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A cuckoo search algorithm based on dynamic neighborhood learning

A cuckoo search and neighborhood technology, applied in the field of cuckoo search algorithm, can solve the problems of poor overall quality and loss of useful information, and achieve the effect of improving the accuracy of optimization

Pending Publication Date: 2019-06-04
HONGHE COLLEGE
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Problems solved by technology

In addition, in the greedy selection strategy, even if the fitness value in some dimensions is better, the solutions with poor overall quality will be discarded, resulting in the loss of some useful information; in order to obtain high-quality solutions, make full use of all dimensions It is very necessary to update the information on the

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  • A cuckoo search algorithm based on dynamic neighborhood learning
  • A cuckoo search algorithm based on dynamic neighborhood learning
  • A cuckoo search algorithm based on dynamic neighborhood learning

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Embodiment Construction

[0034] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0035] The present invention discloses a cuckoo search algorithm based on dynamic neighborhood learning to increase population diversity without significantly reducing the convergence rate; first, modify the learning paradigm in the Levy flight strategy to alleviate premature convergence, that is, each individual Individual optimal solution instead of the optimal solution learning in the population; secondly, in order to further enhance the performance of the cuckoo search algorithm when dealing with complex multi-modal problems, each individual can learn from different examples in different dimensions; finally, from the pre-defined Selecting example individuals in the neighborhood of the cuckoo search algorithm to maintain the diversity of the population; the present invention introduces a learning scheme based on dynamic neighborhood in the ...

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Abstract

The invention discloses a cuckoo search algorithm based on dynamic neighborhood learning, and the algorithm comprises the steps: firstly, modifying a Levy flight strategy, and enabling an individual optimal solution instead of an optimal solution obtained in the whole population so far to serve as a model for guiding a search behavior, so as to alleviate the premature convergence; Secondly, updating information of each individual in different dimensions by using different learning examples, and selecting the learning examples of each individual in each dimension from pre-defined neighborhoods;And finally, in order to further realize information exchange, setting a learning probability to control whether the individual learns to own experience or experience of neighbors of the individual.According to the invention, the optimization capability of the cuckoo search algorithm in processing the complex multi-mode problem can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of function optimization, in particular to a cuckoo search algorithm based on dynamic neighborhood learning. Background technique [0002] Currently, researchers have developed many evolutionary algorithms to solve different optimization problems. Among them, the cuckoo search (CS) algorithm is a newly proposed optimization method. In view of its advantages of less control parameters and easy implementation, the CS algorithm has received extensive attention and has been applied in many fields. [0003] The CS algorithm employs Levy flights and preference random walks to generate new candidate solutions, corresponding to exploration and development, respectively. However, in Levy flight, only using the occasional long-jump mechanism cannot guarantee that the algorithm can effectively explore the solution space, so its global search ability is weak; in addition, the search behavior of each individual is deter...

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Application Information

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IPC IPC(8): G06N3/00
Inventor 程加堂熊燕段志梅孙玉龙
Owner HONGHE COLLEGE
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