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Neural Computing and Applications

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30-06-2017 | Original Article

Applying modified cuckoo search algorithm for solving systems of nonlinear equations

Authors: Xinming Zhang, Qian Wan, Youhua Fan

Published in: Neural Computing and Applications | Issue 2/2019

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Abstract

In the present paper, a modified cuckoo search algorithm is proposed for solving nonlinear equations, that is, the niche cuckoo search algorithm (NCSA) based on fitness-sharing principle. Niche strategy is introduced to enhance the ability of the cuckoo search algorithm to solve nonlinear equations. So as to evaluate the efficiency of NCSA, NCSA has been first benchmarked by twenty standard test functions by comparing with standard genetic algorithm, chaos gray-coded genetic algorithm and standard cuckoo search algorithm. Then, solutions for several examples of nonlinear systems are presented and compared with results obtained by other approaches. Moreover, the sensitivity analysis of the method to initial interval, nests number, probability and niche number has been studied. Comparison results reveal that the proposed algorithm can cope with the highly nonlinear problems and outperforms many algorithms which exist in the literature.

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Title: Applying modified cuckoo search algorithm for solving systems of nonlinear equations
Authors: Xinming Zhang
Qian Wan
Youhua Fan
Publication date: 30-06-2017
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3088-3

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