引入群体发现和加入行为的随机搜索算法

王虹; 卫军胡; 刘昌军; 曹建福

doi:10.7652/xjtuxb201312008

您当前的位置：

首页 >

文章列表页 >

引入群体发现和加入行为的随机搜索算法

更新时间：2025-07-09

- 引入群体发现和加入行为的随机搜索算法
- A Novel Adaptive Stochastic Search Algorithm Based on Group Founding and Joining Behaviors
- 西安交通大学学报 2013年47卷第12期页码：43-49
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201312008
  中图分类号： TP13;TP18
- 网络首发：2013-12-10，
  
  纸质出版：2013
- 稿件说明：
移动端阅览
王虹, 卫军胡, 刘昌军, 等. 引入群体发现和加入行为的随机搜索算法[J]. 西安交通大学学报, 2013,47(12):43-49.

A Novel Adaptive Stochastic Search Algorithm Based on Group Founding and Joining Behaviors[J]. 2013, 47(12): 43-49.
王虹, 卫军胡, 刘昌军, 等. 引入群体发现和加入行为的随机搜索算法[J]. 西安交通大学学报, 2013,47(12):43-49. DOI： 10.7652/xjtuxb201312008.

A Novel Adaptive Stochastic Search Algorithm Based on Group Founding and Joining Behaviors[J]. 2013, 47(12): 43-49. DOI： 10.7652/xjtuxb201312008.

摘要

针对自由搜索算法以及自适应随机搜索(ASS)算法效率低和寻优能力不足的问题

提出了一种基于群体发现和加入行为的随机搜索(HASS)算法。HASS算法采用搜索半径的自适应调整策略提高搜索效率

利用区域混合搜索策略引导群体中的不同个体分别进行全局和局部搜索

通过状态评估方法引入变异策略避免陷入局部最优。HASS算法与ASS算法的主要区别体现在解的选择机制和搜索策略上

HASS算法同时接受较优个体和较差个体

分别为两类个体设计不同的寻优策略来指明搜索方向

增强了算法跳出局部最优的能力和寻优效率。对12个标准测试函数的实验结果表明

该算法的寻优成功率可达100%

较之其他4种算法具有更快的收敛速度和更强的全局搜索能力

特别适于处理复杂的函数优化问题。

Abstract

An adaptive stochastic search algorithm with hybrid strategy(HASS)is presented to improve the low search efficiency and the incompetitive optimization of the free search algorithm and the adaptive stochastic search algorithm(ASS). The algorithm is based on group founding and joining behaviors that exist widely in nature. The strategy to adaptively update the search radius of each individual is used to improve the search efficiency

and a hybrid search strategy is designed to guide different particles to respectively conduct global or local searches. A new mutation operator is introduced to the evolutionary state estimation of the involved particles to improve the population diversity and to avoid premature convergence effectively. The main differences between HASS and ASS are in selection mechanism of solutions and the search strategy. The experimental results on twelve classic benchmark functions show that the HASS algorithm has competitive performance to other four existing algorithms in terms of accuracy

robustness and convergence speed

especially for high-dimensional multimodal problems.

关键词

Keywords

references

KENNEDY J, EBERHART R, SHI Y H. Swarm intelligence [M]. San Mateo, CA, USA: Morgan Kaufmann, 2001.

肖人彬, 陶振武. 群集智能研究进展 [J]. 管理科学学报, 2007, 10(3): 80-96.

XIAO Renbin, TAO Zhenwu. Research progress of swarm intelligence [J]. Journal of Management Sciences in China, 2007, 10(3): 80-96.

DORIGO M, BLUM C. Ant colony optimization theory: a survey [J]. Theoretical Computer Science, 2005, 344(2/3): 243-278.

EBERHART R, KENNEDY J. A new optimizer using particle swarm theory [C]∥Proceedings of the 6th International Symposium on Micro Machine and Human Science. Piscataway, NJ, USA: IEEE, 1995: 39-43.

DORIGO M, BONABEAU E, THERAULAZ G. Ant algorithms and stigmergy [J]. Future Generation Computer Systems, 2000, 16(8): 851-871.

CARLOS A, SEBASTIAN L. A particle swarm optimization algorithm for part-machine grouping [J] Robotics and Computer-Integrated Manufacturing, 2006, 22(5/6): 468-474.

PENEV K, LITTLEFAIR G. Free search: a comparative analysis [J]. Information Sciences, 2005, 172(1/2): 173-193.

周晖, 李丹美, 邵世煌, 等. 一种新的群集智能优化及其改进研究 [J]. 系统工程与电子技术, 2008, 30(2): 337-340.

ZHOU Hui, LI Danmei, SHAO Shihuang, et al. Novel swarm intelligence algorithm and its improvement [J]. Systems Engineering and Electronics, 2008, 30(2): 337-340.

