Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2237-2247.DOI: 10.11772/j.issn.1001-9081.2022060896
Special Issue: 先进计算
• Advanced computing • Previous Articles Next Articles
Meiying CHENG1,2(), Qian QIAN2,3, Weiqing XIONG4
Received:
2022-06-21
Revised:
2022-08-22
Accepted:
2022-08-24
Online:
2022-09-22
Published:
2023-07-10
Contact:
Meiying CHENG
About author:
CHENG Meiying, born in 1983, Ph. D., associate professor. Her research interests include swarm intelligence optimization.Supported by:
通讯作者:
程美英
作者简介:
程美英(1983—),女,安徽黄山人,副教授,博士,主要研究方向:群体智能优化;基金资助:
CLC Number:
Meiying CHENG, Qian QIAN, Weiqing XIONG. Symbiotic organisms search algorithm for information transfer multi-task optimization[J]. Journal of Computer Applications, 2023, 43(7): 2237-2247.
程美英, 钱乾, 熊伟清. 信息迁移多任务优化共生生物搜索算法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2237-2247.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060896
名称 | 表达式 | 简写 | 最优值 | 特征 |
---|---|---|---|---|
Sum of Different Powers function | SD | 0 | 单模态 | |
Sum Squares function | SSF | 0 | 单模态 | |
Zakharov function | Z | 0 | 单模态 | |
Sphere function | S | 0 | 单模态 | |
Quartic | Q | 0 | 单模态 | |
Generalized Griewank | G | 0 | 多模态 | |
Ackley function | A | 0 | 多模态 | |
Rastrigin function | R | 0 | 多模态 |
Tab. 1 MTO benchmark functions
名称 | 表达式 | 简写 | 最优值 | 特征 |
---|---|---|---|---|
Sum of Different Powers function | SD | 0 | 单模态 | |
Sum Squares function | SSF | 0 | 单模态 | |
Zakharov function | Z | 0 | 单模态 | |
Sphere function | S | 0 | 单模态 | |
Quartic | Q | 0 | 单模态 | |
Generalized Griewank | G | 0 | 多模态 | |
Ackley function | A | 0 | 多模态 | |
Rastrigin function | R | 0 | 多模态 |
多任务实例 | 最优适应值 | 平均适应值 |
---|---|---|
30SD | 5.45E-05 | 0.018 1 |
30SSF | 0.003 3 | 1.210 1 |
30Z | 0.088 4 | 20.974 2 |
30S | 0.002 1 | 0.201 7 |
30Q | 0.000 0 | 0.270 0 |
30G | 0.001 7 | 0.047 2 |
30A | 0.024 5 | 1.250 4 |
30R | 1.305 3 | 5.969 4 |
50SD | 0.064 7 | 0.375 7 |
50SSF | 0.016 0 | 5.894 2 |
50Z | 11.311 9 | 149.824 5 |
50S | 0.519 7 | 0.594 5 |
50Q | 0.000 0 | 0.586 7 |
50G | 0.013 0 | 0.076 8 |
50A | 1.180 1 | 2.339 4 |
50R | 3.676 9 | 12.376 3 |
100SD | 8.257 7 | 75.907 6 |
100SSF | 21.634 6 | 35.425 1 |
100Z | 62.847 5 | 420.281 0 |
100S | 6.812 6 | 18.636 7 |
100Q | 0.602 6 | 0.695 1 |
100G | 0.236 6 | 0.358 5 |
100A | 3.013 0 | 6.904 1 |
100R | 7.833 7 | 72.890 5 |
(30SD, 30SSF, 30Z) | (0, 0, 0) | (1.85E-59, 1.82E-14, 6.01E-21) |
(50SD, 50SSF, 50Z) | (0, 0, 0) | (7.83E-30, 1.44E-8, 6.41E-19) |
(100SD, 100SSF, 100Z) | (0, 0, 0) | (2.91E-79, 2.93E-21, 6.87E-19) |
(30S, 30Q, 30G) | (0, 0, 0) | (5.62E-19, 6.67E-2, 0) |
(50S, 50Q, 50G) | (0, 0, 0) | (6.31E-14, 0.357 1, 0) |
(100S, 100Q, 100G) | (0, 0, 0) | (3.54E-10, 0.622 9, 3.58E-11) |
(30S, 30A, 30R) | (0, 0, 0) | (3.19E-19, 1.42E-10, 0) |
(50S, 50A, 50R) | (0, 0, 0) | (6.10E-17, 2.59E-7, 0) |
(100S, 100A, 100R) | (0, 5.89E-16, 0) | (1.36E-15, 2.43E-9, 0) |
