Genetic-bee colony dual-population self-adaptive hybrid algorithm based on information entropy

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Category: IT technologies

Last Updated on Saturday, 02 April 2016 21:58

Published on Saturday, 02 April 2016 21:58

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Authors:

Xiaomeng Pan, Henan Vocational and Technical Institute, Zhengzhou, Henan, China

Abstract:

Purpose. Swarm intelligence is the intelligent behaviour represented by a kind of individuals with no or simple intelligen-ce through any form of cluster and collaboration. The research conserns the dual-population self-adaptive hybrid algorithm based on genetic algorithm (GA) and artificial bee colony (ABC). We have obtained some important performance measures, which are helpful for swarm intelligence algorithms.

Methodology. We proposed a genetic-bee colony dual-population self-adaptive hybrid algorithm based on information entropy, which uses dual-population structure and independent evolution and which conducts information exchange through information entropy to maintain population diversity and accelerate the evolution process between the two populations when appropriate.

Findings. We first analysed the basic structure and characteristics of GA and ABC, and then the dual-population based on GA and ABC, which joined the information entropy, was presented, in the parallel operation of two relatively independent populations to accelerate the emergence of a new individual by competition between the populations; it has better effects in complex function optimization problems.

Originality. We made a combinational study of GA and ABC. Although the current biological intelligent evolutionary algorithm has greatly improved its convergence speed, it is not ideal when optimizing complicated functions. This aspect of re-search is still relatively few at present.

Practical value. We researched the optimization algorithm, which is applied to various research fields. Nowadays, it is a development trend to improve the original algorithm by integrating the intelligent algorithm. Dual-population algorithm can overcome the shortage of separate algorithm, and become more suitable for complex optimization problems. We provided the foundation to search for complex distributed problems without centralized control or global model.   

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