[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]
点击复制
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]
基于概率评估差分进化的多峰值优化
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第2期
页码:
427-439
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2022-03-05
- Title:
- Multimodal function optimization based on DE algorithm of probabilistic evaluation mechanism
- Keywords:
- multimodal function optimization; global optima; evolutionary algorithm; two-level fitness evaluation probability; selective evaluation; differential evolution algorithm; historical update experience; high-efficiency fitness evaluation
- 分类号:
- TP18
- 摘要:
- 多峰值优化问题要求算法同时找到一个问题的多个全局最优解。近年来，演化算法已被广泛用于求解多峰值优化问题。然而，如何在极其有限的适应值评估次数内找到问题的多个全局最优解依然为演化算法带来了巨大的挑战。通过分析个体的历史更新经验，为每个个体赋予双层适应值评估概率，对个体进行选择性评估，从而减少算法运行过程中无效或低效的适应值评估，提出了一种基于概率评估差分进化的多峰值优化算法。实验结果显示，概率评估机制可以为算法节省更多的适应值评估次数，增加迭代过程，效果远好于其他主流的多峰值优化算法。
- Abstract:
- Multimodal optimization problems (MMOPs) require algorithms to simultaneously determine multiple global optima. Recently, evolutionary algorithms (EAs) have been widely used to solve MMOPs. However, there is still a great challenge for EAs to determine multiple global optima within very limited fitness evaluation (FE) times. To solve the inefficient FE, this paper proposes a multimodal function optimization algorithm based on the differential evolution algorithm of the probabilistic evaluation mechanism for solving MMOPs. In this algorithm, each individual will be assigned with the two-level FE probability according to its historical update experience to determine whether it needs to be evaluated. The experimental results show that the probabilistic evaluation mechanism can reduce FE times for the proposed algorithm and increase its iterative process, and its effect is much better than that of other mainstream mechanisms.
- 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
更新日期/Last Update:
1900-01-01