Tab. 2 Experimental results for different shape high-dimensional functions by SOSSA and ITMTSOS
多任务实例 | 最优适应值 | 平均适应值 |
---|---|---|
30SD | 5.45E-05 | 0.018 1 |
30SSF | 0.003 3 | 1.210 1 |
30Z | 0.088 4 | 20.974 2 |
30S | 0.002 1 | 0.201 7 |
30Q | 0.000 0 | 0.270 0 |
30G | 0.001 7 | 0.047 2 |
30A | 0.024 5 | 1.250 4 |
30R | 1.305 3 | 5.969 4 |
50SD | 0.064 7 | 0.375 7 |
50SSF | 0.016 0 | 5.894 2 |
50Z | 11.311 9 | 149.824 5 |
50S | 0.519 7 | 0.594 5 |
50Q | 0.000 0 | 0.586 7 |
50G | 0.013 0 | 0.076 8 |
50A | 1.180 1 | 2.339 4 |
50R | 3.676 9 | 12.376 3 |
100SD | 8.257 7 | 75.907 6 |
100SSF | 21.634 6 | 35.425 1 |
100Z | 62.847 5 | 420.281 0 |
100S | 6.812 6 | 18.636 7 |
100Q | 0.602 6 | 0.695 1 |
100G | 0.236 6 | 0.358 5 |
100A | 3.013 0 | 6.904 1 |
100R | 7.833 7 | 72.890 5 |
(30SD, 30SSF, 30Z) | (0, 0, 0) | (1.85E-59, 1.82E-14, 6.01E-21) |
(50SD, 50SSF, 50Z) | (0, 0, 0) | (7.83E-30, 1.44E-8, 6.41E-19) |
(100SD, 100SSF, 100Z) | (0, 0, 0) | (2.91E-79, 2.93E-21, 6.87E-19) |
(30S, 30Q, 30G) | (0, 0, 0) | (5.62E-19, 6.67E-2, 0) |
(50S, 50Q, 50G) | (0, 0, 0) | (6.31E-14, 0.357 1, 0) |
(100S, 100Q, 100G) | (0, 0, 0) | (3.54E-10, 0.622 9, 3.58E-11) |
(30S, 30A, 30R) | (0, 0, 0) | (3.19E-19, 1.42E-10, 0) |
(50S, 50A, 50R) | (0, 0, 0) | (6.10E-17, 2.59E-7, 0) |
(100S, 100A, 100R) | (0, 5.89E-16, 0) | (1.36E-15, 2.43E-9, 0) |
多任务实例 | ITMTSOS算法 | MT-CPSO算法[ | IEPSOM算法[ |
---|---|---|---|
(30SD, 30SSF, 30Z) | (1.85E-59, 1.82E-14, 6.01E-21) | (8.27E-15, 2.77E-5, 7.99E-7) | (4.19E-23, 1.33E-7, 0.001) |
(50SD, 50SSF, 50Z) | (7.83E-30, 1.44E-8, 6.41E-19) | (1.66E-18, 5.72E-5, 0.004 1) | (8.31E-19, 3.05E-5, 0.006 9) |
(100SD,100SSF, 100Z) | (2.91E-79, 2.93E-21, 6.87E-19) | (3.56E-14, 0.001 1, 0.051 9) | (1.84E-15, 0.001 9, 0.023 9) |
(30S, 30Q, 30G) | (5.62E-19, 6.67E-2, 0) | (2.80E-6, 0, 0) | (1.56E-16, 0, 1.04E-11) |
(50S, 50Q, 50G) | (6.31E-14, 0.357 1, 0) | (3.39E-7, 0, 3.42E-15) | (2.75E-6, 0, 1.45E-5) |
(100S, 100Q, 100G) | (3.54E-10, 0.622 9, 3.58E-11) | (3.40E-6, 0, 4.85E-11) | (8.22E-6, 0, 5.37E-12) |
(30S, 30A, 30R) | (3.19E-19, 1.42E-10, 0) | (3.49E-7, 1.75E-9, 0) | (6.33E-53, 1.48E-8, 1.14E-14) |
(50S, 50A, 50R) | (6.10E-17, 2.59E-7, 0) | (2.50E-8, 1.06E-9, 0) | (4.15E-46, 8.89E-6, 3.36E-8) |
(100S, 100A, 100R) | (1.36E-15, 2.43E-9, 0) | (5.19E-15, 4.71E-7, 2.40E-10) | (9.81E-11, 5.14E-9, 2.96E-7) |
Tab. 3 Average fitness comparison of different algorithms in solving multi-task high-dimensional functions
多任务实例 | ITMTSOS算法 | MT-CPSO算法[ | IEPSOM算法[ |
---|---|---|---|
(30SD, 30SSF, 30Z) | (1.85E-59, 1.82E-14, 6.01E-21) | (8.27E-15, 2.77E-5, 7.99E-7) | (4.19E-23, 1.33E-7, 0.001) |
(50SD, 50SSF, 50Z) | (7.83E-30, 1.44E-8, 6.41E-19) | (1.66E-18, 5.72E-5, 0.004 1) | (8.31E-19, 3.05E-5, 0.006 9) |
(100SD,100SSF, 100Z) | (2.91E-79, 2.93E-21, 6.87E-19) | (3.56E-14, 0.001 1, 0.051 9) | (1.84E-15, 0.001 9, 0.023 9) |
(30S, 30Q, 30G) | (5.62E-19, 6.67E-2, 0) | (2.80E-6, 0, 0) | (1.56E-16, 0, 1.04E-11) |
