No abstract

No abstract

The paper presents the use of a self-organizing feature map (SOFM) for determining damage in reinforced concrete frames with shear walls. For this purpose, a concrete frame with a shear wall was subjected to nonlinear dynamic analysis. The SOFM was optimized using the genetic algorithm (GA) in order to determine the number of layers, number of nodes in the hidden layer, transfer function type, and learning algorithm. The obtained model was compared with linear regression (LR) and nonlinear regression (NonLR) models and also the radial basis function (RBF) of a neural network. It was concluded that the SOFM, when optimized with the GA, has more strength, flexibility, and accuracy.

NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.

No abstract