From the social learning theory to a social learning algorithm for global optimization

Gong, Y.-J., Zhang, J. and Li, Y. (2014) From the social learning theory to a social learning algorithm for global optimization. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, USA, 5-8 Oct 2014, pp. 222-227. (doi: 10.1109/SMC.2014.6973911)

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Publisher's URL: http://dx.doi.org/10.1109/SMC.2014.6973911

Abstract

Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization approach that mimics the social learning process of humans. Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Experimental studies in this paper showed the appealing search behavior of this human intelligence-inspired approach, which can reach the global optimum even in ill conditions. The effectiveness and high efficiency of the proposed algorithm has further been verified by comparing to some representative EC algorithms and variants on a set of benchmarks.

Item Type:Conference Proceedings
Additional Information:© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Professor Yun
Authors: Gong, Y.-J., Zhang, J., and Li, Y.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright © 2014 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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