模糊非基因信息记忆的双克隆选择算法

宋丹; 樊晓平; 文中华; 黄大足; 屈喜龙

doi:10.11999/JEIT160359

模糊非基因信息记忆的双克隆选择算法

doi: 10.11999/JEIT160359

2.
(湖南工程学院计算机与通信学院湘潭 411104) ②(中南大学信息科学与工程学院长沙 410083) ③(湖南财政经济学院信息管理系长沙 410205)

基金项目:

国家自然科学基金(61272295, 61673164, 61402540)，湖南省自然科学基金(2016JJ6031, 2016JJ2040)，湖南省教育厅科学研究项目(16A049, 13A010)

计量
- 文章访问数: 1435
- HTML全文浏览量: 117
- PDF下载量: 559
- 被引次数: 0
出版历程
- 收稿日期: 2016-04-14
- 修回日期: 2016-09-20
- 刊出日期: 2017-02-19

Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory

2.
(College of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China)

Funds:

The National Natural Science Foundation of China (61272295, 61673164, 61402540), The Natural Science Foundation of Hunan Province (2016JJ6031, 2016JJ2040), The Scientific Research Fund of Hunan Provincial Education Department (16A049, 13A010)

摘要

摘要: 该文针对传统智能优化算法中虚拟碰撞而导致的全局搜索效率降低的问题，提出一种模糊非基因信息记忆的双克隆选择算法。该算法设计基于模糊非基因信息的搜索机制与克隆选择原理相结合，对抗体进化中的非基因信息进行采集、模糊化并保存到记忆库，运用这些信息引导该抗体后续的双克隆搜索过程，从而减少非优区域的虚拟碰撞，提高全局搜索效率。通过标准测试函数的仿真试验并与其他算法比较，新算法表现出更快的全局收敛速度和更高的全局收敛精度。
- 克隆选择 /
- 智能记忆 /
- 模糊信息 /
- 数值优化
Abstract: To provide a better solution for search efficiency reduction problem caused by pseudo collision in the traditional intelligent optimization algorithms, this paper proposes a double clonal selection algorithm based on fuzzy non-genetic information memory. By combing with clonal selection theory, the search mechanism based on fuzzy non-genetic information memory is well performed. The non-genetic information in antibody evolution is collected, fuzzified and stored in the memory. Using this information to guide the subsequent double cloning search process, it can reduce the pseudo collision in non-optimal area, thus the global search efficiency is improved greatly. Extensive simulations show that the proposed algorithm has fast global convergence rate and high global convergence accuracy. Comparative results further demonstrate that it performs better than existing algorithms.
- Clonal selection /
- Intelligent memory /
- Fuzzy information /
- Numerical optimization

HTML全文

参考文献(26)

DE CASTRO L N and VON ZUBEN F J. Learning and optimization using the clonal selection principle[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(3): 239-251. doi: 10.1109/TEVC.2002.1011539.

GONG Maoguo, JIAO Licheng, and ZHANG Lining. Baldwinian learning in clonal selection algorithm for optimization[J]. Information Sciences, 2010, 180(8): 1218-1236. doi: 10.1016/j.ins.2009.12.007.

IRINA Ciornei and ELIAS Kyriakides. Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2012, 42(1): 234-245. doi: 10.1109/TSMCB. 2011.2164245.

HO S L, YANG S Y, BAI Y N, et al. A robust metaheuristic combining clonal colony optimization and population-based incremental learning methods[J]. IEEE Transactions on Magnetics, 2014, 50(2): 677-680. doi: 10.1109/TMAG.2013. 2283886.

PENG Y and LU B L. Hybrid learning clonal selection algorithm[J]. Information Sciences, 2015, 296(1): 128-146. doi: 10.1016/j.ins.2014.10.056.

TAYARANI-N M, YAO X, and XU M. Meta-heuristic algorithms in car engine design: A literature survey[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(5): 609-629. doi: 10.1109/tevc.2014.2355174.

CAMPELO F, GUIMARES F G, IGARASHI H, et al. A clonal selection algorithm for optimization in electromagnetics[J]. IEEE Transactions on Magnetics, 2005, 41(5): 1736-1739. doi: 10.1109/tmag.2005.846043.

