Over the past few decades, modeling, simulation, and optimization tools have received
attention for their ability to represent and improve complex systems. The use of …

Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …

To handle the multiobjective optimization problems of truss-bar design, this paper introduces
a new metaheuristic multiobjective optimization algorithm. The proposed algorithm is based …

This paper proposes a new Multi-Objective Equilibrium Optimizer (MOEO) to handle
complex optimization problems, including real-world engineering design optimization …

Supply chains operate in a highly distuptive environment where a SC master plan should be
updated in line with disruptions to ensure that a high service level is provided to customers …

This paper proposes an arterial signal control stochastic simulation‐based optimization
model with traffic safety and efficiency as biobjectives and solves it by a biobjective …

Hyperparameter optimization constitutes a large part of typical modern machine learning
(ML) workflows. This arises from the fact that ML methods and corresponding preprocessing …

During the optimization process, a large number of data are generated through the search.
Machine learning techniques and algorithms can be used to handle the generated data to …

A surrogate-assisted optimization approach is an attractive way to reduce the total
computational budget for obtaining optimal solutions. This makes it special for its application …