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 Pearson correlation-based adaptive variable grouping method for large-scale multi-objective optimization
Dividing variables into groups is an intuitive idea for tackling large-scale multi-objective
problems. However, regular grouping methods often suffer from the computationally …
problems. However, regular grouping methods often suffer from the computationally …
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 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 …
An evolutionary algorithm for solving large-scale robust multi-objective optimization problems
Robust multi-objective optimization problems (RMOPs) widely exist in real-world
applications, which introduce a variety of uncertainty in optimization models. While some …
applications, which introduce a variety of uncertainty in optimization models. While some …
Neural Network-Based Dimensionality Reduction for Large-Scale Binary Optimization With Millions of Variables
Binary optimization assumes a pervasive significance in the context of practical applications,
such as knapsack problems, maximum cut problems, and critical node detection problems …
such as knapsack problems, maximum cut problems, and critical node detection problems …
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 …