(50S, 50Q, 50G) | (6.31E-14, 0.357 1, 0) | (3.39E-7, 0, 3.42E-15) | (2.75E-6, 0, 1.45E-5) |
(100S, 100Q, 100G) | (3.54E-10, 0.622 9, 3.58E-11) | (3.40E-6, 0, 4.85E-11) | (8.22E-6, 0, 5.37E-12) |
(30S, 30A, 30R) | (3.19E-19, 1.42E-10, 0) | (3.49E-7, 1.75E-9, 0) | (6.33E-53, 1.48E-8, 1.14E-14) |
(50S, 50A, 50R) | (6.10E-17, 2.59E-7, 0) | (2.50E-8, 1.06E-9, 0) | (4.15E-46, 8.89E-6, 3.36E-8) |
(100S, 100A, 100R) | (1.36E-15, 2.43E-9, 0) | (5.19E-15, 4.71E-7, 2.40E-10) | (9.81E-11, 5.14E-9, 2.96E-7) |
多任务实例 | 最优适应值 | 平均适应值 | 测试集公布最优结果 |
---|---|---|---|
weing1 | 163 935 | 160 728 | 141 278 |
weing7 | 1 102 872 | 1 102 836 | 1 095 445 |
TSM | 107 | 141 | — |
(weing1, weing7, TSM) | (164 045,1 118 047,81) | (164 045,111 7611,108) | — |
Tab. 4 Experimental results for MKP and TSM by SOSSA and ITMTSOS
多任务实例 | 最优适应值 | 平均适应值 | 测试集公布最优结果 |
---|---|---|---|
weing1 | 163 935 | 160 728 | 141 278 |
weing7 | 1 102 872 | 1 102 836 | 1 095 445 |
TSM | 107 | 141 | — |
(weing1, weing7, TSM) | (164 045,1 118 047,81) | (164 045,111 7611,108) | — |
1 | CHENG M Y, PRAYOGO D. Symbiotic organisms search: a new metaheuristic optimization algorithm[J]. Computers and Structures, 2014, 139: 98-112. 10.1016/j.compstruc.2014.03.007 |
2 | 王艳娇, 马壮. 基于子种群拉伸操作的精英共生生物搜索算法[J]. 控制与决策, 2019, 34 (7): 1355-1364. 10.13195/j.kzyjc.2017.1747 |
WANG Y J, MA Z. Elite symbiotic organisms search algorithm based on subpopulation stretching operation[J]. Control and Decision, 2019, 34 (7): 1355- 1364. 10.13195/j.kzyjc.2017.1747 | |
3 | MIAO F H, YAO L, ZHAO X J. Symbiotic organisms search algorithm using random walk and adaptive Cauchy mutation on the feature selection of sleep staging[J]. Expert Systems with Applications, 2021, 176: No.114887. 10.1016/j.eswa.2021.114887 |
4 | NGUYEN-VAN S, NGUYEN K T, LUONG V H, et al. A novel hybrid differential evolution and symbiotic organisms search algorithm for size and shape optimization of truss structure under multiple frequency constraints[J]. Expert Systems with Applications, 2021, 184: No.115534. 10.1016/j.eswa.2021.115534 |
5 | CHAKRABORTY F, ROY P K, NANDI D. A novel chaotic symbiotic organisms search optimization in multilevel image segmentation[J]. Soft Computing, 2021, 25 (10): 6973-6998. 10.1007/s00500-021-05611-w |
6 | DUMAN S, LI J, WU L, et al. Symbiotic organisms search algorithm-based security-constrained AC-DC OPF regarding uncertainty of wind, PV and PEV systems[J]. Soft Computing, 2021, 25 (14): 9389-9426. 10.1007/s00500-021-05764-8 |
7 | 秦旋, 房子涵, 张赵鑫. 混合共生生物搜索算法求解置换流水车间调度问题[J]. 浙江大学学报 (工学版), 2020, 54 (4): 712-721. 10.3785/j.issn.1008-973X.2020.04.010 |
QIN X, FANG Z H, ZHANG Z X. Hybrid symbiotic organisms search algorithm for permutation flow shop scheduling problem[J]. Journal of Zhejiang University (Engineering Science), 2020, 54 (4): 712-721. 10.3785/j.issn.1008-973X.2020.04.010 | |
