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Social cognitive optimization

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Social Cognitive Optimization (SCO) is a population-based metaheuristic optimization algorithm which was developed in 2002 [1] [2] . This algorithm is based on the social cognitive theory, and the key point of the ergodicity is the process of individual learning of a set of agents with their own memory and their social learning of the knowledge points in the social sharing library. It has been used for solving combinatorial optimization, integer programming, and continuous optimization problems.

References

  1. ^ Xie, Xiao-Feng; Zhang, Wen-Jun; Yang, Zhi-Lian (2002). Social cognitive optimization for nonlinear programming problems. International Conference on Machine Learning and Cybernetics (ICMLC), Beijing, China: 779-783.
  2. ^ Xie, Xiao-Feng; Zhang, Wen-Jun (2004). Solving engineering design problems by social cognitive optimization. Genetic and Evolutionary Computation Conference (GECCO), Seattle, WA, USA: 261-262.
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