最早是在2014年,接触到群体智能,那时候还只是小白,和老婆一起探索了下处于起步阶段的算法 ,随后在精读灰狼优化算法时,发现一些存在冲突的地方,于是提出了改进,偿试写英文sci期刊论文,但一直未果,2018年转业后,不知道做什么,阅读了中科院白皮书人工智能概要,发现群体智能也是一个大方向,于是转入较为深入的研究,积累素材,先是发掘了168个基础函数,然后据此深入做一些单目标优化,构建了基准,引导学生加入团队一起做。随后逐步想转入应用和多目标优化,目前还在路上。
这几天接受了一个会议keynote speaker邀请,准备稿子,想把过去的工作总结一下,于是汇总工作发现,在群体智能上,已经有40篇稿子了,其中sci检索差不多10篇,其余都是在会议上宣读的小改进。
价值不算太高,主要是还没有找到应用,从今年起,开始慢慢深入了。
[1] juan zhao, zheng ming gao.the bat algorithm and its parameters: the 4th international conference on electronic, communications and networks (cecnet2014) boca raton : crc press, [2014], 2015[c]. boca raton : crc press, [2014], iv: 1323-1326. 10.1201/b18592-237
[2] zheng-ming gao, juan zhao. an improved grey wolf optimization algorithm with variable weights[j]. computational intelligence and neuroscience, 2019, 2019: 2981282. 10.1155/2019/2981282
[3] zheng-ming gao, juan zhao, su-ruo li, et al.the improved equilibrium optimization algorithm: 2020 3rd international conference on advanced electronic materials, computers and software engineering (aemcse), 24-26 april 2020, 2020[c]. 26-30. 10.1109/aemcse50948.2020.00013
[4] g. a. o. z. -m, zhao j, h. u. y. -r, et al.the improved harris hawk optimization algorithm with the tent map: 2019 3rd international conference on electronic information technology and computer engineering (eitce), 18-20 oct. 2019, 2019[c]. 336-339. 10.1109/eitce47263.2019.9095091
[5] juan zhao, zheng-ming gao. the chaotic slime mould algorithm with chebyshev map[j]. journal of physics: conference series, 2020, 1631: 012071. 10.1088/1742-6596/1631/1/012071
[6] juan zhao, zheng-ming gao. the improved mayfly optimization algorithm with chebyshev map[j]. journal of physics: conference series, 2020, 1684: 012075. 10.1088/1742-6596/1684/1/012075
[7] juan zhao, zheng-ming gao, bao-lian jia.the improved slime mould algorithm with piecewice map: 2020 international symposium on computer engineering and intelligent communications (isceic), 7-9 aug. 2020, 2020[c]. 25-29. 10.1109/isceic51027.2020.00013
[8] juan zhao, zheng-ming gao, yu-jun zhang. piecewise linear map enabled harris hawk optimization algorithm[j]. journal of physics: conference series, 2021, 1994 (1): 012038. 10.1088/1742-6596/1994/1/012038
[9] juan zhao, zheng-ming gao, wu sun. the improved slime mould algorithm with levy flight[j]. journal of physics: conference series, 2020, 1617: 012033. 10.1088/1742-6596/1617/1/012033
[10] zheng-ming gao, juan zhao, su-ruo li, et al. the improved mayfly optimization algorithm[j]. journal of physics: conference series, 2020, 1684: 012077. 10.1088/1742-6596/1684/1/012077
[11] zheng-ming gao, juan zhao, qian yu, et al.the improved slime mould algorithm with exponential functions: 2020 international symposium on computer engineering and intelligent communications (isceic), 7-9 aug. 2020, 2020[c]. 30-33. 10.1109/isceic51027.2020.00014
[12] zheng-ming gao, juan zhao, su-ruo li. the binary equilibrium optimization algorithm with sigmoid transfer functions: proceedings of the 2020 12th international conference on machine learning and computing, shenzhen, china, [c]. association for computing machinery, 2020. 193–197. 10.1145/3383972.3384064
[13] juan zhao, zheng-ming gao. simulation research on the binary equilibrium optimization algorithm: proceedings of the 2020 12th international conference on machine learning and computing, shenzhen, china, [c]. association for computing machinery, 2020. 140–144. 10.1145/3383972.3384063
[14] zhengming gao, juan zhao, xuejun tian. the improved equilibrium optimization algorithm with averaged candidates[j]. journal of physics: conference series, 2020, 1575: 012105. 10.1088/1742-6596/1575/1/012105
[15] juan zhao, zhengming gao. the improved equilibrium optimization algorithm with best candidates[j]. journal of physics: conference series, 2020, 1575: 012089. 10.1088/1742-6596/1575/1/012089
[16] juan zhao, zheng-ming gao. the negative mayfly optimization algorithm[j]. journal of physics: conference series, 2020, 1693 (1): 012098. 10.1088/1742-6596/1693/1/012098
[17] zhao j, g. a. o. z. -m.the fully informed mayfly optimization algorithm: 2020 international conference on big data & artificial intelligence & software engineering (icbase), 30 oct.-1 nov. 2020, 2020[c]. 450-453. 10.1109/icbase51474.2020.00101
[18] zhao j, g. a. o. z. -m.bare bones mayfly optimization algorithm: 2020 2nd international conference on machine learning, big data and business intelligence (mlbdbi), 23-25 oct. 2020, 2020[c]. 238-241. 10.1109/mlbdbi51377.2020.00051
[19] yu-jun zhang, yu-fei wang, yu-xin yan, et al. lmraoa: an improved arithmetic optimization algorithm with multi-leader and high-speed jumping based on opposition-based learning solving engineering and numerical problems[j]. alexandria engineering journal, 2022, 61 (12): 12367-12403.
