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Crossover probability factor adjustable differential evolution algorithm

A technology of differential evolution algorithm and crossover probability factor, which is applied in computing, computing models, instruments, etc., can solve problems such as performance differences and differences, and achieve the effects of reducing computing time, iteration times, and memory consumption

Inactive Publication Date: 2018-04-13
SOUTHEAST UNIV
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Problems solved by technology

For this reason, many experts have proposed a variety of mutation strategies, but different strategies have different performances when facing different problems, and the problems they are suitable for solving are also different.

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  • Crossover probability factor adjustable differential evolution algorithm
  • Crossover probability factor adjustable differential evolution algorithm
  • Crossover probability factor adjustable differential evolution algorithm

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Embodiment Construction

[0035] The flow chart of the differential evolution algorithm numerical optimization that the present invention proposes is as figure 1 As shown, the algorithm first initializes the optimization vector, and then performs mutation, crossover, and selection operations to obtain the next generation of optimization vectors, and iterates until the fitness value returned by the objective function is lower than the set threshold or the number of iterations reaches the set Maximum number of iterations. The invention proposes a new crossover probability determination scheme to improve optimization efficiency and reduce required iteration times. The results given in the examples show that the differential evolution scheme proposed by the present invention can efficiently complete the numerical optimization task.

[0036] Specific steps are as follows:

[0037] Step 1): Population initialization

[0038] The differential evolution algorithm is also a group-type operation algorithm, an...

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Abstract

The invention discloses a crossover probability factor adjustable differential evolution algorithm. The algorithm comprises the following steps of (1) performing population initialization; (2) performing mutation operation; (3) performing crossover operation; (4) selecting an operator; and (5) stopping iteration. According to the crossover probability factor adjustable differential evolution algorithm provided by the invention, in a crossover link, different individuals are endowed with different crossover probability factor values according to the previous-generation fitness values of the different individuals; and compared with a conventional DE algorithm, when the crossover probability factor adjustable differential evolution algorithm is used for performing numerical optimization, theneeded iteration frequency is remarkably reduced, an expected optimal value can be found more quickly, and the calculation time and the memory consumption are effectively shortened and reduced.

Description

technical field [0001] The invention relates to the technical field of numerical optimization, in particular to a differential evolution algorithm with an adjustable crossover probability factor. Background technique [0002] The Differential Evolution (DE) algorithm is an optimization algorithm jointly proposed by Rainer Storn and Kenneth Price in 1997 that uses floating-point vector encoding to perform random search in continuous space. Since the advent of the DE algorithm, it has attracted the attention of many experts and scholars at home and abroad because of its simplicity and high efficiency, and the corresponding research results in different fields have emerged as the times require. Today, DE has become an effective method for solving nonlinear, non-differentiable, and multi-extreme optimization problems. It has achieved good application results in planning and other engineering fields. [0003] Although the DE algorithm has been widely used in many different fiel...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06N3/04
CPCG06N3/006G06N3/047
Inventor 蒋忠进崔铁军陈阳阳
Owner SOUTHEAST UNIV
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