Науковий вісник НГУ

Application of self-adaptive dynamic niche genetic algorithm in global multimodal optimization problems

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Information technologies, systems analysis and administration

Last Updated on 02 April 2016

Published on 02 April 2016

Hits: 4735

Authors:

Zhanshen Feng, Xu Chang University, Xuchang, Henan

Yan Yu, Xu Chang University, Xuchang, Henan

Abstract:

Purpose. Genetic algorithm is a kind of random search method evolved from the genetic mechanism, it has strong robustness and optimization ability. However, a large number of researches indicated that the traditional genetic algorithms have many deficiencies and limitations in global multimodal optimization, such as they are prone to premature convergence, high computational cost and weak local search abilities. The purpose is to overcome these disadvantages through the creation of a new algorithm for solving global multimodal optimization problems, which is self-adaptive dynamic niche genetic algorithm (SDNGA).

Methodology. By studying the GA optimization and niche theory, we combine multi-groups and niche method to traditional genetic algorithm, which is used in the solution of global multimodal optimization problems. The proposed algorithm is applied to test functions to demonstrate its effectiveness and applicability.

Findings. We adopted the niche technology to divide each generation of a group into several subgroups. Then, we cho-osed the best individual from each subgroup as the representative of such a subgroup, and then carried out the hybridization and mutation to produce a new generation within the population and between populations, thus enhancing the global optimization ability of the algorithm, and improving the convergence speed.

Originality. We made a study of genetic algorithm and niche theory to apply in the global multimodal optimization problem. We discussed the ideas and the steps of proposed algorithm, made the qualitative analysis on the searching ability and the convergence speed. The research on this aspect has not been found at present.

Practical value. We proposed a self-adaptive dynamic niche genetic algorithm, which can be used in global multimodal optimization problems. The test experimental results have shown that SDNGA has good searching ability, good performance and very strong robustness, which allows for solutions of higher quality.

References:

1. Ras, M.N., Wilke, D.N., Groenwold, A.A. and Kok, S., 2014. On rotationally invariant continuous-parameter genetic algorithms. Advances in Engineering Software, vol.78, no.12, pp. 52−59.

2. 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.

3. Basu, M., 2014. Fuel constrained economic emission dispatch using nondominated sorting genetic algorithm-II. Energy, vol.78, no.15, pp. 649−664.

4. Jafar Ramadhan Mohammed, 2012. A study on the suitability of genetic algorithm for adaptive channel equalization. International Journal of Electrical and Computer Engineering, vol.2, no.3, pp. 285−292.

5. Saeed Soltanali, Rouein Halladj, Shokoufe Tayyebi and Alimorad Rashidi, 2014. Neural network and genetic algorithm for modeling and optimization of effective parameters on synthesized ZSM-5 particle size. Materials Letters, vol.136, no.1, pp. 138−140.

6. Rômulo Alves de Oliveira, Manoel Firmino de Medeiros Júnior and Roberto Felipe Andrade Menezes, 2014. Application of genetic algorithm for optimization on projects of public illumination. Electric Power Systems Research, vol. 117, no.12, pp. 84−93.

7. Pourvaziri, H. and Naderi, B., 2014. A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Applied Soft Computing, vol.24, no.11, pp. 457−469.

8. Y. Volkan Pehlivanoglu, 2013. Direct and indirect design prediction in genetic algorithm for inverse design problems. Applied Soft Computing, vol.24, no.11, pp. 781−793.

9. Jafar Ramadhan Mohammed, 2012. Comparative performance investigations of stochastic and genetic algorithms under fast dynamically changing environment in smart antennas. International Journal of Electrical and Computer Engineering, vol.2, no.1, pp. 98−105.

10. Mostafa Z. Ali and Noor H. Awad, 2014. A novel class of niche hybrid cultural algorithms for continuous engineering optimization, Information Sciences, vol.267, no.20, pp. 158− 190.

2016_01_feng
2016-04-02  642.35 KB  974

• < Prev
• Next >