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

A technology of nonlinear function and optimization algorithm, which is applied in the field of complex nonlinear function solution to achieve the effect of improving fine search ability and improving optimization efficiency.

Inactive Publication Date: 2017-03-15
YANSHAN UNIV
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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 a

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

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Embodiment Construction

[0043] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0044] see figure 1 , is the flow chart of the inventive method, a kind of non-linear function solution method based on adaptive variable step-size chaotic wolf pack optimization algorithm (CWOA), and its specific implementation steps include the following contents:

[0045] Step 1: Initialize the wolves

[0046] Initialize the number N of artificial wolves in the wolf pack, the dimension of the search space D, and the value range of the search space [w dmax ,w dmin ], the maximum number of iterations n max , the number of wolves running for the leader q, the maximum number of searches H max , search direction h, search step initial value stepa 0 , moving step stepb, siege threshold r 0 , the initial value of the siege step size stepc 0 , eliminate the number m of wolves; use the Logistic chaotic mapping expression (1) to generate N cha...

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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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Application Information

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IPC IPC(8): G06F17/15
CPCG06F17/15
Inventor 姜万录朱勇
Owner YANSHAN UNIV
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