[1]王子佳,詹志辉.基于概率评估差分进化的多峰值优化[J].智能系统学报,2022,17(2):427-439.[doi:10.11992/tis.202108007]
 WANG Zijia,ZHAN Zhihui.Multimodal function optimization based on DE algorithm of probabilistic evaluation mechanism[J].CAAI Transactions on Intelligent Systems,2022,17(2):427-439.[doi:10.11992/tis.202108007]
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基于概率评估差分进化的多峰值优化

参考文献/References:
[1] WONG K C, LEUNG K S, WONG M H. Protein structure prediction on a lattice model via multimodal optimization techniques[C]//Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. Portland, USA, 2010: 155-162.
[2] WOO D K, CHOI J H, ALI M, et al. A novel multimodal optimization algorithm applied to electromagnetic optimization[J]. IEEE transactions on magnetics, 2011, 47(6): 1667–1673.
[3] 孙文新, 穆华平. 自适应群体结构的粒子群优化算法[J]. 智能系统学报, 2013, 8(4): 372–376
SUN Wenxin, MU Huaping. Particle swarm optimization based on self-adaptive population structure[J]. CAAI transactions on intelligent systems, 2013, 8(4): 372–376
[4] 陈丽, 马楠, 逄桂林, 等. 多视角数据融合的特征平衡YOLOv3行人检测研究[J]. 智能系统学报, 2021, 16(1): 57–65
CHEN Li, MA Nan, PANG Guilin, et al. Research on multi-view data fusion and balanced YOLOv3 for pedestrian detection[J]. CAAI transactions on intelligent systems, 2021, 16(1): 57–65
[5] 杨会成, 朱文博, 童英. 基于车内外视觉信息的行人碰撞预警方法[J]. 智能系统学报, 2019, 14(4): 752–760
YANG Huicheng, ZHU Wenbo, TONG Ying. Pedestrian collision warning system based on looking-in and looking-out visual information analysis[J]. CAAI transactions on intelligent systems, 2019, 14(4): 752–760
[6] 伍鹏瑛, 张建明, 彭建, 等. 多层卷积特征的真实场景下行人检测研究[J]. 智能系统学报, 2019, 14(2): 306–315
WU Pengying, ZHANG Jianming, PENG Jian, et al. Research on pedestrian detection based on multi-layer convolution feature in real scene[J]. CAAI transactions on intelligent systems, 2019, 14(2): 306–315
[7] WANG Zijia, ZHAN Zhihui, YU Weijie, et al. Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling[J]. IEEE transactions on cybernetics, 2020, 50(6): 2715–2729.
[8] 卢福强, 刘婷, 杜子超, 等. 模糊粒子群优化算法的第四方物流运输时间优化[J]. 智能系统学报, 2021, 16(3): 474–483
LU Fuqiang, LIU Ting, DU Zichao, et al. Convergence fuzzy particle swarm optimization based transportation time optimization of 4PL[J]. CAAI transactions on intelligent systems, 2021, 16(3): 474–483
[9] 陈强, 王宇嘉, 梁海娜, 等. 目标空间映射策略的高维多目标粒子群优化算法[J]. 智能系统学报, 2021, 16(2): 362–370
CHEN Qiang, WANG Yujia, LIANG Haina, et al. Multi-objective particle swarm optimization algorithm based on an objective space papping strategy[J]. CAAI transactions on intelligent systems, 2021, 16(2): 362–370
[10] 吴一全, 周建伟. 布谷鸟搜索算法研究及其应用进展[J]. 智能系统学报, 2020, 15(3): 435–444
WU Yiquan, ZHOU Jianwei. Overview of the cuckoo search algorithm and its applications[J]. CAAI transactions on intelligent systems, 2020, 15(3): 435–444
[11] 钱小宇, 葛洪伟, 蔡明. 基于目标空间分解和连续变异的多目标粒子群算法[J]. 智能系统学报, 2019, 14(3): 464–470
QIAN Xiaoyu, GE Hongwei, CAI Ming. Decomposition and continuous mutation-based multi-objective particle swarm optimization[J]. CAAI transactions on intelligent systems, 2019, 14(3): 464–470
[12] 裴小兵, 孙志卫. 改进区块遗传算法解决分布式车间调度问题[J]. 智能系统学报, 2021, 16(2): 303–312
PEI Xiaobing, SUN Zhiwei. Solving distributed-shop scheduling problems based on modified genetic algorithm[J]. CAAI transactions on intelligent systems, 2021, 16(2): 303–312
[13] ZHAN Zhihui, WANG Zijia, JIN Hu, et al. Adaptive distributed differential evolution[J]. IEEE transactions on cybernetics, 2020, 50(11): 4633–4647.
[14] WANG Zijia, ZHAN Zhihui, KWONG S, et al. Adaptive granularity learning distributed particle swarm optimization for large-scale optimization[J]. IEEE transactions on cybernetics, 2021, 51(3): 1175–1188.
[15] THOMSEN R. Multimodal optimization using crowding-based differential evolution[C]//Proceedings of the 2004 Congress on Evolutionary Computation. Portland, USA, 2004: 1382-1389.
[16] LI Xiaodong. Efficient differential evolution using speciation for multimodal function optimization[C]//Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. Washington, USA, 2005: 873-880.
[17] GAO Weifeng, YEN G G, LIU Sanyang. A cluster-based differential evolution with self-adaptive strategy for multimodal optimization[J]. IEEE transactions on cybernetics, 2014, 44(8): 1314–1327.