8 | CHAKRABORTY F, ROY P K, NANDI D. Symbiotic organisms search optimization for multilevel image thresholding[J]. International Journal of Swarm Intelligence, 2020, 11 (2): 31-61. 10.4018/ijsir.2020040103 |
9 | GUPTA A, ONG Y S, FENG L. Multifactorial evolution: toward evolutionary multitasking[J]. IEEE Transactions on Evolutionary Computation, 2016, 20 (3): 343-357. 10.1109/tevc.2015.2458037 |
10 | ONG Y S, GUPTA A. Evolutionary multitasking: a computer science view of cognitive multitasking[J]. Cognitive Computation, 2016, 8 (2): 125-142. 10.1007/s12559-016-9395-7 |
11 | BAI L, LIN W, GUPTA A, et al. From multitask gradient descent to gradient-free evolutionary multitasking: a proof of faster convergence[J]. IEEE Transaction on Cybernetics, 2022, 52 (8): 8561-8573. 10.1109/tcyb.2021.3052509 |
12 | GUPTA A, ZHOU L, ONG Y S, et al. Half a dozen real-world application of evolutionary multitasking and more[J]. IEEE Computational Intelligence Magazine, 2022, 17 (2): 49-66. 10.1109/mci.2022.3155332 |
13 | FENG L, ZHOU L, GUPTA A, et al. Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking[J]. IEEE Transactions on Cybernetics, 2021, 51 (6): 3171-3184. 10.1109/tcyb.2019.2955599 |
14 | FENG L, HUNAG Y X, ZHOU L, et al. Explicit evolutionary multitasking for combinatorial optimization: a case study on capacitated vehicle routing problem[J]. IEEE Transactions on Cybernetics, 2021, 51 (6): 3143-3156. 10.1109/tcyb.2019.2962865 |
15 | GUPTA A, ONG Y S, DA B S, et al. Landscape synergy in evolutionary multitasking [C]// Proceedings of the 2016 IEEE Congress on Evolutionary Computation. Piscataway: IEEE, 2016: 3076-3083. 10.1109/cec.2016.7744178 |
16 | DA B S, ONG Y S, FENG L, et al. Evolutionary multitasking for single-objective continuous optimization: benchmark problems, performance metric and baseline results[EB/OL]. (2017-06-12) [2022-04-23]. . 10.1109/cec.2016.7743992 |
17 | ZHOU L, FENG L, ZHONG J H, et al. A study of similarity measure between tasks for multifactorial evolutionary algorithm [C]// Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2018: 229-230. 10.1145/3205651.3205736 |
18 | SHEN F, LIU J, WU K. Evolutionary multitasking network reconstruction from time series with online parameter estimation[J]. Knowledge-Based Systems, 2021, 222: No.107019. 10.1016/j.knosys.2021.107019 |
19 | THANG T B, DAO T C, LONG N H, et al. Parameter adaptation in multifactorial evolutionary algorithm for many-task optimization[J]. Memetic Computing, 2021, 13 (4): 433-446. 10.1007/s12293-021-00347-4 |
20 | CAI Y Q, PENG D M, LIU P Z, et al. Evolutionary multi-task optimization with hybrid knowledge transfer strategy[J]. Information Sciences, 2021, 580: 874-896. 10.1016/j.ins.2021.09.021 |
21 | 程美英, 钱乾, 倪志伟, 等. 信息筛选多任务优化自组织迁移算法[J]. 计算机应用, 2021, 41 (6): 1748-1755. 10.11772/j.issn.1001-9081.2020091390 |
