[1]王立平,肖乐意.基于模拟退火的万有引力算法[J].江西师范大学学报(自然科学版),2014,(05):459-463.
 WANG Li-ping,XIAO Le-yi.The Gravity Algorithm Based Simulated Annealing[J].Journal of Jiangxi Normal University:Natural Science Edition,2014,(05):459-463.
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基于模拟退火的万有引力算法()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2014年05期
页码:
459-463
栏目:
出版日期:
2014-10-31

文章信息/Info

Title:
The Gravity Algorithm Based Simulated Annealing
作者:
王立平;肖乐意
萍乡学院,江西 萍乡,337000;长沙师范学院教务处,湖南 长沙,410100
Author(s):
WANG Li-ping;XIAO Le-yi
关键词:
万有引力算法模拟退火算法函数优化
Keywords:
A Gravitational Search Algorithmsimulated annealingfunction optimization
分类号:
TP301
文献标志码:
A
摘要:
针对标准万有引力算法的个体位置更新策略可能对个体造成破坏且算法局部搜索能力较弱问题提出了一种改进算法。该算法将模拟退火思想引入万有引力算法,采用基于 Metroplis 准则的个体位置更新策略,并在引力操作之后,对每代最优个体进行退火操作。一定程度避免了个体移动的盲目性,提高了算法的局部搜索能力、收敛速度与精度。实验结果表明:算法的改进策略是有效的,且改进后的算法在收敛速度、收敛精度等方面具有明显优势。
Abstract:
In Gravitational Search Algorithm(GSA),individual location update strategy may damage the individual, and the local search ability is weak,an improved algorithm has been proposed. The proposed algorithm integrated simulated annealing mechanism into GSA,used individual location update strategy which based on Metroplis,and did annealing operation for optimal individual of every generation after gravity operation. To some extent,avoided the individual blind Mobile,Improve the local search ability of the algorithm,the velocity and precision of convergence. The experimental results demonstrate that improvement strategy of the algorithm is effective,and the improved algo-rithm has obvious advantages in the velocity of convergence,convergence accuracy,etc.

参考文献/References:

[1] 王晓娟,高亮,陈亚洲.类电磁机制算法及其应用 [J].计算机应用研究,2006,23(6):67-70.
[2] Karakuzu J,Eberhart R C.Particle swarm optimization [J].IEEE International Conference on Neural Networks,1995,4:1942-1948.
[3] Suman B,Kumar P.A survey of simulated annealing as tool for single and muhiobjective optimization [J].Journal of the Operational Research Society,2006,57(10):1143-1160.
[4] Birbil S I,Fang S C.An electromagnetism-like mechanism for global optimization [J].Journal of Global Optimozation,2003,25(3):263-282.
[5] Tang K S,Man K F,Kwong S,et al.Genetic algorithms and their applications [J].IEEE Signal Processing Magazine 1996,13(6):22-37.
[6] Dorigo M,Birattari M,Stutzle T.Ant colony optimization artificial ants as a computational intelligence technique [J].IEEE Computational Intelligence Magazine,2006,1(4):28-39.
[7] Esmat Rashedi,Hossein Nezamabadipour,Saeid Saryazdi.GSA:a gravitational search algorithm [J].Information Sciences,2009,179(13):2232-2248.
[8] 谢丽萍,曾建湖.受拟态物理学启发的全局优化算法 [J].系统工程理论与实践,2010,30(12):2276-2282.
[9] Suman B,Kumar P.A survey of simulated annealing as a tool for single and multiobjective optimization [J].Journal of the Operational Research Society,2006,57(10):1143-1160.
[10] ElBouri A,Azizi N,Zolfaghari S.A comparative study of a new heuristic based on adaptive memory programming and simulated annealing:the case of job shop scheduling [J].European Journal of Operational Research,2007,177:1894-1910.
[11] Tan K C,Chew Y H,Lee L H.A hybrid multi-ob jective evolutionary algorithm for solving truck and trailer vehicle routing problems [J].European Journal of Operational Research,2006,172:855-885.
[12] 杨勇,谭渊,张晓发,等.基于模拟退火算法的阵列模型有源校正方法 [J].国防科技大学学报,2011,33(1):91-94.
[13] 王银年,葛洪伟.求解TSP问题的改进模拟退火遗传算法 [J].计算机工程与应用,2010,46(5):44-85.
[14] 吴涛,金义富.基于云控制的自适应遗传算法 [J].计算机工程,2011,37(8):189-191.
[15] 马海平,李寰,阮谢永.一种群体迁移优化算法及性能分析 [J].控制理论与应用,2010,27(3):329-33.

备注/Memo

备注/Memo:
江西省自然科学基金(20144BAB2020010);江西省科技厅科技支撑项目(2013ZBBF60001);江西省教育厅科技课题(GJJ14789)
更新日期/Last Update: 1900-01-01