The quantum particle swarm optimization (QPSO) algorithm exists some defects, such as premature convergence, poor search ability and easy falling into local optimal solutions. The adaptive adjustment strategy of inertia weight, chaotic search method and neighborhood mutation strategy are introduced into the QPSO algorithm in order to propose an improved quantum particle swarm optimization (AMCQ...

The optimal parameters of the support vector machine (SVM) are very important for accuracy modeling and generalization performance. The quantum particle swarm optimization (QPSO) algorithm takes on the characteristics of the rapid global optimization, scale chaos method provides the characteristics of the fast convergence and the SVM has the characteristics of the nonlinear fitting. These advan...

It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-be...

Quantum behaved particle swarm optimization (QPSO) has been one of the most widely used algorithm in engineering world. Since its debut 2004, QPSO for resolving numerous complicated multimodal problems. Moreover, considering adaptability and versatility, it resolved a variety real-world test To tackle numerical problems, we introduce novel hybrid QPSODE. The integrates with differential evoluti...

This paper establishes the models of the steelmaking continuous casting (SCC) process, and proposed the improved algorithms for this problem. The simulation results of a computerized scheduling system are also given to prove the model. The SCC process scheduling problem is very difficult to get a good performance solution in practice. The scheduling of the SCC process requires that each cast pl...

A novel optimization concept for modeling irregular-shaped patch antenna with high bandwidth and efficient radiation attributes is proposed in this paper, along the ability to accomplish design at a reduced computational cost burden. revolutionary computing perception established Gravitational Search Algorithm (GSA) Quantum Based Delta Particle Swarm Optimization (QPSO), now known as GSA-QPSO. ...

A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staph...

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This ne...

Linear Quadratic Regulator (LQR) is an optimal multivariable feedback control approach that minimizes the excursion in state trajectories of a system while requiring minimum controller effort. The behaviour of a LQR controller is determined by two parameters: state and control weighting matrices. These two matrices are main design parameters to be selected by designer and greatly influence the ...