Two-stage knowledge-driven evolutionary algorithm for distributed green flexible job shop scheduling with type-2 fuzzy processing time
This study is investigated on multi-objective distributed green flexible job shop scheduling
problem with type-2 fuzzy processing time. Minimizing makespan and total energy …
problem with type-2 fuzzy processing time. Minimizing makespan and total energy …
Surrogate-assisted level-based learning evolutionary search for geothermal heat extraction optimization
An enhanced geothermal system is essential to provide sustainable and long-term
geothermal energy supplies and reduce carbon emissions. Optimal well-control scheme for …
geothermal energy supplies and reduce carbon emissions. Optimal well-control scheme for …
Evolutionary optimization methods for high-dimensional expensive problems: A survey
Evolutionary computation is a rapidly evolving field and the related algorithms have been
successfully used to solve various real-world optimization problems. The past decade has …
successfully used to solve various real-world optimization problems. The past decade has …
A binary individual search strategy-based bi-objective evolutionary algorithm for high-dimensional feature selection
Evolutionary computation is promising in tackling with the feature selection problem, but still
has poor performance in obtaining good feature subset in high-dimensional problems. In …
has poor performance in obtaining good feature subset in high-dimensional problems. In …
Surrogate-assisted evolutionary algorithm with model and infill criterion auto-configuration
Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving
computationally expensive optimization problems (EOPs). However, the performance of …
computationally expensive optimization problems (EOPs). However, the performance of …
Edge–cloud co-evolutionary algorithms for distributed data-driven optimization problems
Surrogate-assisted evolutionary algorithms (EAs) have been proposed in recent years to
solve data-driven optimization problems. Most existing surrogate-assisted EAs are for …
solve data-driven optimization problems. Most existing surrogate-assisted EAs are for …
Linear subspace surrogate modeling for large-scale expensive single/multi-objective optimization
Despite that the surrogate-assisted evolutionary algorithms have achieved great success in
addressing expensive optimization problems, they still suffer from stiff challenges when the …
addressing expensive optimization problems, they still suffer from stiff challenges when the …
Expensive optimization via surrogate-assisted and model-free evolutionary optimization
The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient
approaches for solving expensive optimization problems. However, it still faces challenges …
approaches for solving expensive optimization problems. However, it still faces challenges …
Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level
Energy storage such as battery and thermal energy storage is an effective approach to shift
building peak load and alleviate grid stress at a building cluster level. However, due to the …
building peak load and alleviate grid stress at a building cluster level. However, due to the …
Evolutionary sampling agent for expensive problems
Data-driven evolutionary algorithms are widely studied for their ability to solve expensive
optimization problems in engineering and science. This article introduces a novel …
optimization problems in engineering and science. This article introduces a novel …