[20] g. a. o. z. -m, l. i. s. -r, zhao j, et al.heterogeneous mayfly optimization algorithm: 2020 2nd international conference on machine learning, big data and business intelligence (mlbdbi), 23-25 oct. 2020, 2020[c]. 227-230. 10.1109/mlbdbi51377.2020.00049
[21] juan zhao, zheng-ming gao. the heterogeneous aquila optimization algorithm[j]. mathematical biosciences and engineering, 2022, 19 (6): 5867-5904. 10.3934/mbe.2022275
[22] zheng-ming gao, juan zhao, su-ruo li, et al. the improved equilibrium optimization algorithm with multiple updating discipline[j]. journal of physics: conference series, 2020, 1682: 012054. 10.1088/1742-6596/1682/1/012054
[23] zheng-ming gao, juan zhao, xu-ruo li, et al. an improved sine cosine algorithm with multiple updating ways for individuals[j]. journal of physics: conference series, 2020, 1678: 012079. 10.1088/1742-6596/1678/1/012079
[24] juan zhao, zheng-ming gao. an improved grey wolf optimization algorithm with multiple tunnels for updating[j]. journal of physics: conference series, 2020, 1678: 012096. 10.1088/1742-6596/1678/1/012096
[25] j. zhao, z. m. gao.the multi-start mayfly optimization algorithm: 2020 7th international forum on electrical engineering and automation (ifeea), 25-27 sept. 2020, 2020[c]. 879-882. 10.1109/ifeea51475.2020.00184
[26] juan zhao, zheng-ming gao, hua-feng chen. the simplified aquila optimization algorithm[j]. ieee access, 2022, 10: 22487-22515. 10.1109/access.2022.3153727
[27] z. m. gao, s. r. li, j. zhao, et al.the guaranteed convergence mayfly optimization algorithm: 2020 7th international forum on electrical engineering and automation (ifeea), 25-27 sept. 2020, 2020[c]. 858-861. 10.1109/ifeea51475.2020.00179
[28] yun-feng zou, juan zhao, zheng-ming gao. guaranteed convergence sine cosine algorithm: proceedings of the 2021 5th international conference on electronic information technology and computer engineering, xiamen, china, [c]. association for computing machinery, 2021. 986–990. 10.1145/3501409.3501586
[29] g. a. o. z. -m, l. i. s. -r, zhao j, et al.self-organizing hierarchical mayfly optimization algorithm: 2020 international conference on big data & artificial intelligence & software engineering (icbase), 30 oct.-1 nov. 2020, 2020[c]. 355-358. 10.1109/icbase51474.2020.00081
[30] jiahao zhang, zhengming gao, suruo li, et al. improved intelligent clonal optimizer based on adaptive parameter strategy[j]. mathematical biosciences and engineering, 2022, 19 (10): 10275-10315. 10.3934/mbe.2022481
[31] yufei wang, yujun zhang, yuxin yan, et al. an enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based learning[j]. mathematical biosciences and engineering, 2023, 20 (4): 6422-6467. 10.3934/mbe.2023278
[32] j. zhao, z. m. gao.the regrouping mayfly optimization algorithm: 2020 7th international forum on electrical engineering and automation (ifeea), 25-27 sept. 2020, 2020[c]. 1026-1029. 10.1109/ifeea51475.2020.00214
[33] z. gao, j. zhao, s. li.the hybridized slime mould and particle swarm optimization algorithms: 2020 ieee 3rd international conference on automation, electronics and electrical engineering (auteee), 20-22 nov. 2020, 2020[c]. 304-308. 10.1109/auteee50969.2020.9315694
[34] yu jun zhang, yu x yan, juan zhao, et al. aoaao: the hybrid algorithm of arithmetic optimization algorithm with aquila optimizer[j]. ieee access, 2022, 10: 10907-10933. 10.1109/access.2022.3144431
[35] yu-jun zhang, juan zhao, zheng-ming gao. hybridized improvement of the chaotic harris hawk optimization algorithm and aquila optimizer[m]. spie, 2022.
[36] yu-jun zhang, yu-xin yan, juan zhao, et al.chaotic map enabled algorithm hybridizing hunger games search algorithm with aquila optimizer: icmlca 2021; 2nd international conference on machine learning and computer application, 17-19 dec. 2021, 2021[c]. 1-5.
[37] j. zhao, z. gao, y. tian.the hybridized equilibrium and particle swarm optimization algorithms: 2020 ieee 3rd international conference on automation, electronics and electrical engineering (auteee), 20-22 nov. 2020, 2020[c]. 294-298. 10.1109/auteee50969.2020.9315629
[38] yu-jun zhang, yu-xin yan, juan zhao, et al. cscahho: chaotic hybridization algorithm of the sine cosine with harris hawk optimization algorithms for solving global optimization problems[j]. plos one, 2022, 17 (5): e0263387. 10.1371/journal.pone.0263387
[39] juan zhao, yu-jun zhang, shu-jia li, et al. a chaotic self-adaptive jaya algorithm for parameter extraction of photovoltaic models[j]. mathematical biosciences and engineering, 2022, 19 (6): 5638-5670. 10.3934/mbe.2022264
[40] yu-jun zhang, yu-fei wang, shu-jia li, et al. an enhanced adaptive comprehensive learning hybrid algorithm of rao-1 and jaya algorithm for parameter extraction of photovoltaic models[j]. mathematical biosciences and engineering, 2022, 19 (6): 5610-5637. 10.3934/mbe.2022263