An Improved Social Cognitive Optimization Algorithm
An Improved Social Cognitive Optimization Algorithm
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DOI：
10.4028/www.scientific.net/amm.427-429.2580
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Jia Ze Sun;Guohua Geng;Hao Chen;Mingquan Zhou
中科院分区：
文献类型：
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作者：
Jia Ze Sun;Guohua Geng;Hao Chen;Mingquan Zhou
为了提高经典社会认知优化算法的全局收敛速度，提出了一种基于量子行为的混合社会认知优化算法。在所提出的算法中，学习主体在量子多维空间中学习，并建立量子Delta势垒模型。在局部搜索操作中引入量子搜索过程，以提高可行解的邻域搜索效率。对一组基准问题的仿真结果表明，该算法具有较高的优化效率和较好的全局性能。
To improve the global convergence speed of classical social cognitive optimization (SCO) algorithm, a novel hybrid social cognitive optimization algorithm based on quantum behavior (QSCO) is proposed. In the proposed algorithm, learning agents learn in a quantum multi-dimensional space and establish a quantum delta potential well model. A quantum 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 and good global performance.