Improved Quantum Particle Swarm Optimization Algorithm and Its Applied Method for Predicting Network Traffic
A quantum particle swarm and particle swarm technology, applied in the field of improved quantum particle swarm optimization algorithm, can solve problems such as premature convergence, decrease in particle diversity, and slow convergence speed, so as to prevent rapid convergence, increase algorithm complexity, and solve The effect of premature convergence
Inactive Publication Date: 2021-06-25
NORTHEASTERN UNIV LIAONING
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[0006] Embodiments of the present invention provide an improved quantum particle swarm optimization algorithm and a method for predicting network traffic, so as to at least solve the problem of premature convergence when dealing with complex problems. In the late stage of algorithm iteration, the diversity of particles decreases rapidly. Technical Issues with Slower Convergence
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[0030] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.
[0031] It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate ...
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The invention discloses an improved quantum particle swarm optimization algorithm and a method for predicting network flow. Wherein, the method includes: randomly generating the contraction expansion coefficient through the aggregation degree of the particle group, wherein the aggregation degree of the particle group refers to the similarity and degree of aggregation between the particles of the particle group, and the contraction expansion coefficient obeys random distribution; according to the contraction expansion Coefficient updates the position of the particle. The invention solves the problem that premature convergence is easy to occur when dealing with complex problems, and the technical problem that the particle diversity decreases rapidly and the convergence speed is slow in the later stage of algorithm iteration.
Description
technical field [0001] The invention relates to the field of predicting network traffic, in particular to an improved quantum particle swarm optimization algorithm and a method applied to predicting network traffic. Background technique [0002] At present, the existing technology in the financial field has a quantum particle swarm optimization recursive neural network method for forecasting financial time series. Specifically, it first applies the theory of chaos and phase space reconstruction, and calculates the chaotic financial time series through the saturated correlation dimension (G-P) method. Attractor dimension, determine the structure of the neural network RPNN, then train the recursive neural network RPNN through the quantum particle swarm optimization QPSO algorithm, and finally determine the dynamic optimal weight and threshold of the network, so that the predicted value and actual value of the RPNN neural network simulation can reach The minimum error accuracy,...
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IPC IPC(8): H04L12/24G06N3/00
CPCG06N3/006H04L41/142H04L41/145H04L41/147
Inventor 于尧郭磊滕飞
Owner NORTHEASTERN UNIV LIAONING