quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. quantum-behaved particle swarm optimization algorithm is the integration of particleswarm...
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quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. quantum-behaved particle swarm optimization algorithm is the integration of particleswarmoptimizationalgorithm and quantum computing theory The superposition characteristic and probability representation of quantum methodology are combined into particleswarmoptimizationalgorithm This can make a single particle be expressed by several certain probability states. And the quantum rotation gates are used to realize update operation of particles. The algorithm is simulated by two cases, which validates it can effectively solve economic load dispatch problem. Though performance comparison, it is obvious the solution is superior to that of improved particleswarmoptimizationalgorithm and other optimizationalgorithms. (C) 2009 Elsevier Ltd. All rights reserved.
Aiming at the drawback of being easily trapped into the local optima and premature convergence in quantum-behaved particle swarm optimization algorithm, quantum-behaved particle swarm optimization algorithm with adapt...
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Aiming at the drawback of being easily trapped into the local optima and premature convergence in quantum-behaved particle swarm optimization algorithm, quantum-behaved particle swarm optimization algorithm with adaptive mutation based on q-Gaussian distribution is proposed. q-Gaussian mutation operator is applied to the mean best position of particles to overcome the drawback of premature convergence caused by loss of diversity in the population. In the evolution of population, adaptive adjustment of the nonextensive entropic index q balances exploration and exploitation. The simulation results of testing four standard benchmark functions and traveling salesman problem show that quantum-behaved particle swarm optimization algorithm with adaptive mutation based on q-Gaussian distribution has best optimization performance and robustness.
This paper is concerned with a significant issue in the research of nonlinear science, i.e., parameter identification of uncertain incommensurate fractional-order chaotic systems, which can be essentially formulated a...
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This paper is concerned with a significant issue in the research of nonlinear science, i.e., parameter identification of uncertain incommensurate fractional-order chaotic systems, which can be essentially formulated as a multidimensional optimization problem. Motivated by the basic particleswarmoptimization and quantum mechanics theories, an improved quantum-behavedparticleswarmoptimization (IQPSO) algorithm is proposed to tackle this complex optimization problem. In this work, both systematic parameters and fractional derivative orders are regarded as independent unknown parameters to be identified. Numerical simulations are conducted to identify two typical incommensurate fractional-order chaotic systems. Simulation results and comparisons analyses demonstrate that the proposed method is suitable for parameter identification with advantages of high effectiveness and efficiency. Moreover, we also, respectively, investigate the effect of systematic parameters, fractional derivative orders, and additional noise on the optimization performances. The corresponding results further validate the superior searching capabilities of the proposed algorithm.
Image registration based on mutual information is of high accuracy and robustness. Unfortunately, the mutual information function is generally not a smooth function but one containing many local maxima, which has a la...
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ISBN:
(纸本)9781467365932
Image registration based on mutual information is of high accuracy and robustness. Unfortunately, the mutual information function is generally not a smooth function but one containing many local maxima, which has a large influence on optimization. This paper proposes a registration method based on quantum-behaved particle swarm optimization algorithm. Not only QPSO has less parameters to con-troll, but also does its sampling space at each iteration covers the whole solution space. Thus QPSO can find the best solution quickly and guarantee to be global convergent. Experiments shows that this registration method could efficiently restrain local maxima of mutual information function and it can improve accuracy. Compare with the gold standard, the subvoxel accuracy can be achieved.
In this paper, a Two Sub-swarms quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy (TS-QPSO) is proposed. Two sub-swarms of particles with quantum Behavior are set up in TS-QPSO. Once the...
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ISBN:
(纸本)9780769540207
In this paper, a Two Sub-swarms quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy (TS-QPSO) is proposed. Two sub-swarms of particles with quantum Behavior are set up in TS-QPSO. Once the whole swarm falls into local optima and the best value of the global swarm is not improved after the allowable iterations, the exchange strategy will be carried out. The amount of exchange particles is different in each searching phase. In this way, the population diversity can be improved greatly and the problem that falling into local optima can be avoided effectively. Experiment results show that the overall performance of TS-QPSO is superior to QPSO algorithm and TSPSO algorithm.
This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references f...
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ISBN:
(纸本)9783037855034
This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particleswarmoptimizationalgorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.
This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behavedparticleswarmoptimization (QPSO) algorithm to solve the inverse problem. Automatic identifica...
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This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behavedparticleswarmoptimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particleswarmoptimizationalgorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.
Simultaneous synthesis problem of heat exchanger network is formulated as a mixed-integer nonlinear programming model. Limited by the Characteristic of the nonlinearity, the convexity and the discontinuity of the mode...
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ISBN:
(纸本)9781479947249
Simultaneous synthesis problem of heat exchanger network is formulated as a mixed-integer nonlinear programming model. Limited by the Characteristic of the nonlinearity, the convexity and the discontinuity of the model, the classical optimizationalgorithms which is used to solving the model are easy to fall into local minima. Based on the superstructure model with non-isothermal mixing of split stream, a two-level approach combined with quantum-behavedparticleswarmoptimization (QPSO) algorithm is proposed to find the optimum structure with a minimum annual cost. In the upper level, QPSO is utilized to generate the structure of the network, while in the lower level, split-stream fractions and heat load of exchangers are optimized by QPSO. Benchmark problems are solved and results show that the two level quantumparticleswarmalgorithm effectively avoids falling into local minima and it is feasible and effective for heat exchanger network problems.
In the paper, a new method for designing Cosine Modulated Filter Banks (CMFB) is proposed, the prototype filter is designed by optimization method. Firstly, the method appropriately relaxes the limit conditions fo...
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In the paper, a new method for designing Cosine Modulated Filter Banks (CMFB) is proposed, the prototype filter is designed by optimization method. Firstly, the method appropriately relaxes the limit conditions for perfect reconstruction, the design problem of CMFB is formulated as a nonlinear and unconstrained optimization of an objective function, which stopband cutoff frequency is fixed, passband cutoff frequency is adjusted to minimize the cost function which satisfies reconstruction condition, and directly designs the prototype filter. The simulation results illustrate the proposed method and its improvement over other methods in terms of amplitude distortion, aliasing distortion, signal to noise ratio and reconstruction performance.
Image registration based on mutual information is of high accuracy and ***, the mutual information function is generally not a smooth function but one containing many local maxima, which has a large influence on optim...
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Image registration based on mutual information is of high accuracy and ***, the mutual information function is generally not a smooth function but one containing many local maxima, which has a large influence on optimization. This paper proposes a registration method based on quantum-behaved particle swarm optimization algorithm. Not only QPSO has less parameters to con-troll, but also does its sampling space at each iteration covers the whole solution *** QPSO can find the best solution quickly and guarantee to be global convergent. Experiments shows that this registration method could efficiently restrain local maxima of mutual information function and it can improve *** with the gold standard, the subvoxel accuracy can be achieved.
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