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

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Category: Information technologies, systems analysis and administration

Last Updated on 02 April 2016

Published on 02 April 2016

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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.   

References:

1. Rahmani, A. and MirHassani, S.A., 2014. A hybrid firefly-genetic algorithm for the capacitated facility location problem. Information Sciences, vol. 283, no. 1, pp. 70−78.

2. Saeed Soltanali, Rouein Halladj, Shokoufe Tayyebi and Alimorad Rashidi, 2014. Neural Network and Genetic Algo-rithm for Modeling and Optimization of Effective Parame-ters on Synthesized ZSM-5 Particle Size. Materials Letters, vol. 136, no. 1, pp. 138−140.

3. Ergun Uzlu, Murat İhsan Kömürcü, Murat Kankal, Tay-fun Dede and Hasan Tahsin Öztürk, 2014. Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Applied Ocean Research, vol. 48, no. 10, pp. 103−113.

4. Y. Volkan Pehlivanoglu, 2014. Direct and indirect design prediction in genetic algorithm for inverse design problems. Applied Soft Computing, vol. 24, no. 11, pp. 781− 793.

5. Muhammet Unal, Mustafa Onat, Mustafa Demetgul and Haluk Kucuk, 2014. Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, vol. 58, no. 12, pp. 187−196.

6. Nafiseh Imanian, Mohammad Ebrahim Shiri, Parham Moradi, 2014. Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems. Engineering Applications of Artificial Intelligence, vol. 36, no.11, pp. 148−163.

7. Shuzhu Zhang, C.K.M. Lee, K.L. Choy, William Ho and W.H. Ip. 2014. Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem. Transportation Research Part D: Transport and Environment, vol. 31, no. 8, pp. 85−99.

8. A.J. Umbarkar, M.S. Joshi, and Wei-Chiang Hong, 2014. Multithreaded parallel dual population genetic algorithm (MPDPGA) for unconstrained function optimizations on multi-core system. Applied Mathematics and Computation, vol. 243, no. 15, pp. 936−949.

9. Dervis Karaboga and Beyza Gorkemli, 2014. A quick artificial bee colony algorithm and its performance on optimization problems. Applied Soft Computing, vol. 23, no. 10, pp. 227−238.

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