IEEE Computational Intelligence Magazine - May 2022 - 66

[78] C. Wang, J. Liu, K. Wu, and Z. Wu, " Solving multi-task optimization problems with
adaptive knowledge transfer via anomaly detection, " IEEE Trans. Evol. Comput., 2021,
doi: 10.1109/TEVC.2021.3068157.
[79] T. Yu et al., " Meta-world: A benchmark and evaluation for multi-task and meta reinforcement
learning, " in Proc. Conf. Robot Learning., 2020, pp. 1094-1100.
[80] J. Rubio-Hervas, A. Gupta, and Y.-S. Ong, " Data-driven risk assessment and multicriteria
optimization of UAV operations, " Aerosp. Sci. Technol., vol. 77, pp. 510-523, 2018,
doi: 10.1016/j.ast.2018.04.001.
[81] Y.-S. Ong and A. Gupta, " Evolutionary multitasking: A computer science view of
cognitive multitasking, " Cogn. Comput., vol. 8, no. 2, pp. 125-142, 2016, doi: 10.1007/
s12559-016-9395-7.
[82] Y. Zhou, T. Wang, and X. Peng, " MFEA-IG: A multi-task algorithm for mobile
agents path planning, " in Proc. IEEE Congr. Evol. Comput. (CEC), 2020, pp. 1-7, doi:
10.1109/CEC48606.2020.9185906.
[83] J. Yi, J. Bai, H. He, W. Zhou, and L. Yao, " A multifactorial evolutionary algorithm
for multitasking under interval uncertainties, " IEEE Trans. Evol. Comput., 2020, doi:
10.1109/TEVC.2020.2975381.
[84] K. K. Bali, A. Gupta, Y.-S. Ong, and P. S. Tan, " Cognizant multitasking in multiobjective
multifactorial evolution: MO-MFEA-II, " IEEE Trans. Cybern., 2020, doi:
10.1109/TCYB.2020.2981733.
[85] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, " A fast and elitist multiobjective
genetic algorithm: NSGA-II, " IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197,
2002, doi: 10.1109/4235.996017.
[86] L. While, P. Hingston, L. Barone, and S. Huband, " A faster algorithm for calculating hypervolume, "
IEEE Trans. Evol. Comput., vol. 10, no. 1, pp. 29-38, 2006, doi: 10.1109/TEVC.2005.
851275.
[87] W. Dai, Z. Wang, and K. Xue, " System-in-package design using multi-task memetic
learning and optimization, " Memetic Comput., 2021, doi: 10.1007/s12293-02100346-5.
[88]
X. Xue et al., " Affine transformation-enhanced multifactorial optimization for heterogeneous
problems, " IEEE Trans. Cybern., 2020, doi: 10.1109/TCYB.2020.3036393.
[89] J. Liang et al., " Evolutionary multi-task optimization for parameters extraction of
photovoltaic models, " Energy Convers. Manag., vol. 207, p. 112,509, 2020.
[90] J. Liu, P. Li, G. Wang, Y. Zha, J. Peng, and G. Xu, " A multitasking electric power
dispatch approach with multi-objective multifactorial optimization algorithm, " IEEE Access,
vol. 8, pp. 155,902-155,911, 2020, doi: 10.1109/ACCESS.2020.3018484.
[91] D. Wu and X. Tan, " Multitasking genetic algorithm (MTGA) for fuzzy system optimization, "
IEEE Trans. Fuzzy Syst., vol. 28, no. 6, pp. 1050-1061, 2020, doi: 10.1109/
TFUZZ.2020.2968863.
[92] G. Avigad and A. Moshaiov, " Interactive evolutionary multiobjective search and
optimization of set-based concepts, " IEEE Trans. Syst., Man, Cybern., B (Cybern.), vol. 39,
no. 4, pp. 1013-1027, 2009, doi: 10.1109/TSMCB.2008.2011565.
[93] G. Yokoya, H. Xiao, and T. Hatanaka, " Multifactorial optimization using artificial
bee colony and its application to car structure design optimization, " in Proc. IEEE Congr.
Evol. Comput. (CEC), 2019, pp. 3404-3409, doi: 10.1109/CEC.2019.8789940.
[94] H. Xiao, G. Yokoya, and T. Hatanaka, " Multifactorial PSO-FA hybrid algorithm for
multiple car design benchmark, " in Proc. IEEE Int. Conf. Syst., Man Cybern. (SMC), 2019,
pp. 1926-1931, doi: 10.1109/SMC.2019.8914649.
[95] T. Kohira, H. Kemmotsu, O. Akira, and T. Tatsukawa, " Proposal of benchmark
problem based on real-world car structure design optimization, " in Proc. Genetic Evol.
