A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems
In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …
Evolutionary computation for expensive optimization: A survey
Expensive optimization problem (EOP) widely exists in various significant real-world
applications. However, EOP requires expensive or even unaffordable costs for evaluating …
applications. However, EOP requires expensive or even unaffordable costs for evaluating …
A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization
Only a small number of function evaluations can be afforded in many real-world
multiobjective optimization problems (MOPs) where the function evaluations are …
multiobjective optimization problems (MOPs) where the function evaluations are …
Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization
Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-
objective optimization, where a number of variation operators have been developed to …
objective optimization, where a number of variation operators have been developed to …
A surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems
This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for
high-dimensional computationally expensive problems. The proposed algorithm includes …
high-dimensional computationally expensive problems. The proposed algorithm includes …
A surrogate-assisted differential evolution algorithm for high-dimensional expensive optimization problems
The radial basis function (RBF) model and the Kriging model have been widely used in the
surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global …
surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global …
Difficulties in fair performance comparison of multiobjective evolutionary algorithms
Proceedings of the Genetic and Evolutionary Computation Conference Companion:
Difficulties in fair performance comparison of mul Page 1 1 Difficulties in Fair Performance …
Difficulties in fair performance comparison of mul Page 1 1 Difficulties in Fair Performance …
A performance indicator-based infill criterion for expensive multi-/many-objective optimization
In surrogate-assisted multi-/many-objective evolutionary optimization, each solution
normally has an approximated value on each objective, resulting in increased difficulties in …
normally has an approximated value on each objective, resulting in increased difficulties in …
Performance indicator-based adaptive model selection for offline data-driven multiobjective evolutionary optimization
A number of real-world multiobjective optimization problems (MOPs) are driven by the data
from experiments or computational simulations. In some cases, no new data can be sampled …
from experiments or computational simulations. In some cases, no new data can be sampled …
Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems
Q Gu, Q Wang, NN **ong, S Jiang, L Chen - Complex & Intelligent Systems, 2022 - Springer
Surrogate-assisted optimization has attracted much attention due to its superiority in solving
expensive optimization problems. However, relatively little work has been dedicated to …
expensive optimization problems. However, relatively little work has been dedicated to …