Learning to accelerate evolutionary search for large-scale multiobjective optimization
Most existing evolutionary search strategies are not so efficient when directly handling the
decision space of large-scale multiobjective optimization problems (LMOPs). To enhance …
decision space of large-scale multiobjective optimization problems (LMOPs). To enhance …
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 …
Clustering analysis for the pareto optimal front in multi-objective optimization
LA Bejarano, HE Espitia, CE Montenegro - Computation, 2022 - mdpi.com
Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization
problems. Among different proposals, a widely used approach is based on the Pareto front …
problems. Among different proposals, a widely used approach is based on the Pareto front …
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 …
Learning-aided evolutionary search and selection for scaling-up constrained multiobjective optimization
The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great
room for improvement in balancing populations convergence, diversity and feasibility on …
room for improvement in balancing populations convergence, diversity and feasibility on …
An adaptive two-stage evolutionary algorithm for large-scale continuous multi-objective optimization
This paper proposes an adaptive two-stage large-scale multi-objective evolutionary
algorithm, in which a neural network-based accelerating optimizer is designed in the first …
algorithm, in which a neural network-based accelerating optimizer is designed in the first …
A survey of meta-heuristic algorithms in optimization of space scale expansion
J Zhang, L Wei, Z Guo, H Sun, Z Hu - Swarm and Evolutionary …, 2024 - Elsevier
Optimization problem of space scale expansion widely exists in practical applications, such
as transportation, logistics, scheduling, social networks, etc. According to different expansion …
as transportation, logistics, scheduling, social networks, etc. According to different expansion …
A Novel Clustering-Based Evolutionary Algorithm with Objective Space Decomposition for Multi/Many-Objective Optimization
W Zheng, Y Tan, Z Yan, M Yang - Information Sciences, 2024 - Elsevier
The framework of decomposing a multi-objective optimization problem (MOP) into some
MOPs holds considerable promise. However, its advancement is constrained by numerous …
MOPs holds considerable promise. However, its advancement is constrained by numerous …
Evolutionary Reinforcement Learning with Action Sequence Search for Imperfect Information Games
Abstract Deep Reinforcement Learning (DRL) has achieved remarkable success in perfect
information games. However, when applied to imperfect information games like Contract …
information games. However, when applied to imperfect information games like Contract …