Engineering parameter optimizing method and system

Abstract

The invention discloses an engineering parameter optimizing method. The engineering parameter optimizing method comprises the steps of constructing a target function corresponding to a preset engineering problem beforehand; utilizing a novel grey wolf algorithm to solve the target function on the premise that a constraint condition of the target function is satisfied, wherein the process of utilizing the novel grey wolf algorithm to solve the target function specifically comprises the steps of presetting a wolf pack; conducting iteration and renewal on the wolf pack for S times to obtain a renewed wolf pack; screening out a global optimum individual from the renewed wolf pack, and determining the dimension corresponding to the global optimum individual as an optimum engineering parameter, and the iteration and renewal process comprises the steps of renewing the wolf pack, conducting fitness calculation, and utilizing lengthways interlace operation to correct the renewing direction. According to the engineering parameter optimizing method, a competition strategy is added, the lengthways interlace operation in a crisscrossed algorithm is further utilized, and thus the probability that the wolf pack is involved into local optimum is avoided. Besides, the invention further discloses an engineering parameter optimizing system.

Description

technical field [0001] The invention relates to the technical field of engineering optimization, in particular to an engineering parameter optimization method and system. Background technique [0002] With the increasing maturity of system engineering theory research and the continuous development and application of modern computer technology, various new swarm intelligence optimization algorithms have been developed rapidly. Compared with traditional mathematical optimization methods, new swarm intelligence optimization algorithms do not need to rely too much on mathematical models. , when solving multi-constraint optimization problems, it is not necessary to simplify the mathematical model to avoid sacrificing the solution accuracy. Therefore, it has strong applicability and has become one of the hotspots in the field of optimization. [0003] In the prior art, Gray Wolf Algorithm is a new meta-heuristic search algorithm, which has the characteristics of simple structure,...

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Problems solved by technology

[0003] In the prior art, Gray Wolf Algorithm is a new meta-heuristic search algorithm, which has the characteristics of simple structure, few control parameters, easy implementation, and strong search ability. It is superior to the particle swarm algorithm in terms of accuracy and solution accuracy, and has a wide range of applications in the fields of feature subset selection, optimal control of DC motors, multiple input and multiple output power systems, multi-layer sensor training, and economic dispatch of power systems. There is still the defect that it is easy to fall into local optimum

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