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Prediction model method based on an improved moth optimization algorithm

A prediction model and optimization algorithm technology, applied in the computer field, can solve problems such as difficult to find the global optimal solution, trapped in local optimal, etc.

Pending Publication Date: 2019-02-15
WENZHOU UNIVERSITY
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Problems solved by technology

[0004] However, when this algorithm deals with complex optimization problems (such as problems with a large number of local optimal solutions), it is very easy to fall into local optimum, and it is difficult to find the global optimal solution.

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  • Prediction model method based on an improved moth optimization algorithm
  • Prediction model method based on an improved moth optimization algorithm
  • Prediction model method based on an improved moth optimization algorithm

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[0053] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0054] Such as figure 1 Shown is a prediction model method based on an improved moth optimization algorithm proposed in an embodiment of the present invention, and the method includes the following steps:

[0055] Step S1: Parameter initialization; among them, the parameters to be initialized include: the maximum number of iterations T, the number of moths L, the search space of the penalty coefficient C [C min , C max ] And nuclear width γ search space [γ min , Γ max ]; T and L are both positive integers;

[0056] Step S2: Initialize the positions of the L moths, and use the following formulas (1)-(2) to classify all the positions of the moths into the specified search range, and obtain the updated position X of the L moths m =(x m，1 , X m，2 )(m = 1, 2..., L); whe...

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Abstract

The invention provides a prediction model method based on an improved moth optimization algorithm, which comprises the steps of loading a data set and performing standardized processing on sample data; Gaussian mutation strategy and chaotic agitation are used to improve the moth flame optimization algorithm, and support vector machine model and / or limit learning machine model are constructed by using the improved moth optimization algorithm. The implementation of the invention can not only increase the diversity of the population and enhance the searching ability of the algorithm, but also prevent the algorithm from falling into the local optimum and quickly finding the global optimum solution.

Description

Technical field [0001] The invention relates to the field of computer technology, in particular to a prediction model method based on an improved moth optimization algorithm. Background technique [0002] The swarm intelligence optimization algorithm is a random search algorithm that simulates and models the social behavior and foraging behavior of different biological groups in nature. It is different from the traditional random algorithm. As the search process proceeds, the algorithm will perform a full search in the search space. When the search process reaches a certain stage, the algorithm will perform a deeper search around the optimal solution to get more High-quality solutions. The above intelligent algorithms are well-known such as: particle swarm algorithm, gray wolf optimization algorithm, ant colony algorithm, etc. [0003] The moth flame optimization algorithm is a new type of intelligent optimization algorithm proposed by Mirjalili et al. in 2015. The algorithm sim...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00
CPCG06Q10/04G06N3/006
Inventor 徐粤婷陈慧灵罗杰张谦焦珊陈昊赵学华
Owner WENZHOU UNIVERSITY
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