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Optimization method of artificial bee colony algorithm based on multiple improvement strategies

A technology of artificial bee colony algorithm and optimization method, applied in computing, computing model, artificial life and other directions, to achieve the effect of strong global optimization ability, excellent performance and improved accuracy

Inactive Publication Date: 2020-06-16
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] The present invention is for overcoming above-mentioned weak point, the purpose is to provide a kind of optimization method based on the artificial bee colony algorithm of multiple improvement strategies, the present invention respectively to differential evolution strategy (DES), triangular factor oscillation strategy (TFOS), different dimension The learning strategy (DDVLS) and the Gaussian distribution strategy (GDS) are improved to enhance the global search ability of the algorithm, improve the accuracy of understanding, and finally obtain the global optimal solution, thus effectively overcoming the shortcomings of the ABC algorithm and improving the accuracy rate. And the convergence speed is accelerated, achieving the effect of optimizing the classic ABC algorithm

Method used

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  • Optimization method of artificial bee colony algorithm based on multiple improvement strategies
  • Optimization method of artificial bee colony algorithm based on multiple improvement strategies
  • Optimization method of artificial bee colony algorithm based on multiple improvement strategies

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[0021] Example: such as figure 1 As shown, an optimization method of artificial bee colony algorithm based on multiple improvement strategies is based on MISABC algorithm to optimize eight representative standard test functions and find their minimum values. The specific steps are as follows:

[0022] (1) Use block coding to determine the parameters of artificial bee colony algorithm with multiple improved strategies and initialize the population; then calculate the fitness value of each individual in the population and determine the capacity limit to discard individuals that do not meet the capacity requirements; Specifically, first set the parameters of the MISABC algorithm, that is, set the number of initial bee colonies NP, the number of food sources NP / 2, the control parameter limit, the maximum number of cycles MaxCycle, and the D-dimensional solution space, and randomly generate the initial solution X in the solution space i (i=1, 2,..., NP), calculate its fitness value...

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Abstract

The invention relates to an optimization method of an artificial bee colony algorithm based on multiple improved strategies. According to the invention, a differential evolution strategy (DES), a trigonometric factor oscillation strategy (TFOS), a heterogeneous learning strategy (DDVLS) and a Gaussian distribution strategy (GDS) are improved, the global search capability of the algorithm is enhanced, the understanding precision is improved, and finally, a global optimal solution is obtained, so that the defects of the ABC algorithm are effectively overcome, the accuracy is improved, the convergence speed is increased, and the effect of optimizing the classical ABC algorithm is achieved.

Description

technical field [0001] The invention relates to the technical field of swarm intelligence optimization, in particular to an optimization method of an artificial bee colony algorithm based on multiple improvement strategies. Background technique [0002] Swarm intelligence optimization algorithm is a stochastic optimization method based on swarm intelligence algorithm. Different from the traditional mathematical optimization method, the swarm intelligence optimization algorithm performs a random search in the solution space through the continuous iterative evolution of the search agent. In recent decades, scholars in this research field have proposed many swarm intelligence algorithms, most of which are inspired by the behaviors of animal groups in nature such as movement and reproduction. [0003] Because the swarm intelligence optimization algorithm has the advantages of simple implementation, strong flexibility, high robustness and no gradient method, it has been successf...

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006Y02P90/30
Inventor 刘志李鹏航朱李楠沈国江
Owner ZHEJIANG UNIV OF TECH
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