Combinatorial testing (CT) can efficiently detect failures caused by interactions of parameters software under test. The CT study has undergone a transition from traditional to constrained CT, which is crucial for real-world systems testing. Under this scenario, covering array generation (CCAG), vital combinatorial optimisation issue targeted with constructing test suite minimal size while prop...

In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases, which are meaningful to the users and can generate strong rules on the basis of these frequent patterns, which are helpful in decision support system. Quantum Particle Swarm Optimization (QPSO) is one of the several methods for mining associ...

In this paper, a novel and generic multi-objective design paradigm is proposed which utilizes quantum-behaved PSO(QPSO) for deciding the optimal configuration of the LQR controller for a given problem considering a set of competing objectives. There are three main contributions introduced in this paper as follows. (1) The standard QPSO algorithm is reinforced with an informed initialization sch...

In order to solve the problem of monitor in mining and environment, the wireless sensor networks (WSN) is used. As one of the fundamental and important problems in WSN, coverage reflects the effect of monitoring and tracking. Because of the high density and complexity of distributing nodes in WSN, the coverage control algorithm for the optimal working sensor set is studied. On the other hand, e...

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...