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

Yue Jiao Gong, Jun Zhang, Yun Li

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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
Publication statusPublished - 4 Dec 2014


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


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