Multi-target-based improved gray wolf optimization algorithm

Abstract

Embodiments of the invention disclose a multi-target-based improved gray wolf optimization algorithm which is used to solve the technical problems that a standard gray wolf algorithm falls into a local optimal value easily and has a low convergence speed and other defects while processing a multi-target optimization problem in the prior art. The method of the embodiments comprises the following steps: S1, setting a wolf pack initialization parameter and a direction correction probability, and randomly initializing wolves' individual positions; S2, calculating an adaptability value of each wolf individual according to a solving target, and selecting the three wolf individuals ranking top; S3, optimizing the wolves' individual positions of a wolf pack, generating moderate wolves, and updating a wolf pack position; S4, executing direction correction operation on the updated wolf pack, controlling the upgraded wolf pack to participate correction of the size of dimensions according to the direction correction probability, and obtaining a corrected wolf pack position; and S5, determining whether an iteration frequency reaches a preset maximum iteration frequency, outputting the corrected wolf pack position as a final optimization result if the iteration frequency reaches the preset maximum iteration frequency, and, if the iteration frequency does not reaches the preset maximum iteration frequency, turning to the S3 so as to continue performing iteration searching.

Description

technical field [0001] The invention relates to the technical field of multi-objective optimization algorithms, in particular to an improved gray wolf optimization algorithm based on multi-objectives. Background technique [0002] With the development and application of modern computer technology in the field of multi-objective optimization, various new methods and technologies emerge in an endless stream as the research on system engineering theory matures day by day. Common methods are mainly divided into two categories: one is to transform multi-objective into single-objective methods, mainly including weight coefficient method and membership function method. However, the rationality and effectiveness of the weight coefficient setting is a difficult problem for the weight coefficient method, and it cannot effectively deal with the target with a non-convex frontier; the membership function method has the defect of constructing rationality. The other is to use heuristic al...

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Problems solved by technology

[0006] The embodiment of the present invention provides an improved gray wolf optimization algorithm based on multi-objective, which solves the shortcomings of the standard gray wolf algorithm in the prior art, such as slow convergence speed and easy to fall into local optimum when dealing with multi-objective optimization problems. technical issues

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