| 基于模拟退火的差分变异群搜索优化算法 |
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| 引用本文： | 郑慧杰,刘弘,郑向伟,孙玉灵. 基于模拟退火的差分变异群搜索优化算法[J]. 计算机工程, 2012, 38(17): 178-181 |
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| 作者姓名： | 郑慧杰 刘弘 郑向伟 孙玉灵 |
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| 作者单位： | 山东师范大学信息科学与工程学院;山东省分布式计算机软件新技术重点实验室 |
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| 基金项目： | 国家自然科学基金资助项目(6097004);教育部博士点基金资助项目(20093704110002);山东省自然科学基金资助项目(ZR2010QL01) |
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| 摘 要： | 标准群搜索优化算法易陷入局部最优。为此，引入模拟退火策略和差分进化算子，使算法跳出局部极值点，变异和迭代同时进 行，并保持前期搜索速度快的特性。测试结果证明，改进算法的全局收敛能力明显提高，个体具有良好的人工智能性，能够真实模拟群体行为。
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| 关 键 词： | 群搜索优化算法 群体动画 差分进化 局部最优 |
| 收稿时间： | 2011-09-27 |
| 修稿时间： | 2011-12-19 |
Group Search Optimization Algorithm of Differential Variation Based on Simulated Annealing |
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| ZHENG Hui-jie,LIU Hong,ZHENG Xiang-wei,SUN Yu-ling. Group Search Optimization Algorithm of Differential Variation Based on Simulated Annealing[J]. Computer Engineering, 2012, 38(17): 178-181 |
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| Authors: | ZHENG Hui-jie LIU Hong ZHENG Xiang-wei SUN Yu-ling |
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| Affiliation: | 1,2(1.School of Information Science and Engineering,Shandong Normal University,Jinan 250014,China;2.Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology,Jinan 250014,China) |
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| Abstract: | To reduce the possibility of falling into local optimum,metroplis rule and differential evolution operator is introdued in Group Search Optimization algorithm,which makes the variation of the algorithm get rid of the shackles of the local extreme advantage,maintain the pre-fast search feature,and improve global search capabilities.During varation and iterative,merit-based evolution improves optimization performance.Test results point that the ability to improve the global convergence of the algorithm is significantly improved,meanwhile,simulation results show that the individual has a good artificial intelligence,which can simulate group behavior toralistically. |
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| Keywords: | Group Search Optimization(GSO) algorithm group animation Differential Evolution(DE) local optimum |
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