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Spark-based parallel genetic algorithm

A technology of genetic algorithm and genetic operation, applied in the direction of genetic rules, calculation, calculation model, etc., can solve problems such as long calculation time and genetic algorithm calculation performance problems, and achieve the effect of improving performance

Inactive Publication Date: 2018-06-22
HOHAI UNIV
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AI Technical Summary

Problems solved by technology

When the traditional genetic algorithm is used to solve complex optimization problems, it usually takes a long time to calculate
[0003] In order to solve the calculation performance problem brought by the genetic algorithm, the present invention provides a parallel genetic algorithm based on Spark

Method used

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Embodiment Construction

[0023] The technical solution will be described in detail below through a preferred embodiment and in conjunction with the accompanying drawings.

[0024] Such as figure 1 As shown, a Spark-based parallel genetic algorithm includes the following steps:

[0025] (1) Parallelization of fitness value calculation: Randomly generate an initial population, create a Spark RDD from the initial population, and divide the RDD into multiple partitions and distribute them to multiple nodes in the cluster. Each partition corresponds to a subpopulation, and each subpopulation The population calculates the fitness value on their respective nodes, and returns the calculation results to the master node of Spark. The parallelization process of the fitness value calculation is as follows: figure 2 As shown, it specifically includes the following sub-steps:

[0026] (1.1) Randomly generate the initial population. The randomly generated initial population is converted into a population RDD thro...

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Abstract

The invention discloses a Spark-based parallel genetic algorithm, including fitness value calculation parallelization and genetic manipulation parallelization, an RDD of Spark is created from an initial population, the RDD is divided into a plurality of partitions to be distributed to a plurality of nodes of a cluster, each partition corresponds to a sub-population, calculation of a fitness valueis performed on each sub-population at respective nodes, and a calculation result is returned to a major node of Spark. The population with the fitness value is divided into a plurality of sub-populations which serve as the plurality of partitions of the RDD to be distributed to the plurality of nodes of the cluster again, each sub-population independently evolves at respective nodes, the best individuals in the different partitions of the RDD are collected when evolution meets an end condition, and the result is returned to the major node of Spark. The Spark-based parallel genetic algorithm provided by the invention utilizes a memory-based calculation model of Spark, the genetic algorithm is parallelized in the two aspects of fitness value calculation and genetic manipulation, thereby improving performance of the genetic algorithm.

Description

technical field [0001] The invention relates to a parallelized genetic algorithm, in particular to a parallelized genetic algorithm based on Spark. Background technique [0002] Genetic algorithm is a meta-heuristic search technique that simulates the biological evolution process and mechanism in nature, and is widely used to solve complex optimization problems. Usually, it is not feasible to exhaustively search the complete input space, and the genetic algorithm can be used to find a good problem solution in a reasonable time by searching a smaller input space. The traditional genetic algorithm finds the optimal solution to the problem by sequentially performing genetic operations such as selection, crossover, and mutation. When the traditional genetic algorithm is used to solve complex optimization problems, it usually requires a long calculation time. [0003] In order to solve the calculation performance problem caused by the genetic algorithm, the present invention pr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12G06N3/00
CPCG06N3/126G06N3/006
Inventor 戚荣志李水艳曾涛安纪存
Owner HOHAI UNIV
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