[1]赵嘉,陈丹丹,肖人彬,等.一种基于最大最小策略和非均匀变异的萤火虫算法[J].智能系统学报,2022,17(1):116-130.[doi:10.11992/tis.202106018]
ZHAO Jia,CHEN Dandan,XIAO Renbin,et al.A heterogeneous variation firefly algorithm with maximin strategy[J].CAAI Transactions on Intelligent Systems,2022,17(1):116-130.[doi:10.11992/tis.202106018]
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ZHAO Jia,CHEN Dandan,XIAO Renbin,et al.A heterogeneous variation firefly algorithm with maximin strategy[J].CAAI Transactions on Intelligent Systems,2022,17(1):116-130.[doi:10.11992/tis.202106018]
一种基于最大最小策略和非均匀变异的萤火虫算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第1期
页码:
116-130
栏目:
学术论文—人工智能基础
出版日期:
2022-01-05
- Title:
- A heterogeneous variation firefly algorithm with maximin strategy
- Keywords:
- firefly algorithm; multi-objective optimization; Pareto optimality; Maximin strategy; heterogeneous variation; exploration; convergence; diversity
- 分类号:
- TP242
- 摘要:
- 针对多目标萤火虫算法勘探能力弱、求解精度差的问题，本文提出了一种基于最大最小策略和非均匀变异的萤火虫算法（HVFA-M）。该算法首先引入Maximin策略，实现对外部档案的动态调整和对精英解的随机选择；其次，精英解结合当前最好解共同引导萤火虫进行全局搜索以扩大算法的搜索范围，提高算法的勘探能力，从而增加找寻全局最优解的概率；最后，在算法全面勘探的基础上，添加非均匀变异算子使得算法融合局部搜索的思想引导种群进行局部开采，进一步增强算法的寻优能力。将HVFA-M 与经典及新近多目标进化算法进行比较，实验结果表明：HVFA-M 能有效提升算法的勘探能力，在解的收敛性和多样性也表现出良好的性能。
- Abstract:
- Aiming at weak exploration ability and poor solving accuracy of multi-objective firefly algorithms, a heterogeneous variation firefly algorithm with maximin strategy (HVFA-M) is proposed in this paper. Firstly, the Maximin strategy is introduced to realize dynamic adjustment of external archive and random selection of elite solutions; Secondly, the elite solutions guide the firefly global search together with the current best solution to expand the search range and improve the exploration ability of the algorithm, so as to increase the probability of finding the global optimal solution; Finally, on the basis of comprehensive exploration of the algorithm, the heterogeneous variation operator is added to make the algorithm integrate the idea of local search to guide the population to carry out local mining, so as to further enhance the optimization ability of the algorithm. By comparing HVFA-M with the classical and recent multi-objective evolutionary algorithms, the experimental results show that HVFA-M can effectively improve the exploration ability of the algorithm, and also shows good performance in the convergence and diversity of solutions.
- 参考文献/References:
-
[1] FARAG M A, MOUSA A A, EL-SHORBAGY M A, et al. A new hybrid metaheuristic algorithm for multiobjective optimization problems[J]. International journal of computational intelligence systems, 2020, 13(1): 920–940.
[2] BAROCIO E, REGALADO J, CUEVAS E, et al. Modified bio-inspired optimisation algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem[J]. IET generation, transmission & distribution, 2017, 11(4): 1012–1022.
[3] LIAGKOURAS K, METAXIOTIS K. Enhancing the performance of MOEAs: an experimental presentation of a new fitness guided mutation operator[J]. Journal of experimental & theoretical artificial intelligence, 2017, 29(1): 91–131.
[4] KHAN A, BAIG A R. Multi-objective feature subset selection using non-dominated sorting genetic algorithm[J]. Journal of applied research and technology, 2015, 13(1): 145–159.
[5] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE transactions on evolutionary computation, 2002, 6(2): 182–197.
[6] DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints[J]. IEEE transactions on evolutionary computation, 2014, 18(4): 577–601.
[7] LüCKEN C, BARáN B, BRIZUELA C. A survey on multi-objective evolutionary algorithms for many-objective problems[J]. Computational optimization and applications, 2014, 58(3): 707–756.
