Resource scheduling optimization method based on binary space partitioning tree

Abstract

The embodiment of the invention provides a resource scheduling optimization method based on a binary space partitioning tree. The binary space partitioning tree drives search, when overlapping individuals occur under the action of a selection operator, self-variation drive of a genetic algorithm that local search, neighborhood search and cross-domain search are carried out in a search space with directivity is achieved, and therefore the overlapping individuals are effectively prevented from occurring, population diversity is kept, and random search of the genetic algorithm is changed.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a resource scheduling optimization method based on a binary space partition tree. Background technique [0002] Genetic Algorithm is based on Darwin's theory of evolution and Mendel's genetic theory, simulates the basic characteristics of biological evolution reproduction, variation, competition, and selection, and proposes a self-organizing and adaptive artificial algorithm that solves real problems through three operations: selection, recombination, and variation Intelligent Algorithm. Chromosome, as the main carrier of genetic algorithm, is composed of several genes with certain characteristics. The genetic algorithm uses genetic operators to perform crossover and mutation operations on the chromosomes of the contemporary population to generate offspring populations; the crossover operator has a strong search ability, and the diversity of the population determines the se...

AI Technical Summary

Problems solved by technology

The genetic algorithm uses genetic operators to perform crossover and mutation operations on the chromosomes of the contemporary population to generate offspring populations; the crossover operator has a strong search ability, and the diversity of the population determines the search ability of the crossover operator; the mutation operator uses mutation The operation can generate chromosomal genes that are not in the contemporary population to maintain the diversity of population individuals [137], and experiments have found that if the mutation effect is cancelled, the genetic algorithm cannot tend to converge

[0003] The invention of this scheme found in the process of researching the traditional genetic algorithm, because the mutation operation of the traditional genetic algorithm has only a fixed empirical probability value, the mutation operation is random, which leads to the non-directional search of the parameter space, and brings A large number of invalid calculations lead to poor convergence of the algorithm; and under the action of selection and crossover genetic operators in the standard genetic algorithm, a large number of repeated individuals will inevitably be generated during the evolution process, and the mutation operation is random and blind sexual flaw

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