Non-linear function solving method based on adaptive variable-step chaos wolf pack searching optimization algorithm

Abstract

The invention discloses a non-linear function solving method based on an adaptive variable-step chaos wolf pack searching optimization algorithm; the content comprises the following steps: initializing a wolf pack, and using a chaotic variable as a wolf pack initialization position; selecting a candidate wolf with the best position to be the leader wolf; allowing other artificial wolves to move to the leader wolf; allowing other wolves to execute siege behaviors by taking the leader wolf as center when the leader wolf finds a prey; updating the colony according to a principle in which the fittest survives; iterating said steps until the maximum iteration frequency is reached, and outputting the optimal solution. The method can improve a wolf pack search algorithm (LWPS) based on the leader strategy, and can solve the complex non-linear function solving problems; the non-linear function solving method can make up LWPS algorithmic insufficient, can provide good superiority in the searching optimization precision and convergence speed aspects, has better searching optimization capability, can more accurately and fast search the complex non-linear function optimal solution, thus enriching non-linear function solving theory methods.

Description

technical field [0001] The invention relates to the field of solving complex nonlinear functions, in particular to a method for solving nonlinear functions based on an adaptive variable step size chaotic wolf pack optimization algorithm. Background technique [0002] After a long period of natural selection and biological evolution, many wonderful swarm intelligence phenomena in nature have been created, which are amazing and have brought us endless scientific enlightenment. In order to solve complex nonlinear function problems, many bionic swarm intelligence optimization algorithms have been proposed, such as genetic algorithm (genetic algorithm, GA), particle swarm optimization (particle swarm optimization, PSO), artificial bee colony algorithm (artificial bee colony, ABC )Wait. Compared with the traditional optimization method, the swarm intelligence algorithm is simple to implement and can not be restricted by the search space and the shape of the objective function, wh...

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Problems solved by technology

[0005] In view of the above-mentioned existing problems, the present invention improves the LWPS algorithm and uses it to solve complex nonlinear function solving problems, and proposes a chaos wolf optimization algorithm with adaptive variable step, The nonlinear function solution method of CWOA) aims to make up for the shortcomings of the LWPS algorithm and enrich the theoretical methods for solving nonlinear functions.

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