LIU R C, JAO L C, ZHANG X, et al. Gene transposon based clone selection algorithm for automatic clustering[J]. Information Sciences, 2012, 204(22): 1-22. doi: 10.1016/ j.ins.2012.03.021.

SHANG R H, JIAO L C, XU H, et al. Quantum immune Clone for Solving constrained multi-objective Optimization [C]. 2015 IEEE Congress on Evolutionary Compntation, Sendai, Japan, 2015: 3049-3056. doi: 10.1109/CEC.2015. 7257269.

高维尚, 邵诚, 高琴. 群体智能优化中的虚拟碰撞: 雨林算法[J]. 物理学报, 2013, 62(19): 28-43. doi: 10.7498/aps.62. 190202.

GAO Weishang, SHAO Cheng, and GAO Qin. Pseudo- collision in swarm optimization algorithm and solution: Rain forest algorithm[J]. Acta Physica Sinica, 2013, 62(19): 28-43. doi: 10.7498/aps.62.190202.

MININNO E, NERI F, CUPERTINO F, et al. Compact differential evolution[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 32-54. doi: 10.1109/tevc.2010. 2058120.

SABAR N R, AYOB M, KENDALL G, et al. Grammatical evolution hyper-heuristic for combinatorial optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(6): 840-861. doi: 10.1109/TEVC.2013. 2281527.

BOUAZIZ S, ALIMI A M, and ABRAHAM A. PSO-based update memory for improved harmony search algorithm to the evolution of FBBFNT parameters[C]. 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 2014: 1951-1958. doi: 10.1109/CEC.2014.6900304.

刘若辰, 贾建, 赵梦玲, 等. 一种免疫记忆动态克隆策略算法[J]. 控制理论与应用, 2007, 24(5): 777-784. doi: 10.3969/j. issn.1000-8152.2007.05.016.

LIU Ruochen, JIA Jian, ZHAO Mengling, et al. An immune memory dynamic clonal strategy algorithm[J]. Control Theory Applications, 2007, 24(5): 777-784. doi: 10.3969/ j.issn.1000-8152.2007.05.016.

朱思峰, 刘芳, 柴争义, 等. 简谐振子免疫优化算法求解异构无线网络垂直切换判决问题[J]. 物理学报, 2012, 61(9): 375-384. doi: 10.7498/aps.61.096401.

ZHU Sifeng, LIU Fang, CHAI Zhengyi, et al. Simple harmonic oscillator immune optimization algorithm for solving vertical handoff decision problem in heterogeneous wireless network[J]. Acta Physica Sinica, 2012, 61(9): 375-384. doi: 10.7498/aps.61.096401.

ZITZLER E and THIELE L. Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271. doi: 10.1109/4235.797969.

ZITZLER E, LAUMANNS M, and THIELE L. SPEA2: Improving the strength Pareto evolutionary algorithm[C]. Proceedings of the Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems, Athens, Greece, 2001: 19-26.

CAI Zixing and WANG Yong. A multiobjective optimization based evolutionary algorithm for constrained optimization[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 658-675. doi: 10.1109/TEVC.2006.872344.

邓泽林, 谭冠政, 何锫, 等. 一种基于动态识别邻域的免疫网络分类算法及其性能分析[J]. 电子与信息学报, 2015, 37(5): 1167-1172. doi: 10.11999/JEIT141077.

DENG Zelin, TAN Guanzheng, HE Pei, et al. A dynamic recognition neighborhood based immune network classification algorithm and its performance analysis[J]. Journal of Electronics Information Technology, 2015, 37(5): 1167-1172. doi: 10.11999/JEIT141077.

WANG H, WU Z and RAHANAMAYAN S. Enhancing particle swarm optimization using generalized opposition based learning[J]. Information Sciences, 2011, 181(20): 4699-4714. doi: 10.1016/j.ins.2011.03.016.

喻飞, 李元香, 魏波, 等. 透镜成像反学习策略在粒子群算法中的应用[J]. 电子学报, 2014, 42(2): 230-235. doi: 10.3969/ j.issn.0372-2112.2014.02.004.

YU Fei, LI Yuanxiang, WEI Bo, et al. The application of a novel OBL based on lens imaging principle in PSO[J]. Acta Electronica Sinica, 2014, 42(2): 230-235. doi: 10.3969/j.issn. 0372-2112.2014.02.004.

施引文献

资源附件(0)

访问统计