Aiming to reduce the drawbacks of traditional VIRE approach, such as inaccurate boundary positioning and poor results of virtual tags using linear interpolation, we propose a new algorithm based on Radial Basis Functions (RBF) interpolation method and Quantum-behaved Particle Swarm Optimization (QPSO) in this paper. In order to simulate the actual loss of RSSI better, the proposed approach uses...

The phenomenon of network resource-constrained often appears in wireless sensor network (WSN) because of nodes with energy-limited, so it becomes a research topic to study on high-performance routing protocol in wireless sensor network. In accordance with the characteristic of quantum particle swarm optimization (QPSO), a novel clustering routing protocol with QPSO algorithm is proposed based o...

In this paper we investigate the use of quantum particle swarm optimization (QPSO) principles to resolve the satisfiability problem. We describe QPSOSAT, a new iterative approach for solving the well known Maximum Satisfiability problem (MAX-SAT). This latter has been shown to be NP-hard if the number of variables per clause is greater than 3. The basic idea is to harness the optimization capab...

Variation elliptical piston skirt has better mechanical and thermodynamic properties and it is widely applied in internal combustion engine in recent years. Because of its complex form, its geometrical precision evaluation is a difficult problem. In this paper, quasi-particle swarm optimization QPSO is proposed to calculate the minimum zone error and ellipticity of crosssection linear profile, ...

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPS...

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) with MOEA/D framework order to make QPSO be able solve MOPs effectively, advantage of being fully used. We also employ a diversity controlling mechanism avoid premature convergenc...

Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This p...

In cooperative relay networks, the selected relay nodes have great impact on the system performance. In this paper, a multi-relay selection schemes that consider both single objective and multi-objective are proposed based on evolutionary algorithms. First, the single-objective optimization problems of the best cooperative relay nodes selection for signal-to-noise ratio (SNR) maximization or po...

Twin Support Vector Machines (TWSVM) are developed on the basis of Proximal Support Vector Machines (PSVM) and Proximal Support Vector Machine based on the generalized eigenvalues(GEPSVM). The solving of binary classification problem is converted to the solving of two smaller quadratic programming problems by TWSVM. And then it gets two non-parallel hyperplanes. Its efficiency of dealing with t...

The computing method of the average optimal position is one most important factors that affect optimization performance QPSO algorithm. Therefore, a particle weight based on fitness value grading proposed, which called HWQPSO (hierarchical QPSO). In this method, higher particle, level and greater weight. Particles at different levels have weights, while particles same Through excellent weight, ...