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Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization.

Wang JS, Li SX, Song JD - Comput Intell Neurosci (2015)

Bottom Line: A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location.In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made.The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China ; National Financial Security and System Equipment Engineering Research Center, University of Science and Technology Liaoning, Anshan 114044, China.

ABSTRACT
In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

No MeSH data available.


Convergence curves of Function f1.
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Related In: Results  -  Collection


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fig8: Convergence curves of Function f1.

Mentions: Evaluate the performances of algorithms through statistics of the best value, average value and worst value among 30 times running, and the convergence curves of each function. The convergence curves of six functions f1–f6 are shown in Figures 8–13. The numerical test results of each algorithm are shown in Table 2.


Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization.

Wang JS, Li SX, Song JD - Comput Intell Neurosci (2015)

Convergence curves of Function f1.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4538968&req=5

fig8: Convergence curves of Function f1.
Mentions: Evaluate the performances of algorithms through statistics of the best value, average value and worst value among 30 times running, and the convergence curves of each function. The convergence curves of six functions f1–f6 are shown in Figures 8–13. The numerical test results of each algorithm are shown in Table 2.

Bottom Line: A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location.In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made.The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China ; National Financial Security and System Equipment Engineering Research Center, University of Science and Technology Liaoning, Anshan 114044, China.

ABSTRACT
In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

No MeSH data available.