Feature selection aims to reduce the number of features and improve the classification
accuracy, which is an essential step in many real-world problems. Multiple feature subsets …

In this paper, a self-adaptive differential evolution (DE) algorithm is designed to solve multi-
objective flow shop scheduling problems with limited buffers (FSSPwLB). The makespan …

The development of the efficient sparse signal recovery algorithm is one of the important
problems of the compressive sensing theory. There exist many types of sparse signal …

Feature selection aims to select a small subset of relevant features while maintaining or
even improving the classification performance over using all features. Feature selection can …

The problem of radio frequency identification network planning (RNP) is one of the biggest
challenges in the field of RFID research. In order to improve the service quality of RFID …

Emotion categorization plays an important role in understanding human emotions by
artificial intelligence systems such as robots. It is a difficult task as humans express many …

The brain storm optimization (BSO) algorithm has received increased attention in the field of
evolutionary computation. While BSO has been applied in numerous industrial scenarios …

Abstract Brain Storm Optimization (BSO) is a swarm intelligence optimization algorithm,
based on the human brainstorming process. The ideas of a brainstorming process comprise …

The quality of the data space, which is often represented by a set of features, is one of the
most critical aspects affecting the classification performance of a machine learning …

Nowadays, many real-world optimization problems, which are separated and non-
differentiable, could not be solved by traditional optimization algorithms efficiently. To deal …