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Hybrid multi-objective evolution method

A multi-objective evolution, target technology, applied in the field of mixed multi-objective evolution, can solve the problem of poor Pareto solution distribution

Inactive Publication Date: 2017-08-04
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a hybrid multi-objective evolution method to solve the problem of poor distribution of Pareto solutions in the prior art

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Embodiment Construction

[0062] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0063] The invention provides a mixed multi-objective evolution method aiming at the problem of poor distribution of existing Pareto solutions.

[0064] Such as figure 1 As shown, the hybrid multi-objective evolution method provided by the embodiments of the present invention includes:

[0065] S11, at the G-th iteration, adjust the variation factor in the adaptive mutation and the crossover factor in the crossover operation according to the current iteration number, based on the adjusted variation factor and crossover factor, use the adaptive global differential evolution (Differential evolution, DE ) algorithm performs adaptive mutation and crossover operations on all individuals in the G generation population to generate subpopulations;

[0066] S12...

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Abstract

The invention provides a hybrid multi-objective evolution method in order to acquire a solution with good distribution. The hybrid multi-objective evolution method comprises the steps of adjusting a mutation factor in adaptive mutation and a crossover factor in a crossover operation according to the current iteration times at the G times of iteration, performing adaptive mutation and crossover operations on all individuals in the Gth generation of population by using an adaptive global DE algorithm based on the adjusted mutation factor and crossover factor so as to generate a sub-population; combining the Gth generation of population and the sub-population, determining a QoS indicator value of each individual, and calculating the non-domination level and the congestion degree of each individual according to the determined QoS indicator value; selecting N individuals with low non-domination level and high congestion degree to act as a new population according to the calculated non-domination level and congestion degree; and performing local search on non-dominated solution sets in the new population by adopting a local search method, and eliminating individuals with poor distribution degree. The hybrid multi-objective evolution method is applicable to the field of service combination in the Internet cloud computing environment.

Description

technical field [0001] The invention relates to the field of service composition in an interconnected cloud computing environment, in particular to a hybrid multi-objective evolution method. Background technique [0002] The service combination in the Internet cloud environment (abbreviation: Internet cloud service combination) is to find multiple service instances in cloud providers in different geographical locations and combine them together to complete the tasks submitted by users and satisfy users with multiple service qualities. (QoS) metrics are therefore a multi-objective optimization problem. Multi-objective evolutionary algorithm can effectively deal with such problems. However, most of the existing algorithms do not adjust the parameters adaptively and dynamically, and further improve the distribution of the Pareto solution locally, which leads to poor distribution of the Pareto solution, and there are a large number of similar solutions in the Pareto solution. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04
CPCG06Q10/043
Inventor 刘丽刘涛谷淑贤范琦
Owner UNIV OF SCI & TECH BEIJING
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