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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 …
Inverse model and adaptive neighborhood search based cooperative optimizer for energy-efficient distributed flexible job shop scheduling
Solving the energy-efficient distributed flexible job shop scheduling problem (EEDFJSP)
obtains increased attention. However, most previous studies barely considered the large …
obtains increased attention. However, most previous studies barely considered the large …
MOCPSO: A multi-objective cooperative particle swarm optimization algorithm with dual search strategies
Particle swarm optimization (PSO) is a widely embraced meta-heuristic approach to tackling
the complexities of multi-objective optimization problems (MOPs), renowned for its simplicity …
the complexities of multi-objective optimization problems (MOPs), renowned for its simplicity …
A modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization
Multi-objective evolutionary algorithms have demonstrated promising performance in
solving multi/many-objective problems. However, their performance decreases sharply …
solving multi/many-objective problems. However, their performance decreases sharply …
A fast nondominated sorting-based MOEA with convergence and diversity adjusted adaptively
In the past few decades, to solve the multi-objective optimization problems, many multi-
objective evolutionary algorithms (MOEAs) have been proposed. However, MOEAs have a …
objective evolutionary algorithms (MOEAs) have been proposed. However, MOEAs have a …
A flexible ranking-based competitive swarm optimizer for large-scale continuous multi-objective optimization
X Gao, S Song, H Zhang, Z Wang - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Due to the curse of dimensionality, the search efficiency of existing operators in large-scale
decision space deteriorates dramatically. The competitive swarm optimizer (CSO)-based …
decision space deteriorates dramatically. The competitive swarm optimizer (CSO)-based …
Pareto optimization with small data by learning across common objective spaces
In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF)
as the number of points scales exponentially with the dimensionality of the objective space …
as the number of points scales exponentially with the dimensionality of the objective space …