A review of population-based metaheuristics for large-scale black-box global optimization—Part II
MN Omidvar, X Li, X Yao - IEEE Transactions on Evolutionary …, 2021 - ieeexplore.ieee.org
This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …
The first part covered two major algorithmic approaches to large-scale optimization, namely …
[HTML][HTML] Large-scale evolutionary optimization: A review and comparative study
Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …
world applications. However, their inherent complexity, coupled with the curse of …
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 …
Large-scale multiobjective optimization via reformulated decision variable analysis
With the rising number of large-scale multiobjective optimization problems (LSMOPs) from
academia and industries, some multiobjective evolutionary algorithms (MOEAs) with …
academia and industries, some multiobjective evolutionary algorithms (MOEAs) with …
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 …
A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization
With the popularity of “flipped classrooms,” teachers pay more attention to cultivating
students' autonomous learning ability while imparting knowledge. Inspired by this, this paper …
students' autonomous learning ability while imparting knowledge. Inspired by this, this paper …
Evolutionary multitasking for large-scale multiobjective optimization
Evolutionary transfer optimization (ETO) has been becoming a hot research topic in the field
of evolutionary computation, which is based on the fact that knowledge learning and transfer …
of evolutionary computation, which is based on the fact that knowledge learning and transfer …
Neural net-enhanced competitive swarm optimizer for large-scale multiobjective optimization
The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner
particles and then uses the winner particles to efficiently guide the search of the loser …
particles and then uses the winner particles to efficiently guide the search of the loser …
Heat charge performance prediction and optimization of locally refined bionic fin heat exchanger with PCM and nanoparticles based on NSGA-II
Z Wang, Y Wang, L Yang, Y Cui, A Dong, W Cui… - Renewable Energy, 2024 - Elsevier
Phase change energy storage plays an important role in the popularization of renewable
energy sources (wind, solar, geothermal, etc.). However, previous studies have paid little …
energy sources (wind, solar, geothermal, etc.). However, previous studies have paid little …