[8] KNOWLES J D, CORNE D W. Approximating the nondominated front using the Pareto archived evolution strategy[J]. Evolutionary computation, 2000, 8(2): 149–172.
[9] ZITZLER E, THIELE L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach[J]. IEEE transactions on evolutionary computation, 1999, 3(4): 257–271.
[10] ADHAM A M, MOHD-GHAZALI N, AHMAD R. Performance optimization of a microchannel heat sink using the improved strength pareto evolutionary algorithm (SPEA2)[J]. Journal of engineering thermophysics, 2015, 24(1): 86–100.
[11] GADHVI B, SAVSANI V, PATEL V. Multi-objective optimization of vehicle passive suspension system using NSGA-II, SPEA2 and PESA-II[J]. Procedia technology, 2016, 23: 361–368.
[12] QI Yutao, MA Xiaoliang, LIU Fang, et al. MOEA/D with adaptive weight adjustment[J]. Evolutionary computation, 2014, 22(2): 231–264.
[13] LIU Ruochen, ZHOU Runan, REN Rui, et al. Multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition[J]. Information sciences, 2020, 509: 420–436.
[14] BASSEUR M, LIEFOOGHE A, LE K, et al. The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems[J]. Journal of heuristics, 2012, 18(2): 263–296.
[15] BADER J, ZITZLER E. HypE: an algorithm for fast hypervolume-based many-objective optimization[J]. Evolutionary computation, 2011, 19(1): 45–76.
[16] GUO Weian, CHEN Ming, WANG Lei, et al. Hyper multi-objective evolutionary algorithm for multi-objective optimization problems[J]. Soft computing, 2017, 21(20): 5883–5891.
[17] WANG Hongfeng, FU Yaping, HUANG Min, et al. A hybrid evolutionary algorithm with adaptive multi-population strategy for multi-objective optimization problems[J]. Soft computing, 2017, 21(20): 5975–5987.
[18] COELLO C A C, PULIDO G T, LECHUGA M S. Handling multiple objectives with particle swarm optimization[J]. IEEE transactions on evolutionary computation, 2004, 8(3): 256–279.
[19] MIRJALILI S, SAREMI S, MIRJALILI S M, et al. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization[J]. Expert systems with applications, 2016, 47: 106–119.
[20] YANG Shexin. Nature—Inspired metaheuristic algorithm[M]. Frome: Luniver Press, 2008.
[21] 赵嘉, 谢智峰, 吕莉, 等. 深度学习萤火虫算法[J]. 电子学报, 2018, 46(11): 2633–2641
ZHAO Jia, XIE Zhifeng, LYU Li, et al. Firefly algorithm with deep Learning[J]. Acta electronica sinica, 2018, 46(11): 2633–2641
[22] ZHAO Jia, CHEN Wenping, XIAO Renbin, et al. Firefly algorithm with division of roles for complex optimal scheduling[J]. Frontiers of information technology & electronic engineering, 2021, 22(10): 1311–1333.
[23] YANG Xinshe. Multiobjective firefly algorithm for continuous optimization[J]. Engineering with computers, 2013, 29(2): 175–184.
[24] TSAI C W, HUANG Yaoting, CHIANG Mingchao. A non-dominated sorting firefly algorithm for multi-objective optimization[C]//2014 14th International Conference on Intelligent Systems Design and Applications. New York, USA: 2014: 62-67.
[25] 谢承旺, 张飞龙, 陆建波, 等. 一种多策略协同的多目标萤火虫算法[J]. 电子学报, 2019, 47(11): 2359–2367
XIE Chengwang, ZHANG Feilong, LU Jianbo, et al. Multi-objective firefly algorithm based on multiply cooperative strategies[J]. Acta electronica sinica, 2019, 47(11): 2359–2367
[26] LYU L, ZHAO Jia, WANG Jiayuan, et al. Multi-objective firefly algorithm based on compensation factor and elite learning[J]. Future generation computer systems, 2019, 91: 37–47.
