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Self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling

A multi-objective evolution and self-adaptive technology, applied in office automation, genetic laws, data processing applications, etc., can solve problems such as easy to fall into local optimum, and achieve excellent global detection and local mining capabilities, excellent performance, good convergence and The effect of diversity

Active Publication Date: 2017-06-13
北京明易达科技股份有限公司
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide an adaptive multi-objective evolutionary method with constrained cloud workflow scheduling to solve the problem that existing multi-objective evolutionary algorithms in the prior art tend to fall into local optimum when using static penalty functions to deal with constraints. excellent question

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  • Self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling
  • Self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling
  • Self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling

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Embodiment approach 1

[0138] In this embodiment, the time limit values ​​are respectively set to time limit 1, time limit 2, time limit 3, and time limit 4. The scientific workflow model Epigenomics is selected, and the ai-NSGA-II-PE and the NSGA-II algorithm based on Pareto entropy (ParetoEntropy based onNSGA-II, NSGA-II-PE) (NSGA-II-PE adopts the adaptive adjustment evolutionary parameters proposed by the present invention, but uses traditional static penalty function to deal with constraints), NSGA-II, multi-objective evolutionary algorithm based on decomposition (Multi-ObjectiveEvolutionary Algorithm based on Decomposition, MOEA / D), strength Pareto evolutionary algorithm (Strength Pareto Evolutionary Algorithm 2, SPEA2), multi-objective particle swarm optimization algorithm (Multi-Objective Particle Swarm Optimization, MOPSO) were compared respectively; simulation results like image 3 , Figure 4 , Figure 5 , Figure 6 ,Depend on image 3 , Figure 4 , Figure 5 , Figure 6 It can be s...

Embodiment approach 2

[0140] In this embodiment, the time limit values ​​are respectively set to time limit 1, time limit 2, time limit 3, and time limit 4, and the scientific workflow model Inspiral is selected, and ai-NSGA-II-PE and NSGA-II-PE (using the method proposed by the present invention) The evolutionary parameters are adaptively adjusted, but the traditional static penalty function is used to deal with the constraints), NSGA-II, MOEA / D, SPEA2, and MOPSO evolutionary algorithms are compared respectively; the simulation results are as follows Figure 7 , Figure 8 , Figure 9 , Figure 10 ,Depend on Figure 7 , Figure 8 , Figure 9 , Figure 10 It can be seen that compared with other algorithms, ai-NSGA-II-PE can find a Pareto front with better global detection and local mining effects under strict constraints.

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Abstract

The invention provides a self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling. The overall detection and local mining capability of the multi-object evolution method can be improved. The multi-object evolution method comprises the steps that S1, the evolution states of populations in the evolution process are detected according to the number of Pareto solutions and Pareto entropies, and corresponding individual evaluation strategy processing constraint conditions are self-adaptively utilized to sort individuals in the populations according to the detected evolution states of the populations in the evolution process, wherein a constraint violation processing method is adopted to process the constraint conditions in individual evaluation strategies; S2, according to individual sorting results, individuals are selected from the populations to perform genetic manipulation, and sub-populations are obtained, wherein genetic manipulation parameters are self-adaptively adjusted according to the evolution states of the populations in the evolution process during genetic manipulation. The self-adaptive multi-object evolution method is suitable for solving the multi-object evolution problem having constraints and can be applied to the technical field of workflow scheduling in a cloud computing environment.

Description

technical field [0001] The invention relates to solving the problem of constrained multi-objective optimization, and is applied to the technical field of cloud workflow scheduling, in particular to an adaptive multi-objective evolution method for constrained cloud workflow scheduling. Background technique [0002] Workflow scheduling in cloud environment (abbreviation: cloud workflow scheduling) is to find suitable cloud resources to execute workflow tasks and meet users' service quality requirements. Cloud workflow scheduling problem is a constrained multi-objective optimization problem, and multi-objective evolutionary algorithm can effectively deal with this kind of problem. However, in the existing technologies, most of them simply use static penalty functions to deal with constraints, which can easily lead to premature convergence, and even enter into an infeasible search space, for example: [0003] Existing technology 1, by using Pareto (Pareto) entropy information a...

Claims

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Application Information

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IPC IPC(8): G06N3/12G06Q10/10
CPCG06N3/126G06Q10/103
Inventor 刘丽张淼李慧琦范琦
Owner 北京明易达科技股份有限公司
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