEAs) have received extensive attention. However …
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 …
Evolutionary multi and many-objective optimization via clustering for environmental selection
Recently, multi and many-objective evolutionary algorithms (MOEAs) embedded with
clustering techniques to enhance their environmental selection show promising …
clustering techniques to enhance their environmental selection show promising …
Vertical distance-based clonal selection mechanism for the multiobjective immune algorithm
Traditional multiobjective immune algorithms (MOIAs) widely use the domination
relationship and crowding distance metric to run the cloning operator, which places more …
relationship and crowding distance metric to run the cloning operator, which places more …