[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 …
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 …
Carbon emission flow based energy routing strategy in energy Internet
In the energy Internet (EI), energy can flow likewise information routing. Notably, under the
graph-structured regional EI scenario, the energy routing path with the least power losses …
graph-structured regional EI scenario, the energy routing path with the least power losses …
An evolutionary multitasking algorithm with multiple filtering for high-dimensional feature selection
Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-
dimensional classification. However, the generation of multiple tasks in the existing EMT …
dimensional classification. However, the generation of multiple tasks in the existing EMT …
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 …
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 …
Rapidly evolving soft robots via action inheritance
The automatic design of soft robots characterizes as jointly optimizing structure and control.
As reinforcement learning is gradually used to optimize control, the time-consuming …
As reinforcement learning is gradually used to optimize control, the time-consuming …
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 …
Directed quick search guided evolutionary framework for large-scale multi-objective optimization problems
For large-scale multi-objective evolutionary algorithms (LSMOEAs), obtaining efficient
evolutionary directions in an ultrahigh-dimensional decision space to produce high-quality …
evolutionary directions in an ultrahigh-dimensional decision space to produce high-quality …
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 …