Configurable intelligent optimization method based on double-layer automatic generation strategy

An automatic generation and intelligent optimization technology, applied in artificial life, biological models, instruments, etc., can solve problems such as difficult to solve efficiently, mixed design of intelligent optimization methods is not flexible enough to solve, etc., to enhance dynamic and robustness, Ensure global optimization efficiency and efficient solution effect

Pending Publication Date: 2021-08-10
BEIHANG UNIV +1
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Problems solved by technology

[0003] The technical problem solved by the present invention is: Aiming at the current existing technology, a configurable intelligent optimization method based on a two-layer automatic generation strategy is proposed, which solves the problem of multi-field complex optimization based on a large population that is difficult to efficiently solve, intelligent optimization method Mixed Designs for Solving Inflexible Problems

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  • Configurable intelligent optimization method based on double-layer automatic generation strategy
  • Configurable intelligent optimization method based on double-layer automatic generation strategy
  • Configurable intelligent optimization method based on double-layer automatic generation strategy

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Embodiment Construction

[0033] A configurable intelligent optimization method based on a two-layer automatic generation strategy, which combines the operator configuration method in the serial intelligent optimization method and the topology configuration mode in the parallel intelligent optimization method, through the two-layer automatic generation strategy in the evolution iteration process Divide sub-populations, automatically configure operators and realize self-adaptive communication topology selection in inter-group interaction, form a synchronous search of multiple processes or threads, and realize the diffusion of the overall information of the population in the way of periodic process interaction And application, and then in the case of sub-population scattered independent solution to ensure the global optimization efficiency, enhance the dynamics and robustness of the intelligent optimization method in large-scale solution space search, and make complex problem decomposition optimization mor...

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Abstract

A configurable intelligent optimization method based on a double-layer automatic generation strategy combines an operator configuration method in a serial intelligent optimization method and a topological structure configuration mode in a parallel intelligent optimization method, and divides sub-populations in an evolutionary iteration process through the double-layer automatic generation strategy. Self-adaptive communication topology selection is realized through automatic operator configuration and inter-group interaction, synchronous search of a plurality of processes or threads is formed, and diffusion and application of population overall information are realized in a periodic process interaction mode, so that the global optimization efficiency is ensured under the condition of sub-population dispersion independent solution, the dynamic nature and robustness of the intelligent optimization method in large-scale solution space search are enhanced, complex problem decomposition and optimization are more flexible, and more space is provided for hybrid design and solution of the intelligent optimization method.

Description

technical field [0001] The invention relates to a configurable intelligent optimization method based on a double-layer automatic generation strategy, which belongs to the field of parallel computing of complex optimization problems. Background technique [0002] Since the early 1990s, in order to speed up the iterative process of intelligent optimization methods, researchers have designed a population-based parallel framework according to its characteristics of population-based iteration. Under this framework, the population of methods is divided into multiple sub-populations, and each sub-population is carried by different computing cores, and is optimized in parallel with the same evolutionary mechanism in the solution space. The intelligent optimization mode of the population can be divided into master-slave parallel mode, coarse-grained parallel mode and fine-grained parallel mode. In the fine-grained mode, the population is divided into several very small subgroups, wh...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/00
CPCG06N3/006G06Q10/04
Inventor 赖李媛君叶飞张霖张连怡
Owner BEIHANG UNIV
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