An Improved Social Cognitive Optimization Algorithm

Jia Ze Sun; Guo Hua Geng; Hao Chen; Ming Quan Zhou

doi:10.4028/www.scientific.net/AMM.427-429.2580

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429An Improved Social Cognitive Optimization...

An Improved Social Cognitive Optimization Algorithm

Abstract:

To improve the global convergence speed of classical social cognitive optimization (SCO) algorithm, a novel hybrid social cognitive optimization algorithm based on quantum behavior (QSCO) is proposed. In the proposed algorithm, learning agents learn in a quantum multi-dimensional space and establish a quantum delta potential well model. A quantum search process is incorporated into local searching operation so as to enhance the local searching efficiency in the neighboring areas of the feasible solutions. Simulation results on a set of benchmark problems show that the proposed algorithm has high optimization efficiency and good global performance.

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Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

2580-2583

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.2580

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Online since:

September 2013

Authors:

Jia Ze Sun*, Guo Hua Geng, Hao Chen, Ming Quan Zhou

Keywords:

Learning Agents, Quantum Social Cognitive Optimization (QSCO), Social Cognitive Optimization (SCO)

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References

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