Local search and global search fusion method and system based on differential evolution algorithm

Abstract

The invention discloses a local search and global search fusion method and a system based on a differential evolution algorithm, and the method comprises the steps: carrying out the initialization configuration of population parameters, and generating an initial population; configuring a local search algebra counter and a global search algebra counter; when the population enters a local search stage, determining that a local search algebra counter is located between an upper limit and a lower limit and the sub-population is not converged, and enabling the population to enter a global search stage; judging whether the value of the global search algebra counter is smaller than the upper limit of the global search algebra, and if yes, re-executing the global search stage; and otherwise, returning to execute the step of configuring the local search algebra counter and the global search algebra counter, and ending the differential evolution algorithm until the termination condition of execution of the initially configured differential evolution algorithm is determined to be met. According to the method, the convergence and exploratory performance of the population are enhanced, the comprehensiveness of search is improved, the method is efficient and low in complexity, and the method can be widely applied to the technical field of numerical optimization.

Description

technical field [0001] The invention relates to the technical field of numerical optimization, in particular to a local search and global search fusion method and system based on a differential evolution algorithm. Background technique [0002] In the fields of science and engineering, we often encounter some global optimization or combinatorial optimization problems. These problems generally have the characteristics of wide optimization range and nonlinearity. It is often complicated to use artificial methods to optimize and find the optimal solutions to these problems. The global optimal solution of these problems can be easily obtained by using the optimization algorithm. [0003] The differential evolution algorithm was proposed by R.Storn and K.Price in 1995, and has now become one of the efficient heuristic search algorithms for solving various continuous optimization problems, and it is also used in pattern recognition, machine learning, neural network training, Inte...

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Problems solved by technology

There are also some differential evolution algorithms that combine global search and local search, also known as meme differential evolution algorithms. These algorithms mainly combine differential evolution algorithms and local search algorithms such as hill climbing and annealing algorithms. The time complexity of the search algorithm is not very low, and it is easy to fall into a local optimum, thus losing the diversity of the population

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