Fuch mapping-based hybrid improved grey wolf optimization algorithm

Abstract

The invention discloses a Fuch mapping-based hybrid improved grey wolf optimization algorithm, which comprises the following steps: step 1, generating an initial population by using a chaotic elitist strategy, and laying a foundation for global search diversity; step 2, introducing an inertia weight matching convergence factor strategy, and adjusting population global exploration capability and local development capability; and step 3, introducing polynomial variation to the optimal grey wolf position to enhance the ability of the algorithm to jump out of the local optimal solution. The traditional grey wolf optimization algorithm is improved by utilizing a Fuch mapping hybrid improvement strategy, and compared with the traditional grey wolf optimization algorithm, the improved grey wolf optimization algorithm has the advantages that the convergence speed is increased, the convergence precision is improved, the calculation time is shortened, and a global optimal solution can be found to the maximum extent.

Description

technical field [0001] The invention relates to the technical field of bionic swarm intelligence optimization algorithms, in particular to a hybrid improved gray wolf optimization algorithm based on Fuch mapping. Background technique [0002] The gray wolf optimization algorithm is a new group bionic intelligent optimization algorithm, and its idea comes from the hunting behavior of gray wolf groups in nature. The algorithm mainly seeks the optimal prey through the gray wolf group to encircle and attack the prey. The algorithm is simple and easy to implement, with few adjustable parameters, and has been extensively researched and applied. But in practical engineering, many problems are complex designs with strong coupling, multiple parameters and nonlinearity. However, the traditional gray wolf optimization algorithm relies on the initial population, and the initial population is randomly generated, which will cause the traditional gray wolf optimization algorithm to fail ...

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Problems solved by technology

[0003] The purpose of the present invention is to solve the above-mentioned defects in the prior art, to provide a hybrid improved gray wolf optimization algorithm based on Fuch mapping, through the introduction of chaos elite reverse learning strategy, convergence factor and inertia weight collaborative update strategy and optimal position polynomial The mutation strategy improves the convergence accuracy, convergence speed and the ability to jump out of the local optimal solution of the traditional gray wolf optimization algorithm, so as to solve the problems of low convergence accuracy and low convergence accuracy of the traditional gray wolf optimization algorithm when dealing with strong coupling, multiple parameters and nonlinear problems. Slow convergence and easy to fall into technical problems such as local optimum

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