非线性规划问题的社会认知优化

Proceedings. International Conference on Machine Learning and Cybernetics Pub Date : 2002-11-04 DOI:10.1109/ICMLC.2002.1174487

Xiao-Feng Xie, Wenjun Zhang, Zhilian Yang

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引用次数: 51

摘要

基于社会认知理论，提出了一种基于人类智能的求解非线性规划问题的社会认知优化方法。在一些基准函数上比较SCO和遗传算法的实验表明，即使只有一个学习代理，前者也能有效地产生高质量的解。

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Social cognitive optimization for nonlinear programming problems

Social cognitive optimization (SCO) for solving nonlinear programming problems (NLP) is presented based on human intelligence with the social cognitive theory (SCT). Experiments comparing SCO with genetic algorithms on some benchmark functions show that the former can produce high-quality solutions efficiently, even with only one learning agent.

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Proceedings. International Conference on Machine Learning and Cybernetics

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