[27] 贾树晋, 杜斌, 岳恒. 基于局部搜索与混合多样性策略的多目标粒子群算法[J]. 控制与决策, 2012, 27(6): 813–818,826
JIA Shujin, DU Bin, YUE Heng. Local search and hybrid diversity strategy based multi-objective particle swarm optimization algorithm[J]. Control and decision, 2012, 27(6): 813–818,826
[28] ZHAO Xinchao, GAO Xiaoshan, HU Zechun. Evolutionary programming based on non-uniform mutation[J]. Applied mathematics and computation, 2007, 192(1): 1–11.
[29] BALLING R. The maximin fitness function; multi-objective city and regional planning[C]. International Conference on Evolutionary Multi-criterion Optimization, 2003: 1?15.
[30] 徐鸣, 沈希, 马龙华, 等. 一种多目标粒子群改进算法的研究[J]. 控制与决策, 2009, 24(11): 1713–1718,1728
XU Ming, SHEN Xi, MA Longhua, et al. Research on modified multi-objective particle swarm optimization[J]. Control and decision, 2009, 24(11): 1713–1718,1728
[31] GONG Maoguo, JIAO Licheng, ZHANG Xiangrong. A population-based artificial immune system for numerical optimization[J]. Neurocomputing, 2008, 72(1-3): 149–161.
[32] ZITZLER E, DEB K, THIELE L. Comparison of multiobjective evolutionary algorithms: empirical results[J]. Evolutionary computation, 2000, 8(2): 173–195.
[33] DEB K, THIELE L, LAUMANNS M, et al. Scalable multi-objective optimization test problems[C]. Proceedings of the 2002 Congress on Evolutionary Computation. Honolulu, US: IEEE, 2002, 1: 825?830.
[34] TANG Lixin, WANG Xianpeng. A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems[J]. IEEE transactions on evolutionary computation, 2013, 17(1): 20–45.
[35] ZHENG Li ming, WANG Qiang, ZHANG Sheng xin, et al. Population recombination strategies for multi-objective particle swarm optimization[J]. Soft computing, 2017, 21(16): 4693–4705.
[36] XING Huanlai, WANG Zhaoyuan, LI Tianrui, et al. An improved MOEA/D algorithm for multi-objective multicast routing with network coding[J]. Applied soft computing, 2017, 59: 88–103.
[37] WANG Wan liang, LI Wei kun, WANG Zheng, et al. Opposition-based multi-objective whale optimization algorithm with global grid ranking[J]. Neurocomputing, 2019, 341: 41–59.
[38] ZITZLER E, KüNZLI S. Indicator-based selection in multiobjective search[C]//Parallel problem solving from nature-PPSN VIII. Birmingham, UK: Springer, 2004: 832?842.
[39] TRIPATHI P K, BANDYOPADHYAY S, PAL S K. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients[J]. Information sciences, 2007, 177(22): 5033–5049.
[40] LIANG J J, QU B Y, SUGANTHAN P N, et al. Dynamic multi-swarm particle swarm optimization for multi-objective optimization problems[J]. 2012 IEEE congress on evolutionary computation, 2012: 1?8.
[41] CHEN Bili, ZENG Wenhua, LIN Yangbin, et al. A new local search-based multiobjective optimization algorithm[J]. IEEE transactions on evolutionary computation, 2015, 19(1): 50–73.
[42] TIAN Ye, ZHANG Xingyi, CHENG Ran, et al. A multi-objective evolutionary algorithm based on an enhanced inverted generational distance metric[C]//2016 IEEE Congress on Evolutionary Computation. Vancouver, Canada. IEEE, 2016: 5222?5229.
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备注/Memo
收稿日期:2021-06-11。
基金项目:科技创新2030—“新一代人工智能”重大项目(2018AAA0101200); 国家自然科学基金项目(52069014,51669014); 江西省杰出青年基金项目(2018ACB21029).
作者简介:赵嘉，教授，主要研究方向为智能计算、模式识别、大数据分析;陈丹丹，硕士研究生，主要研究方向为智能计算;肖人彬，教授，博士生导师，主要研究方向为群智能、涌现计算、复杂系统建模与仿真。主持国家级项目多项，获得教育部、湖北省自然科学奖和科学进步奖5项。发表学术论文200余篇，入选Elsevier“中国高被引学者”榜单。
通讯作者:肖人彬. E-mail：rbxiao@hust.edu.cn
更新日期/Last Update:
1900-01-01