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An intelligent optimization algorithm for adaptive dynamic evolution of behavior parameters

An intelligent optimization algorithm and dynamic evolution technology, applied in computing, computing models, instruments, etc., can solve problems such as inconvenient selection of behavioral parameters, and achieve the effect of improving convergence speed and optimization accuracy

Inactive Publication Date: 2019-01-04
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0003] The purpose of the present invention is to provide an intelligent optimization algorithm for adaptive dynamic evolution of behavioral parameters, which solves the problem of inconvenient selection of behavioral parameters of particle swarm optimization algorithm in the prior art

Method used

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  • An intelligent optimization algorithm for adaptive dynamic evolution of behavior parameters
  • An intelligent optimization algorithm for adaptive dynamic evolution of behavior parameters
  • An intelligent optimization algorithm for adaptive dynamic evolution of behavior parameters

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Embodiment 1

[0052] Embodiment 1: as figure 1 As shown, an intelligent optimization algorithm for adaptive dynamic evolution of behavioral parameters mainly includes the following steps:

[0053] Step1 Initialize the population space according to the optimized problem, that is, set the population size, dimension, and evolutionary algebra;

[0054] Step2 Calculate the value interval of each behavior parameter according to the optimized problem, and use the divergence tree method to perform evolution calculation on the behavior parameter set to obtain the set U; use the divergence tree method to perform evolution calculation on the behavior parameter set, including the first-order unstable evolution of behavior parameters and The second-level new stable evolution, the first-level unstable evolution is to use the matter-element extension method to develop multiple parameter configuration matter-elements along different paths for the selected population behavior parameters, that is, a divergen...

Embodiment 2

[0089] Embodiment 2: as figure 2 As shown in , an intelligent optimization algorithm for adaptive dynamic evolution of behavioral parameters, the following uses the Greiwank function as an example to illustrate the specific use and effect of this method.

[0090]

[0091] The Greiwank function is a typical nonlinear multimodal function, which has a wide search space and a large number of local optimal points as obstacles. It is a complex modal problem that many optimization algorithms are difficult to deal with. Using this method (ERTPSO algorithm) to optimize the parameters of the Greiwank function, first set the following settings, the population size is set to 30, the dimension is set to 10, the evolutionary algebra is set to 1000, and the value interval of the calculation behavior parameter is ψ 1 ={[0.1,0.5],[0.5,0.9],[0.9,1.5],[10,20]}, ψ 2 ={[0,2],[2,4]},ψ 3 ={[0,2],[2,4]}. Then carry out optimization calculation according to the step described in embodiment 1, a...

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Abstract

The invention relates to an intelligent optimization algorithm of adaptive dynamic evolution of behavior parameters, which relates to the optimization field of particle swarm algorithm. At first, thatpopulation space is initialized according to the optimize problem, the behavior parameter value interval is calculated according to the optimized problem, and the behavior parameter set is calculatedby using the divergent tree method to obtain the set U; the behavior parameters are randomly selected in the set U to form a plurality of groups of behavior parameter combinations, and the optimal values of the current population corresponding to each group of behavior parameters are linearly sorted; a Borda number is obtained for each group of behavior parameter, and the behavior parameters corresponding to the maximum Borda numb are substituted into the current iterative computation process of the population; the behavior parameters are used as the parameters of the current generation of particles to generate the next generation of particles until the current optimal fitness of the population reaches the end of the standard. The method can quickly and accurately select the next generation of the optimal algorithm parameters, so that the convergence of particle swarm optimization algorithm independent of the parameters, effectively improve the convergence rate of particle swarm optimization algorithm.

Description

technical field [0001] The invention belongs to the technical field of particle swarm algorithm optimization, and relates to an intelligent optimization algorithm for adaptive dynamic evolution of behavior parameters. Background technique [0002] Particle swarm optimization algorithm has the advantages of simple concept, easy implementation, good robustness, and can avoid complex genetic operations. The decisive factor of its performance and efficiency is the selection of behavioral parameters of particle swarm optimization algorithm. Although some researchers have done some research on the selection of behavioral parameters of particle swarm optimization algorithm, it is difficult to achieve a balance between improving the algorithm search speed and optimization accuracy in the proposed method. Therefore, in order to effectively improve the performance and efficiency of particle swarm optimization algorithm, it is necessary to propose an effective method for selecting beha...

Claims

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 吴仁杰阴艳超徐凯张立童陈富钊
Owner KUNMING UNIV OF SCI & TECH
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