Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which has been applied widely for continuous optimization problems. In this paper, we propose an improved quantum-behaved particle swarm optimization with memory according to the means of best position of particles and using sigal step seaching strategy for sovle the multidimentional prob...

Abstract Quantum-behaved particle swarm optimization algorithm is analyzed in this paper, and the path of mobile robot is planned based on QPSO with binary coding. Two controller design methods of robots trajectory tracking are analyzed, parameters of them are optimized with weighted operator and the simulation results are compared. Simulation results show that QPSO algorithm and its improvemen...

Due to the difficulties in the measurement of biochemical variables in fermentation process, softsensing model based on radius basis function neural network had been established for estimating the variables. To generate a more efficient neural network estimator, we employed the previously proposed quantum-behaved particle swarm optimization (QPSO) algorithm for neural network training. The expe...

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by in...

In this paper, we formulate the dynamics and philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the whole population level. After that we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization...

Traffic information is so far less than the number of OD variables, that it is difficult to obtain the satisfactory solution. In this paper, a method based on Quantum behaved Particle Swarm Optimization (QPSO) algorithm is developed to obtain the global optimal solution. It designs the method based on QPSO algorithm to solve the OD matrix prediction model, lists the detailed steps and points ou...

To achieve sustainable development and improve market competitiveness, many manufacturers are transforming from traditional product manufacturing to service manufacturing. In this trend, the system (PSS) has become mainstream of supply satisfy customers with individualized products combinations. The diversified customer requirements can be realized by PSS configuration based on modular design. ...

Short-term power prediction of the photovoltaic system is one of the effective means to reduce the adverse effects of photovoltaic power on the grid. Since the efficiency of the traditional support vector machine (SVM) prediction method is low, this paper proposes the SVM based on the parameter optimization method of quantum particle swarm optimization (QPSO), and then apply into the power shor...

The stock market is very volatile, so the change of price also widely concerned by investors. In this paper, a new forecasting model based on Quantum Particle Swarm Optimization(QPSO) , Bee Colony Optimization Algorithm(QABC) and Fruit Fly Algorithm (QFOA) proposed. three methods all use BP neural network to adjust parameters particle swarm, bee colony Drosophila reach optimal parameters. Takin...