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Based on the quantum-behaved particle swarm optimization (QPSO) algorithm, we formulate the philosophy of QPSO and introduce a so-called mainstream thought of the population to evaluate the search scope of a particle and thus propose a novel parameter control method of QPSO. After that, we test the revised QPSO algorithm on several benchmark functions and(More)
Keywords: PSO QPSO Mean best position Weight parameter WQPSO a b s t r a c t Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with(More)
We first briefly review the state of the art of digital in-line holographic microscopy (DIHM) with numerical reconstruction and then discuss some technical issues, such as lateral and depth resolution, depth of field, twin image, four-dimensional tracking, and reconstruction algorithm. We then present a host of examples from microfluidics and biology of(More)
The endoplasmic reticulum (ER) is the cellular organelle responsible for protein folding and assembly, lipid and sterol biosynthesis, and calcium storage. The unfolded protein response (UPR) is an adaptive intracellular stress response to accumulation of unfolded or misfolded proteins in the ER. In this study, we show that the most conserved UPR sensor(More)
A critical component of laboratory surveillance for measles is the genetic characterization of circulating wild-type viruses. The World Health Organization (WHO) Measles and Rubella Laboratory Network (LabNet), provides for standardized testing in 183 countries and supports genetic characterization of currently circulating strains of measles viruses. The(More)
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(More)
The mutation mechanism is introduced into quantum-behaved particle swarm optimization to increase its global search ability and escape from local minima. Based on the characteristic of QPSO algorithm, the variable of gbest and mbest is mutated with Cauchy distribution respectively. The experimental results on test functions show that QPSO with gbest and(More)
Adaptive infinite impulse response (IIR) filters have shown their worth in a wide range of practical applications. Because the error surface of IIR filters is multimodal in most cases, global optimization techniques are required for avoiding local minima. In this paper, we employ a global optimization algorithm, Quantum-behaved particle swarm optimization(More)