基于感知范围的鱼群优化算法

doi:10.7523/j.issn.2095-6134.2010.1.012

中国科学院大学学报 ›› 2010, Vol. 27 ›› Issue (1): 83-89.DOI: 10.7523/j.issn.2095-6134.2010.1.012

基于感知范围的鱼群优化算法

冯春时, 丛爽

中国科学技术大学自动化系,合肥 230027

收稿日期:2009-01-05 修回日期:2009-09-03 发布日期:2010-01-15
通讯作者: 丛爽
基金资助:
国家自然科学基金项目(60774098)资助 

A novel fish shoal algorithm based on sensing zones

FENG Chun-Shi, CONG Shuang

Department of Automation, University of Science and Technology of China, Hefei 230027, China

Received:2009-01-05 Revised:2009-09-03 Published:2010-01-15

摘要/Abstract

摘要：

模拟鱼群在空间的游动行为. 以个体鱼之间的实空间欧式距离为量度,将个体鱼感知范围内的邻域空间分为吸引、排斥和中性区域,同时考虑所有个体鱼都有向食物源运动的趋势. 利用参数选取实验来确定感知范围参数;通过标准测试函数实验对所提出的新鱼群算法和人工鱼群算法进行了对比分析. 在此基础上,对两种算法的搜索步长进行了实验研究. 最后,在基本算法的基础上提出了线性变化权重因子策略,13个测试函数的实验证实此策略可以进一步提升算法性能.

关键词: 新鱼群算法, 空间感知范围, 群智能优化

Abstract:

The algorithm simulates the movement of fish shoal in the space. According to the Euclidean distance, the neighborhood of the fish individual is divided into attraction zone, repulsion zone, and neutral zone. Furthermore, the movement trend to the food source is considered. The neighborhood factors were determined by means of the experiments. The experiments were carried out on the benchmark functions for comparison of the Novel Fish-shoal Algorithm with the Artificial Fish-swarm Algorithm. The search step size was studied experimentally. Finally a linear change weight factor strategy was proposed and the experimental results with 13 test functions verified that the proposed strategy can further improve the performance of the algorithm.

Key words: novel fish shoal algorithm, space sensing zone, optimization of swarm intelligence

中图分类号:

TP13

冯春时, 丛爽. 基于感知范围的鱼群优化算法[J]. 中国科学院大学学报, 2010, 27(1): 83-89.

FENG Chun-Shi, CONG Shuang. A novel fish shoal algorithm based on sensing zones[J]. , 2010, 27(1): 83-89.

参考文献

[1] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies //Proceedings of the 1st European Conference on Artificial Life.1991: 134-142.

[2] Kennedy J, Eberhart R. Particle swarm optimization //Proceedings of IEEE International Conference of Neural Networks. 1995: 1942-1948.

[3] Li X L, Shao Z J, Qian J X. An optimizing method based on autonomous animats: Fish-swarm algorithm
[J]. Systems Engineering-theory & Practice, 2002, 22(11):32-38(in Chinese). 李晓磊, 邵之江, 钱积新. 一种基于动物自治体的寻优模式:鱼群算法
[J]. 系统工程理论与实践, 2002, 22(11): 32-38.

[4] Cao C Z, Zhang K, Zheng H Y, et al. BP neural network speed identifier based on artificial fish algorithm
[J]. Journal of System Simulation, 2009, 21(4): 1047-1050(in Chinese). 曹承志,张\ 坤,郑海英,等. 基于人工鱼群算法的BP神经网络速度辨识器
[J]. 系统仿真学报, 2009, 21(4): 1047-1050.

[5] Wang F, Xu X H, Zhang J. Single airport ground-holding problem optimizing strategy based on artificial fish school algorithm
[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2009, 41(1): 116-120(in Chinese). 王\ 飞,徐肖豪,张\ 静. 基于人工鱼群算法的单机场地面等待优化策略
[J]. 南京航空航天大学学报, 2009, 41(1): 116-120.

[6] Zhang M F, Shao C. Niche artificial fish swarm algorithm for multimodai function optimization
[J]. Control Theory & Applications, 2008, 25(4):773-776(in Chinese). 张梅凤,邵\ 诚. 多峰函数优化的生境人工鱼群算法
[J]. 控制理论与应用,2008, 25(4):773-776.

[7] Couzin L, Krause J, Franks N, et al. Effective leadership and decision-making in animal groups on the move
[J]. Nature, 2005, 433(2): 513-516.

[8] Huth A, Wissel C. The simulation of movement of fish schools . Journal of Theoretical Biology, 1992, 156: 365-385.

[9] Couzin I, Krause J, James R, et al. Collective memory and spatial sorting in animal groups
[J]. Journal of Theoretical Biology , 2002, 218: 1-11.

[10] Oboshi T, Kato S, Mutoh A, et al. Collective or scattering: evolving schooling behaviors to escape from predator //Artificial Life VIII. MIT Press, 2002: 386-389.

[11] Tien J, Levin S, Rubenstein D. Dynamics of fish shoals: identifying key decision rules
[J]. Evolutionary Ecology Research, 2004, 16: 555- 565.

[12] Yao X, Liu Y, Lin G. Evolutionary programming made faster
[J]. IEEE Transactions on Evolutionary Computation , 1999, 3(2): 82-102.

[13] Lin D, Li M Q, Kou J S. Two methods to prevent and overcome premature convergence in evolutionary programming
[J]. Journal of Systems Engineering, 2001, 16(3): 211- 216(in Chinese). 林\ 丹,李敏强,寇纪松. 进化规划中防止早熟收敛的方法
[J]. 系统工程学报,2001,16(3): 211- 216.

基于感知范围的鱼群优化算法

A novel fish shoal algorithm based on sensing zones

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价

访问统计

联系我们