[1]汤可宗,肖 绚,贾建华,等.基于离散式多样性评价策略的 自适应粒子群优化算法[J].南京理工大学学报(自然科学版),2013,37(03):12.
 Tang Kezong,Xiao Xuan,Jia Jianhua,et al.Adaptive particle swarm optimization algorithm based on discrete estimate strategy of diversity[J].Journal of Nanjing University of Science and Technology,2013,37(03):12.
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基于离散式多样性评价策略的 自适应粒子群优化算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
37卷
期数:
2013年03期
页码:
12
栏目:
出版日期:
2013-06-30

文章信息/Info

Title:
Adaptive particle swarm optimization algorithm based on discrete estimate strategy of diversity
作者:
汤可宗12肖 绚1贾建华1徐 星1
1.景德镇陶瓷学院 信息工程学院,江西 景德镇 333000; 2.南昌工程学院 信息工程学院,江西 南昌 330029
Author(s):
Tang Kezong12Xiao Xuan1Jia Jianhua1Xu Xing1
1.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333000,China; 2.School of Information Engineering,Nanchang Institute of Technology,Nanchang 330029,China
关键词:
离散式多样性评价策略 粒子群优化 变异策略
Keywords:
discrete estimate strategy of diversity particle swarm optimization entropy mutation strategy
分类号:
TP301.6
摘要:
为了通过增强种群多样性提高对粒子全局寻优能力与寻优速度的平衡能力,该文提出一种自适应粒子群优化(APSO)算法。基于种群熵对标准粒子群优化(SPSO)算法的多样性进行了研究,给出一种离散式多样性评价策略。为了均衡SPSO算法的勘探和开发能力,该文分析了SPSO算法的惯性权值随多样性评价值变化而变化的动态函数关系,并将该函数关系融入APSO算法。为防止算法搜索后期过早陷入局部最优点,采用一种变异策略增强种群的多样性。仿真结果证明:APSO算法相比耗散粒子群优化(DPSO)算法,增加了对未探测空间的搜索能力,加速了粒子在整个解空间的寻优过程。在开发阶段,惯性权值随多样性的减少而递减,在勘探阶段,惯性权值随多样性的增加而增加。APSO算法较好地平衡了算法的全局搜索和局部细致搜索能力,可使粒子在较大范围空间内快速寻找到最优解所在的区域,并展开细致搜索。
Abstract:
In order to balance the ability between the global searching ability of particles and the optimization speed by enhancing population diversity,an adaptive particle swarm optimization(APSO)algorithm is proposed here.The diversity of standard particle swarm optimization(SPSO)algorithm is analyzed based on population entropy and a discrete estimate strategy of diversity is given.The dynamic function relationship between inertia weight and diversity of the SPSO algorithm is analyzed to balance the trade-off between exploration and exploitation and incorporated into the APSO algorithm.To avoid obtaining partial optimal solutions in the searching later stage,the APSO algorithm introduces a mutation strategy to enhance the diversity of the population.The simulation results show that compared with the dissipative particle swarm optimization(DPSO)algorithm,the APSO algorithm enhances the searching ability of the unexplored space and accelerates the searching process of the particles in the whole solution space.In the development phase,the inertia weight decreases with the decrease of the diversity; in the prospection phase,the inertia weight increases with the increase of the diversity.The APSO algorithm balances the global searching ability and the local careful searching ability,and the particles can search out the area of the optimum solution in a wider space,and develop careful searching.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2012-08-02 修回日期:2013-04-11
基金项目:国家自然科学基金(61202313; 61261027; 31260273); 国家科技支撑计划(2012BAH25F02); 江西省自然科学基金(20122BAB201044; 20132BAB211020); 江西省教育厅科技项目(GJJ12642; GJJ12514; GJJ13637)
作者简介:汤可宗(1978-),男,博士,讲师,主要研究方向:智能信息处理,E-mail:tangkezong@126.com。
更新日期/Last Update: 2013-03-25