Hybrid social cognitive optimization algorithm for constrained nonlinear programming

doi:10.1016/S1005-8885(11)60270-7

中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (3): 91-99.doi: 10.1016/S1005-8885(11)60270-7

Hybrid social cognitive optimization algorithm for constrained nonlinear programming

孙家泽,耿国华,王曙燕,周明全

School of Computer Science and Technology, Xi ’an University of Posts and Telecommunications

收稿日期:2011-11-10 修回日期:2012-03-20 出版日期:2012-06-30 发布日期:2012-06-08
通讯作者: 孙家泽 E-mail:sunjiaze@xupt.edu.cn
基金资助:
社会协商机制驱动的多智能体进化计算模型及算法研究;基于群体智能的组合测试关键问题优化研究;基于多智能体协同与流形学习的图像语义挖掘

Hybrid social cognitive optimization algorithm for constrained nonlinear programming

School of Computer Science and Technology, Xi ’an University of Posts and Telecommunications

Received:2011-11-10 Revised:2012-03-20 Online:2012-06-30 Published:2012-06-08
Contact: Jia-Ze SUN E-mail:sunjiaze@xupt.edu.cn

摘要/Abstract

摘要：

To improve the global convergence speed of social cognitive optimization (SCO) algorithm, a hybrid social cognitive optimization (HSCO) algorithm based on elitist strategy and chaotic optimization is proposed to solve constrained nonlinear programming problems (NLPs). The proposed algorithm partitions learning agents into three groups in proportion: elite learning agents, chaotic learning agents and common learning agents. The common learning agents work in the search way of traditional SCO, chaotic learning agents search via chaotic search (CS) algorithm based on Tent Map which helps to avoid the premature convergence, elite learning agents search via elitist selection which helps to improve the global searching performance. Additionally, a chaotic search process is incorporated into local searching operation so as to enhance the local searching efficiency in the neighboring areas of the feasible solutions. Simulation results on a set of benchmark problems show that the proposed algorithm has high optimization efficiency, good global performance, and stable optimization outcomes for constrained NLPs

关键词:

SCO, elitist strategy, CS, tent map, NLPs

Abstract:

Key words:

SCO, elitist strategy, CS, tent map, NLPs

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Hybrid social cognitive optimization algorithm for constrained nonlinear programming

Hybrid social cognitive optimization algorithm for constrained nonlinear programming

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