[18] QU B Y, SUGANTHAN P N, LIANG J J. Differential evolution with neighborhood mutation for multimodal optimization[J]. IEEE transactions on evolutionary computation, 2012, 16(5): 601–614.
[19] ZHANG Yuhui, GONG Yuejiao, ZHANG Huaxiang, et al. Toward fast niching evolutionary algorithms: A locality sensitive hashing-based approach[J]. IEEE transactions on evolutionary computation, 2017, 21(3): 347–362.
[20] ZHAO Hong, ZHAN Zhihui, LIN Ying, et al. Local binary pattern-based adaptive differential evolution for multimodal optimization problems[J]. IEEE transactions on cybernetics, 2020, 50(7): 3343–3357.
[21] CHEN Zonggan, ZHAN Zhihui, WANG Hua, et al. Distributed individuals for multiple peaks: a novel differential evolution for multimodal optimization problems[J]. IEEE transactions on evolutionary computation, 2020, 24(4): 708–719.
[22] WANG Zijia, ZHAN Zhihui, ZHANG Jun. Distributed minimum spanning tree differential evolution for multimodal optimization problems[J]. Soft computing, 2019, 23(24): 13339–13349.
[23] URSEM R K. Multinational evolutionary algorithms[C]//Proceedings of the 1999 Congress on Evolutionary Computation. Washington, USA, 1999: 1633-1640.
[24] WANG Zijia, ZHAN Zhihui, LIN Ying, et al. Automatic niching differential evolution with contour prediction approach for multimodal optimization problems[J]. IEEE transactions on evolutionary computation, 2020, 24(1): 114–128.
[25] BISWAS S, KUNDU S, DAS S. Inducing niching behavior in differential evolution through local information sharing[J]. IEEE transactions on evolutionary computation, 2015, 19(2): 246–263.
[26] BISWAS S, KUNDU S, DAS S. An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution[J]. IEEE transactions on cybernetics, 2014, 44(10): 1726–1737.
[27] WANG Zijia, ZHAN Zhihui, LIN Ying, et al. Dual-strategy differential evolution with affinity propagation clustering for multimodal optimization problems[J]. IEEE transactions on evolutionary computation, 2018, 22(6): 894–908.
[28] STOEAN C, PREUSS M, STOEAN R, et al. Multimodal optimization by means of a topological species conservation algorithm[J]. IEEE transactions on evolutionary computation, 2010, 14(6): 842–864.
[29] WANG Zijia, ZHOU Yuren, ZHANG Jun. Adaptive estimation distribution distributed differential evolution for multimodal optimization problems[J]. IEEE transactions on cybernetics, 2020,DOI: 10.1109/TCYB.2020.3038694.
[30] STORN R, PRICE K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization, 1997, 11(4): 341–359.
[31] YANG Qiang, CHEN Weineng, LI Yun, et al. Multimodal estimation of distribution algorithms[J]. IEEE transactions on cybernetics, 2017, 47(3): 636–650.
[32] WANG Yong, LI Hanxiong, YEN G G, et al. MOMMOP: multiobjective optimization for locating multiple optimal solutions of multimodal optimization problems[J]. IEEE transactions on cybernetics, 2015, 45(4): 830–843.
[33] LI Xiaodong, ENGELBRECHT A, EPITROPAKIS M G. Benchmark functions for CEC’2013 special session and competition on niching methods for multimodal function optimization[R]. Australia: RMIT University, Evolutionary Computation and Machine Learning Group, 2013.
[34] LI Xiaodong. Niching without niching parameters: particle swarm optimization using a ring topology[J]. IEEE transactions on evolutionary computation, 2010, 14(1): 150–169.
[35] QU B Y, SUGANTHAN P N, DAS S. A distance-based locally informed particle swarm model for multimodal optimization[J]. IEEE transactions on evolutionary computation, 2013, 17(3): 387–402.
[36] DERRAC J, GARCíA S, MOLINA D, et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J]. Swarm and evolutionary computation, 2011, 1(1): 3–18.
[37] FIELDSEND J E. Running up those hills: multi-modal search with the niching migratory multi-swarm optimiser[C]//2014 IEEE Congress on Evolutionary Computation. Beijing, China, 2014: 2593?2600.

备注/Memo

收稿日期:2021-08-09。
基金项目:国家自然科学基金项目(61772207,61873097,62106055)
作者简介:

王子佳,副教授,主要研究方向为演化算法及应用。
詹志辉,教授,博士生导师,主要研究方向为人工智能、进化计算、群体智能、云计算和大数据。先后荣获吴文俊人工智能优秀青年奖、IEEE计算智能学会全球杰出博士学位论文奖、中国计算机学会优秀博士论文奖。发表学术论文100余篇,其中IEEETransactions系列的计算机领域顶尖国际期刊论文40余篇
通讯作者:詹志辉.E-mail:zhanapollo@163.com

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