Flower pollination algorithm optimization method based on adaptive Gaussian mutation

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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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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