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Optimization method of hybrid bat algorithm and optimization method of multi-layer perceptron

A technology of bat algorithm and optimization method, applied in neural learning methods, instruments, computing and other directions, can solve problems such as poor accuracy and stability

Inactive Publication Date: 2020-01-24
WUHAN UNIV
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Problems solved by technology

[0006] In view of this, the present invention provides an optimization method of a hybrid bat algorithm and an optimization method of a multi-layer perceptron to solve or at least partially solve the technical problems of poor accuracy and stability in the methods of the prior art

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Embodiment 1

[0116] This embodiment provides a hybrid bat algorithm optimization method, please refer to figure 1 , the method includes:

[0117] Step S1: Set initialization parameters, initialization parameters include population size N, fitness objective function, loudness A i ∈[1,2], pulse emission rate ri∈[0,1], maximum number of iterations N_gen, frequency f i ∈[f min , f max ], pulse frequency enhancement coefficient γ, and loudness attenuation coefficient α, where both γ and α are constants.

[0118] Specifically, initialization parameters can be set according to actual conditions. In one embodiment, N=40, loudness A i Take a random number between [1, 2], pulse emission rate r i Take a random number between [0, 1], the maximum number of iterations N_gen=200, f min = 0, f max =100, γ=α=0.9.

[0119] Step S2: Initialize the bat population and speed, where the population is initialized according to formula (1), and the speed v i Can be initialized to 0;

[0120] x ij =x jm...

Embodiment 2

[0202] Based on the same inventive concept, this embodiment provides a method for optimizing the multi-layer perceptron of the hybrid bat optimization algorithm obtained by the optimization method described in Embodiment 1. Please refer to Figure 4 , the method includes:

[0203] Step S1: express the weight and bias value of the multi-layer perceptron MLP (multi-layer perceptron) as the vector form of formula (25):

[0204]

[0205] In the formula, Represents the vector used to hold all connection weights in the multilayer perceptron, Represents the vector used to hold all the bias values ​​in the multilayer perceptron.

[0206] Specifically, multi-layer perceptron (MLP) optimization can be found in Mirjalili's literature (Mirjalili, S. (2015). How effective is the Gray Wolf optimizer in training multi-layer perceptrons. Applied Intelligence, 43(1), 150-161. ) method proposed in . The present invention applies the optimized Hybrid Bat Algorithm (ERFBA) obtained in Em...

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Abstract

The invention discloses an optimization method of a hybrid bat algorithm and a multilayer perceptron optimization method, and the method comprises the steps: firstly, improving an original reverse learning strategy in the aspect of population reconstruction, and proposing disturbance multi-strategy reverse learning, so as to reconstruct an effective diversified search space; secondly, in the aspect of global exploration and search, by introducing a whale optimization algorithm and improving the whale optimization algorithm, providing a self-adaptive constraint step length whale optimization algorithm so as to make up for the defects of an original bat algorithm in the aspect of global exploration capacity; thirdly, in the aspect of local mining search, providing a bat algorithm based on Cauchy variation and dynamic correction by introducing Cauchy variation and designing a dynamic correction strategy, so that the local search capability of the original bat algorithm is improved; and fourthly, under the synergistic effect of the three strategies, effectively improving the precision and stability of an optimization result by the new algorithm obtained by optimization; finally, applying the new algorithm to the multi-layer perceptron training problem, and obtaining relatively high classification precision.

Description

technical field [0001] The invention relates to the technical field of intelligent computing, in particular to an optimization method of a hybrid bat algorithm and an optimization method of a multilayer perceptron. Background technique [0002] Bat algorithm (Bat algorithm, BA) was proposed by Yang (Professor Yang Xinshe) in 2010. It is one of the more popular meta-heuristic algorithms in recent years. The algorithm has the advantages of few setting parameters, simple structure and fast convergence speed. Because the meta-heuristic algorithm searches for the optimal solution, it has certain randomness. As a new heuristic algorithm, the bat algorithm mainly simulates the behavior of bats in nature using ultrasonic waves to randomly search for food and avoid obstacles in the global target space. Therefore, the quality of the initialization population and the search strategy directly affect the efficiency of the bat algorithm in searching for the global optimal solution. [...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06N3/04G06N3/08
CPCG06N3/006G06N3/08G06N3/045
Inventor 何发智罗锦坤李浩然雍嘉诗
Owner WUHAN UNIV
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