Flower pollination algorithm optimization method based on adaptive Gaussian mutation

A technology of Gaussian mutation and optimization method, applied in calculation, calculation model, instrument, etc., can solve problems such as slow convergence speed, easy to fall into local optimum, and weak local depth search ability of flower pollination algorithm, so as to improve the ability of falling into the local area , enhance the local search ability, improve the effect of the local search ability

Inactive Publication Date: 2017-03-08
CHANGSHU INSTITUTE OF TECHNOLOGY
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Problems solved by technology

[0005] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide an optimization method of flower pollination algorithm based on adaptive Gaussian variation, to solve the problems of weak local depth search ability of flower pollination algorithm, easy to fall into local optimum, and slow convergence speed in the later stage

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  • Flower pollination algorithm optimization method based on adaptive Gaussian mutation
  • Flower pollination algorithm optimization method based on adaptive Gaussian mutation
  • Flower pollination algorithm optimization method based on adaptive Gaussian mutation

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[0055] The present invention will be further described below in conjunction with the examples, but not as a limitation of the present invention.

[0056] Please combine figure 1 , the specific implementation of the optimization method of the flower pollination algorithm based on adaptive Gaussian variation is as follows:

[0057] Step 1. Initialize the basic parameters and population position. The initialization basic parameters include setting the population size to NP, the maximum number of iterations to itermax, and the cross-pollination probability P C , minimum convergence precision F min And search space D, etc., initialize the population position: randomly generate NP points in the feasible region (D-dimensional space) as the initial population in t is the current iteration number, D is the search space;

[0058] Step 2. According to the selected objective function, calculate the fitness value f(x i ) (take minimization as an example), and select the individual w...

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Abstract

The invention discloses a flower pollination algorithm optimization method based on adaptive Gaussian mutation. Firstly, the population is firstly ranked according to adaptability values and is grouped, and then the position of the worst individual in each group is updated, thereby enhancing the local depth search ability of the algorithm and enhancing the population diversity; and secondly, whether the algorithm falls into the local optimization is dynamically monitored through a bulletin board, and if yes, a Gaussian mutation operation operator is automatically introduced, mutation operation is executed on the global optimal individual, thereby not only improving the ability of the individual jumping out of the local optimization but also enhancing the population diversity and quickening the convergence rate. The method of the invention has better stability, better reliability, quicker convergence rate and higher optimization precision.

Description

technical field [0001] The invention relates to an optimization method of a flower pollination algorithm, in particular to an optimization method of a flower pollination algorithm based on self-adaptive Gaussian variation. Background technique [0002] The core problem in optimization problems is to solve the global optimal solution or optimal solution set in the feasible region, and then find the optimal solution of the problem from these optimal solutions or solution sets. However, as the scale of the problem expands, the time required to solve it increases exponentially, or the optimization function requires continuous derivation, etc. Therefore, the development of simple and efficient optimization algorithms is the focus of researchers. The swarm intelligence optimization algorithm based on bionics is a simple and effective way to solve optimization problems. The swarm intelligence algorithm is to simulate some behavioral characteristics of a certain creature in the biol...

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 张明新戴娇郑金龙孙昊彭颖王子清
Owner CHANGSHU INSTITUTE OF TECHNOLOGY
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