Hybrid global optimization method

Abstract

The invention relates to a hybrid global optimization method. A particle swarm algorithm is used for solving an optimization problem to obtain one group of current optimal solutions; a particle jumpsout of a local extremum by using a chaotic searching algorithm; and local optimal point searching is accelerated by introducing a sequential quadratic programming algorithm into the each generation ofiteration process of the particle swarm algorithm, so that a global optimal solution to the optimization problem is obtained. According to the invention, the concept of particle swarm fitness variance is introduced and the chaotic search and sequential quadratic programming method are combined. When the particle swarm fitness variance is smaller than a given critical value, the particle is easy to fall into local optimum; and chaotic searching is carried out on the optimal particle, so that the particle jumps out of the local optimum. Moreover, according to the particle evolutionary speed andthe particle aggregation degree, the inertia weight is changed adaptively, so that the motion state of the particle is changed and thus the particle is protected from falling into local optimum. During the each iteration process of the particle, the sequential quadratic programming optimization is introduced, so that the searching of the local optimal point of the particle is accelerated and theoverall searching efficiency of the algorithm is improved.

Description

technical field [0001] The invention relates to an optimization method, in particular to a mixed global optimization method. Background technique [0002] Optimization methods have made great progress and many theoretical research and application results since the 1960s. Existing optimization methods can be mainly divided into two categories: traditional deterministic optimization methods and intelligent optimization methods. [0003] The deterministic optimization method represented by the SQP method has very good experimental results, but it also has limitations. First, it can only solve smooth nonlinear optimization problems; second, the optimal solution obtained is only a local optimal solution; finally, it is only suitable for solving small and medium-sized problems. Inspired by the phenomenon of adaptive optimization in nature, and with the development of artificial intelligence, the development of intelligent optimization algorithms for solving complex optimization ...

AI Technical Summary

Problems solved by technology

[0010] Although the above three hybrid optimization methods improve the search accuracy and search efficiency, there are still problems that it is difficult to jump out of the local optimum.

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