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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] . 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 continuous optimization [2], integer programming [3], and combinatorial 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.
  3. ^ Fan, Caixia (2010). Solving Integer Programming Based on Maximum Entropy Social Cognitive Optimization Algorithm. International Conference on Information Technology and Scientific Management (ICITSM), Tianjing, China: 795-798.
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