Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory
SONG Dan①② FAN Xiaoping②③ WEN Zhonghua① HUANG Dazu②③ QU Xilong①
①(College of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China) ②(School of Information Science and Engineering, Central South University, Changsha 410083, China) ③(Department of Information Management, Hunan University of Finance and Economics, Changsha 410205, China)
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.
宋丹, 樊晓平,文中华,黄大足,屈喜龙. 模糊非基因信息记忆的双克隆选择算法[J]. 电子与信息学报, 2017, 39(2): 255-262.
SONG Dan, FAN Xiaoping, WEN Zhonghua, HUANG Dazu,QU Xilong. Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory. JEIT, 2017, 39(2): 255-262.
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.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
CAMPELO F, GUIMARÃES 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.
[8]
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.
[9]
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.
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.
[11]
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.
[12]
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.
[13]
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.
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.
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.
[16]
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.
[17]
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.
[18]
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.
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.
[20]
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.
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.