CHENG M Y, QIAN Q, NI Z W, et al. Self-organized migrating algorithm for multi-task optimization with information filter[J]. Journal of Computer Applications, 2021, 41 (6): 1748-1755. 10.11772/j.issn.1001-9081.2020091390 | |
22 | 程美英, 钱乾, 倪志伟, 等. 基于虚拟多任务二元粒子群算法和分形维数的雾霾天气预测方法[J]. 系统科学与数学, 2018, 38 (5): 623-637. 10.12341/jssms13406 |
CHENG M Y, QIAN Q, NI Z W, et al. Virtual multitasking binary particle swarm optimization and fractal dimension for haze forecast[J]. Journal of Systems Science and Mathematical Science, 2018, 38 (5): 623-637. 10.12341/jssms13406 | |
23 | GUPTA A, ONG Y S, FENG L, et al. Insights on transfer optimization: because experience is the best teacher[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2 (1): 51-64. 10.1109/tetci.2017.2769104 |
24 | CHENG M Y, GUPTA A, ONG Y S, et al. Coevolutionary multitasking for concurrent global optimization: with case studies in complex engineering design[J]. Engineering Applications of Artificial Intelligence, 2017, 64: 13-24. 10.1016/j.engappai.2017.05.008 |
25 | TAN K C, FENG L, JINAG M. Evolutionary transfer optimization — a new frontier in evolutionary computation research[J]. IEEE Computational Intelligence Magazine, 2021, 16 (1): 22-33. 10.1109/mci.2020.3039066 |
26 | 程美英, 钱乾, 倪志伟, 等. 信息交互多任务粒子群算法[J]. 模式识别与人工智能, 2019, 32 (5): 385-397. 10.16451/j.cnki.issn1003-6059.201905001 |
CHENG M Y, QIAN Q, NI Z W, et al. Information exchange particle swarm optimization for multitasking[J]. Pattern Recognition and Artificial Intelligence, 2019, 32 (5): 385-397. 10.16451/j.cnki.issn1003-6059.201905001 | |
27 | 程美英, 熊伟清, 严彬, 等. 求解多维0/1背包问题的二元粒子群算法[J]. 系统仿真学报, 2009, 21 (18): 5735- 5743. |
CHENG M Y, XIONG W Q, YAN B, et al. Binary PSO algorithm for multiple 0/1 knapsack problem[J]. Journal of System Simulation, 2009, 21 (18): 5735- 5743. | |
28 | CLARK R E. Antagonism between achievement and enjoyment in ATI studies[J]. Educational Psychologist, 1982, 17 (2): 92- 101. 10.1080/00461528209529247 |
29 | JAMES W B, GARDNER D L. Learning styles: implications for distance learning[J]. New Directions for Adult and Continuing Education, 1995, 1995 (67): 19-31. 10.1002/ace.36719956705 |
[1] | CHENG Meiying, QIAN Qian, NI Zhiwei, ZHU Xuhui. Self-organized migrating algorithm for multi-task optimization with information filtering [J]. Journal of Computer Applications, 2021, 41(6): 1748-1755. |
[2] | JIA Heming, LI Yao, JIANG Zichao, SUN Kangjian. Multi-threshold segmentation of forest fire images based on modified symbiotic organisms search algorithm [J]. Journal of Computer Applications, 2021, 41(5): 1465-1470. |
[3] | LI Kunlun, GUAN Liwei, GUO Changlong. Cloud task scheduling strategy based on clustering and improved symbiotic organisms search algorithm [J]. Journal of Computer Applications, 2018, 38(3): 707-714. |
[4] | . Optimization method for irregular LDPC codes in AWGN channel [J]. Journal of Computer Applications, 2010, 30(2): 292-294. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||