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In order to improve the ability to optimize artificial immune algorithm, the memory mechanism of non-genetic information is introduced into optimization algorithm. An immune memory optimization algorithm based on the non-genetic information is proposed. Emulating human society education and experiential inheritance mechanism, the algorithm takes, stores and uses non genetic information in the evolutionary process of the population. By setting up a separate memory base, the algorithm stores non genetic information, and guides the subsequent search process. The algorithm uses the short-term memory of the prior knowledge and guides the subsequent evolution, which can increase the intelligence of search and reduce the blind search and repeat the search. The immune memory optimization algorithm based on the non-genetic information includes key operators: mutation operator, crossover operator and complement operator. The mutation operator is able to efficiently use non genetic information of grandparents to search, which can speed up the local search efficiency. In addition, the threshold to control the search depth of single dimension can avoid falling into local optimal solution making the evolutionary standstill. Through calculating comprehensive information about contemporary populations of all antibodies, complementary operator produces new antibodies containing excellent gene fragment in the global solution space. With small probability rules, crossover operator happens in an interval of multi generation, choosing the optimal antibody and a random antibody to exchange information about a single dimension. Crossover operator and complement operator can both be conducive to jumping out of optimal location. In simulation experiment, the immune memory optimization algorithm based on the non-genetic information uses four standard test functions: Ackley function, Griewank function, Rastrigin function, and transformed Rastrigin function. In order to better compare with contrast algorithm, in the case of high dimension the values of dimension are 20 and 30, and the experiment tests the four functions to make the statistical analysis of the results. On the other hand, to further test optimal performance of the algorithm in a more global massive space, multiple random experiment is carried out in the case of dimension 100. Compared with other intelligent algorithm, the simulation experiment with standard test functions of high dimension indicates that the new algorithms are superior in convergence speed, convergence precision and robustness comparison algorithm. In addition, the simulation results in the super high dimension show that the new algorithm has the global searching ability in high-dimensional solution space.
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[2] Wang Y C, Zhao Q C, Wang A B 2008 Chin. Phys. B 17 2373
[3] Zu Y X, Zhou J 2012 Chin. Phys. B 21 019501
[4] Hao M L, Xu W, Gu X D, Qi L Y 2014 Chin. Phys. B 23 090501
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[7] Sabar N R, Ayob M, Kendall G, Qu R 2013 IEEE Trans. Evol. Comput. 17 840
[8] Bouaziz S, Alimi A M, Abraham A 2014 Proceedings of the 2014 IEEE Congress on Evolutionary Computation Beijing, China, July 6-11, 2014 p1951
[9] Liu R C, Jia J, Zhao M L, Jiao L C 2007 Control Theor. Appl. 24 777 (in Chinese) [刘若辰, 贾建, 赵梦玲, 焦李成 2007 控制理论与应用 24 777]
[10] Zitzler E, Thiele L 1999 IEEE Trans. Evol. Comput. 3 257
[11] Zitzler E, Laumanns M, Thiele L 2001 Proceedings of the 2001 EUROGEN on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems Athens, Greece 2001 p95
[12] Cai Z X, Wang Y 2006 IEEE Trans. Evol. Comput. 10 658
[13] Wang J, Li B 2011 Comput. Integrat. Manufact. Syst. 17 858 (in Chinese) [王君, 李波 2011 计算机集成制造系统 17 858]
[14] Qian J, Zheng J G 2012 J. Xi'an Jiaotong Univ. 46 51 (in Chinese) [钱洁, 郑建国 2012 西安交通大学学报 46 51]
[15] Li P C, Wang H Y, Song K P, Yang E L 2012 Acta Phys. Sin. 61 060302 (in Chinese) [李盼池, 王海英, 宋考平, 杨二龙 2012 61 060302]
[16] Fang W, Sun J, Xie Z P, Xu W B 2010 Acta Phys. Sin. 59 3686 (in Chinese) [方伟, 孙俊, 谢振平, 须文波 2010 59 3686]
[17] Liu L Z, Zhang J Q, Xu G X, Liang L S, Huang S F 2013 Acta Phys. Sin. 62 170501 (in Chinese) [刘乐柱, 张季谦, 许贵霞, 梁立嗣, 黄守芳 2013 62 170501]
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[1] de Castro L N, Von Zuben F J 2002 IEEE Trans. Evol. Comput. 6 239
[2] Wang Y C, Zhao Q C, Wang A B 2008 Chin. Phys. B 17 2373
[3] Zu Y X, Zhou J 2012 Chin. Phys. B 21 019501
[4] Hao M L, Xu W, Gu X D, Qi L Y 2014 Chin. Phys. B 23 090501
[5] Zhang Z H, Yue S G, Liao M, Long F 2014 Soft Comput. 18 185
[6] Mininno E, Neri F, Cupertino F, Naso D 2011 IEEE Trans. Evol. Comput. 15 32
[7] Sabar N R, Ayob M, Kendall G, Qu R 2013 IEEE Trans. Evol. Comput. 17 840
[8] Bouaziz S, Alimi A M, Abraham A 2014 Proceedings of the 2014 IEEE Congress on Evolutionary Computation Beijing, China, July 6-11, 2014 p1951
[9] Liu R C, Jia J, Zhao M L, Jiao L C 2007 Control Theor. Appl. 24 777 (in Chinese) [刘若辰, 贾建, 赵梦玲, 焦李成 2007 控制理论与应用 24 777]
[10] Zitzler E, Thiele L 1999 IEEE Trans. Evol. Comput. 3 257
[11] Zitzler E, Laumanns M, Thiele L 2001 Proceedings of the 2001 EUROGEN on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems Athens, Greece 2001 p95
[12] Cai Z X, Wang Y 2006 IEEE Trans. Evol. Comput. 10 658
[13] Wang J, Li B 2011 Comput. Integrat. Manufact. Syst. 17 858 (in Chinese) [王君, 李波 2011 计算机集成制造系统 17 858]
[14] Qian J, Zheng J G 2012 J. Xi'an Jiaotong Univ. 46 51 (in Chinese) [钱洁, 郑建国 2012 西安交通大学学报 46 51]
[15] Li P C, Wang H Y, Song K P, Yang E L 2012 Acta Phys. Sin. 61 060302 (in Chinese) [李盼池, 王海英, 宋考平, 杨二龙 2012 61 060302]
[16] Fang W, Sun J, Xie Z P, Xu W B 2010 Acta Phys. Sin. 59 3686 (in Chinese) [方伟, 孙俊, 谢振平, 须文波 2010 59 3686]
[17] Liu L Z, Zhang J Q, Xu G X, Liang L S, Huang S F 2013 Acta Phys. Sin. 62 170501 (in Chinese) [刘乐柱, 张季谦, 许贵霞, 梁立嗣, 黄守芳 2013 62 170501]
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