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

Yue Jiao Gong, Jun Zhang, Yun Li

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish
Article number6973911
Pages (from-to)222-227
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume1 (Jan 2015)
Issue number1
DOIs
Publication statusPublished - 4 Dec 2014

Fingerprint

Global optimization
Learning algorithms
Evolutionary algorithms
Reinforcement
Animals

Keywords

  • evolutionary computation
  • global optimization
  • observational learning
  • social learning theory
  • swarm intelligence

Cite this

Gong, Y. J., Zhang, J., & Li, Y. (2014). From the social learning theory to a social learning algorithm for global optimization. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 1 (Jan 2015)(1), 222-227. [6973911]. https://doi.org/10.1109/SMC.2014.6973911
Gong, Yue Jiao ; Zhang, Jun ; Li, Yun. / From the social learning theory to a social learning algorithm for global optimization. In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2014 ; Vol. 1 (Jan 2015), No. 1. pp. 222-227.
@article{deadffe10a8e46e5a55132ad9bbd73fc,
title = "From the social learning theory to a social learning algorithm for global optimization",
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.",
keywords = "evolutionary computation, global optimization, observational learning, social learning theory, swarm intelligence",
author = "Gong, {Yue Jiao} and Jun Zhang and Yun Li",
year = "2014",
month = "12",
day = "4",
doi = "10.1109/SMC.2014.6973911",
language = "English",
volume = "1 (Jan 2015)",
pages = "222--227",
number = "1",

}

Gong, YJ, Zhang, J & Li, Y 2014, 'From the social learning theory to a social learning algorithm for global optimization', Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 1 (Jan 2015), no. 1, 6973911, pp. 222-227. https://doi.org/10.1109/SMC.2014.6973911

From the social learning theory to a social learning algorithm for global optimization. / Gong, Yue Jiao; Zhang, Jun; Li, Yun.

In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Vol. 1 (Jan 2015), No. 1, 6973911, 04.12.2014, p. 222-227.

Research output: Contribution to journalArticle

TY - JOUR

T1 - From the social learning theory to a social learning algorithm for global optimization

AU - Gong, Yue Jiao

AU - Zhang, Jun

AU - Li, Yun

PY - 2014/12/4

Y1 - 2014/12/4

N2 - 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.

AB - 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.

KW - evolutionary computation

KW - global optimization

KW - observational learning

KW - social learning theory

KW - swarm intelligence

UR - http://www.scopus.com/inward/record.url?scp=84930961962&partnerID=8YFLogxK

U2 - 10.1109/SMC.2014.6973911

DO - 10.1109/SMC.2014.6973911

M3 - Article

VL - 1 (Jan 2015)

SP - 222

EP - 227

IS - 1

M1 - 6973911

ER -

Gong YJ, Zhang J, Li Y. From the social learning theory to a social learning algorithm for global optimization. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2014 Dec 4;1 (Jan 2015)(1):222-227. 6973911. https://doi.org/10.1109/SMC.2014.6973911