HOLLAND J H. Adaptation in natural and artificial systems [M]. Cambridge, USA: MIT Press, 1992: 6-24.

LI Tuanjie, CAO Yuyan, SUN Guoding. Free search algorithm with the variable neighbourhood and step [J]. Journal of Xidian University, 2010, 37(4): 737-742.

LIU Changjun, WEI Junhu, QIAO Yan, et al. An adaptive stochastic search algorithm [C]∥Proceedings of the 2011 IEEE International Conference on Automation and Logistics. Piscataway, NJ, USA: IEEE, 2011: 154-158.

BAMARD C J, SIBLY R M. Producers and scroungers: a general model and its application to captive flocks of house sparrows [J]. Animal Behaviour, 1981, 29(5): 543-550.

GIRALDEAU L A, BEAUCHAMP G. Food exploitation: searching for the optimal joining policy [J]. Trends in Ecology and Evolution, 1999, 14(3): 102-106.

CLARK C W, MANGEL M. Foraging and flocking strategies: information in an uncertain environment [J]. American Naturalist, 1984, 12(3): 626-641.

LI Xiaodong, YAO Xin. Cooperatively coevolving particle swarms for large scale optimization [J]. IEEE Transactions on Evolutionary Computation, 2012, 16(2): 210-224.

LIANG J J, QIN A K, SUGANTHAN P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions [J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295.

ZHAN Zhihui, ZHANG Jun, LI Yun, et al. Adaptive particle swarm optimization [J]. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, 2009, 39(6): 1362-1381.

HOUCK C R, JOINES J, KAY M. A genetic algorithm for function optimization: a MATLAB implementation, NCSU-IE TR 95-09[R]. Raleigh, North Carolina, USA: North Carolina State University, 1995.

BIRGE B. PSOt: a particle searm optimization toolbox for use with MATLAB [C]∥Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Piscataway, NJ, USA: IEEE, 2003: 182-186.

杨清宇,孙凤伟.一种迭代改进的球向量机故障诊断算法.2013,47(10):1-6.[doi:10.7652/xjtuxb201310001]

仲兆平,严青,邓学群,等.采用非线性粒子群算法的同步糖化发酵参数辨识.2013,47(7):124-128.[doi:10.7652/xjtuxb201307023]

傅文渊,凌朝东.自适应折叠混沌优化方法.2013,47(2):33-38.[doi:10.7652/xjtuxb201302006]

莫同,褚伟杰,李伟平,等.采用超图的微博群落感知方法.2012,46(11):120-126.[doi:10.7652/xjtuxb201211022]

徐健,景明利,齐春,等.采用遗传算法的分层贪婪字典训练算法.2012,46(4):18-23.[doi:10.7652/xjtuxb201204004]

薛咏,冯博琴,等.扩展主题图本体融合策略与算法.2011,45(10):13-18.[doi:10.7652/xjtuxb201110003]

彭柳青,张军英.一种鲁棒的子空间聚类算法.2011,45(6):13-19.[doi:10.7652/xjtuxb201106003]

豆增发,高琳.应用粒子群优化-条件随机域的文本生物实体识别.2010,44(12):38-42.[doi:10.7652/xjtuxb201012008]

郑巍,刘三阳,寇晓丽.一种面向传感器网络的蚁群优化路径恢复算法.2010,44(1):83-86.[doi:10.7652/xjtuxb2010010 18]

魏平,徐成贤,段成德.全局智能优化集成算法研究.2009,43(12):60-64.[doi:10.7652/xjtuxb200912013]

王峰,邢科义,徐小平.系统辨识的粒子群优化方法.2009,43(2):116-120.[doi:10.7652/xjtuxb200902025]

张育林,庄健,李小虎,等.小世界邻域优化的局部线性嵌入算法.2008,42(12):1486-1489.[doi:10.7652/xjtuxb2008120 11]

吴慧卓,张可村.基于拉格朗日对偶的一类全局优化算法.2008,42(8):1031-1034.[doi:10.7652/xjtuxb200808022]

夏克文,苏昶,沈钧毅,等.一种改进的Grover量子搜索算法.2007,41(10):1127-1131.[doi:10.7652/xjtuxb2007100 01]

李清华,邵志标,耿莉.一种多层平面电感设计的全局优化方法.2007,41(6):665-668.[doi:10.7652/xjtuxb200706008]

贺群武,王秋旺,索晓娜,等.直接模拟蒙特卡罗方法下的逆温度抽样算法.2006,40(1):22-25.[doi:10.7652/xjtuxb 2006 01006]

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

不同板叠材料热声热机能量转换过程数值模拟研究

语义区分和子图整合驱动的知识图谱表示学习方法

超临界二氧化碳向心透平轮背间隙内风阻损失及流动特性研究

冷库水平空气幕热湿阻隔性能数值模拟研究

水蒸气掺混对氨裂解气旋流燃烧与排放特性的影响研究