Industrial scale continuous stirred tank reactor (CSTR) for production and grade transitions of poly vinyl acetate (PVAc) at the different reactor sizes was investigated. Such reactor is known to show oscillatory behavior and to have periodic limit points, particularly at high molecular weights. Four efficient novel hybrid optimization methods which use variable population size genetic algorith...

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. ...

The paper proposes shuffled frog leaping algorithm (SFLA) based on enhanced learning, which generates a virtual general centre frog that is related to the optimal frog of each memeplex. The algorithm can utilise the superior information of each memeplex, enhance the mutual learning and use the average centre of optimal frog. In the processing of evolution, the optimal frog of sub-memeplex learn...

Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the shuffled frog-leaping (SFLO) algorithm is proposed: called the maximum entropy based shuffled frog-leaping algorithm thresholding (MESFLOT) me...

This paper studies wireless sensor networks node deployment problem and proposes intelligent single particle optimizer based wireless sensor networks adaptive coverage. According to the probability model measure characteristic of wireless sensor nodes, the method adaptively determines the optimal deployment of sensor nodes using intelligent single particle optimizer, achieving sensor node based...

Clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. K-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. In recent years, several algorithms are provided based on evolutionary algorithms for cluster...