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, ...

Based on weighted signal covariance (WSC) matrix and maximum likelihood (ML) estimation, a directionof-arrival (DOA) estimation method of multiple moving targets is designed and named as WSC-ML in the presence of impulse noise. In order to overcome the shortcoming of the multidimensional search cost of maximum likelihood estimation, a novel continuous quantum particle swarm optimization (QPSO) ...

Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping a...

Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the numbe...

Accurate control of excavator trajectory is the foundation for intelligent and unmanned development excavators. The operation process requires multiple actuators to cooperate complete response action. However, existing methods realize a single actuator can no longer meet practical demand. Based on this, hybrid adaptive quantum particle swarm optimization algorithm (HAQPSO) proposed tune proport...

Aiming at the parameter optimization of least square support vector machine (LS-SVM), an improved quantum-behaved particle swarm optimization (IQPSO) algorithm for LS-SVM parameter selection was proposed. Based on QPSO, the algorithm optimizes particle initializing positions and improves solving speed and precision by sampling and linearizing methods. IQPSO LSSVM model was test by test function...