A task flow scheduling method based on DAG artificial bee colony

By using a task flow scheduling method based on DAG artificial bee colonies, the task flow allocation scheme is optimized, which solves the problem of slow convergence speed in traditional algorithms in task flow scheduling, and achieves faster task flow completion time and higher scheduling efficiency.

CN116302386BActive Publication Date: 2026-06-26HANGZHOU DIANZI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HANGZHOU DIANZI UNIV
Filing Date
2022-12-09
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Traditional metaheuristic algorithms and hybrid metaheuristic algorithms have slow convergence speed in task flow scheduling, are prone to getting trapped in local optima, and require a long running time, making them unable to effectively solve the NP-Hard problem.

Method used

A task flow scheduling method based on DAG artificial bee colonies is adopted. By constructing a bee colony model and generating nectar sources, the task flow allocation scheme is optimized by utilizing the search mechanism of foraging bees and observation bees, combined with roulette wheel algorithm and discrete algorithm, thereby narrowing the search range of solution space and improving search efficiency.

Benefits of technology

It shortens the task flow completion time, enables the determination of a reasonable scheduling scheme in a shorter time, improves the efficiency of task flow scheduling, and is suitable for the scheduling needs of small-scale task flows.

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Abstract

The application belongs to the technical field of task scheduling, and particularly relates to a task flow scheduling method based on DAG artificial bee colony, which comprises the following steps: constructing a directed acyclic graph through a task flow; applying the directed acyclic graph to initial solution generation of an artificial bee colony algorithm; and obtaining a distribution scheme of the task flow through the artificial bee colony algorithm. The application not only can accelerate the execution time of the task flow as a whole, but also enhances the local search capability, overcomes the problem of insufficient search capability of the original algorithm, and can determine a relatively reasonable scheduling scheme in a relatively short time, so that the whole method is more practical.
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