Abstract Air pollution monitoring is constantly increasing, giving more and attention to its consequences on human health. Since Nitrogen dioxide (NO 2 ) sulfur (SO are the major pollutants, various models have been developed predicting their potential damages. Nevertheless, providing precise predictions almost impossible. In this study, a new hybrid intelligent model based long short-term memo...

Abstract To solve travelling salesman problems (TSPs), most existing evolutionary algorithms search for optimal solutions from zero initial information without taking advantage of the historical solving similar problems. This paper studies a transfer learning-based particle swarm optimization (PSO) algorithm, where is used to guide find paths quickly. begin with, all cities in new and TSP are c...

An Artificial Neural Network (ANN) is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of ―learning‖. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be ...

Abstract As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial of applications with noise (DBSCAN), as a common can achieve clusters via finding high-density areas separated by low-density based on cluster density. Different from other methods, DBSCAN work well for any shape the database and effectively exceptional data. However,...

– In this article, Teaching-learning opposition based optimization (TLOBO) algorithm based on the natural phenomenon of teaching and learning is applied to design an optimal higher order stable low pass (LP) and high pass (HP) IIR digital filter using different design criterion. The original TeachingLearning Based Optimization (TLBO) algorithm has been remodeled by merging the concept of opposi...

In this work we focus on the determination of the relative distributions of young, intermediate-age and old populations of stars in galaxies. Starting from a grid of theoretical population synthesis models we constructed a set of model galaxies with a distribution of ages, metallicities and intrinsic reddening. Using this set we have explored a new fitting method that presents several advantage...

This paper presents a new Artificial Bee Colony (ABC) optimization algorithm to solve function optimization problems. The proposed approach is called OCABC, which introduces opposition-based learning concept and dynamic Cauchy mutation into the standard ABC algorithm. To verify the performance of OCABC, eight well-known benchmark function optimization problems are used in the experiments. Exper...

As a new margin-based classifier, ψ-learning shows great potential for high accuracy. However, the optimization of ψ-learning involves non-convex minimization and is very challenging to implement. In this article, we convert the optimization of ψ-learning into a mixed integer programming (MIP) problem. This enables us to utilize the state-of-art algorithm of MIP to solve ψ-learning. Moreover, t...