Comput. Conf. Companion, 2018, pp. 183-184, doi: 10.1145/3205651.3205702.
[96] Z. Wang and X. Wang, " Multiobjective multifactorial operation optimization for
continuous annealing production process, " Ind. Eng. Chem. Res., vol. 58, pp. 19,166-
19,178, 2019, doi: 10.1021/acs.iecr.9b03399.
[97] C. Yang, J. Ding, Y. Jin, C. Wang, and T. Chai, " Multitasking multiobjective evolutionary
operational indices optimization of beneficiation processes, " IEEE Trans. Automat.
Sci. Eng., vol. 16, no. 3, pp. 1046-1057, 2018, doi: 10.1109/TASE.2018.2865593.
[98] J. W. E. Tay, H. Y. Ng, and P. S. Tan, " Model factory@ simtech-sense and response
manufacturing for industry 4.0, " in Implementing Industry 4.0, 2021, p. 399.
[99] S. Jiang et al., " Complex and intelligent systems in manufacturing, " IEEE Potentials,
vol. 35, no. 4, pp. 23-28, 2016, doi: 10.1109/MPOT.2016.2540079.
[100] N. T. Tam, V. T. Dat, P. N. Lan, H. T. T. Binh, A. Swami et al., " Multifactorial
evolutionary optimization to maximize lifetime of wireless sensor network, " Inf. Sci., vol.
576, pp. 355-373, 2021, doi: 10.1016/j.ins.2021.06.056.
[101] T. T. Huong et al., " A multi-task approach for maximum survival ratio problem in
large-scale wireless rechargeable sensor networks, " in Proc. IEEE Congr. Evol. Comput.
(CEC), 2021, pp. 1688-1695.
[102] J. Park, Y. Mei, S. Nguyen, G. Chen, and M. Zhang, " Evolutionary multitask optimisation
for dynamic job shop scheduling using niched genetic programming, " in Proc.
Australasian Joint Conf. Artif. Intell., 2018, pp. 739-751.
[103] F. Zhang, Y. Mei, S. Nguyen, M. Zhang, and K. C. Tan, " Surrogate-assisted evolutionary
multitask genetic programming for dynamic flexible job shop scheduling, " IEEE
Trans. Evol. Comput., 2021.
[104] Q. Shang, Y. Huang, Y. Wang, M. Li, and L. Feng, " Solving vehicle routing problem
by memetic search with evolutionary multitasking, " Memetic Comput., pp. 1-14, 2022,
doi: 10.1007/s12293-021-00352-7.
[105] E. Osaba, A. D. Martinez, J. L. Lobo, I. Laña, and J. Del Ser, " On the transferability
of knowledge among vehicle routing problems by using cellular evolutionary multitasking, "
in Proc. IEEE 23rd Int. Conf. Intell. Transp. Syst., 2020.
[106] A. Rauniyar, R. Nath, and P. K. Muhuri, " Multi-factorial evolutionary algorithm
based novel solution approach for multi-objective pollution-routing problem, " Comput.
Ind. Eng., vol. 130, pp. 757-771, 2019, doi: 10.1016/j.cie.2019.02.031.
[107] L. Feng et al., " Solving generalized vehicle routing problem with occasional drivers
via evolutionary multitasking, " IEEE Trans. Cybern., 2019.
[108] T. B. Thang, N. B. Long, N. V. Hoang, and H. T. T. Binh, " Adaptive knowledge
transfer in multifactorial evolutionary algorithm for the clustered minimum routing cost
problem, " Appl. Soft Comput., vol. 105, p. 107,253, 2021.
[109] T. P. Dinh, B. H. T. Thanh, T. T. Ba, and L. N. Binh, " Multifactorial evolutionary
algorithm for solving clustered tree problems: competition among Cayley codes, " Memetic
Comput., vol. 12, pp. 185-217, 2020, doi: 10.1007/s12293-020-00309-2.
[110] Y. Yuan, Y.-S. Ong, A. Gupta, P. S. Tan, and H. Xu, " Evolutionary multitasking
in permutation-based combinatorial optimization problems: Realization with TSP, QAP,
LOP, and JSP, " in Proc. IEEE Region 10 Conf. (TENCON), 2016, pp. 3157-3164.
[111] F. Zhang, Y. Mei, S. Nguyen, K. C. Tan, and M. Zhang, " Multitask genetic programming-based
generative hyperheuristics: A case study in dynamic scheduling, " IEEE
Trans. Cybern., 2021, doi: 10.1109/TCYB.2021.3065340.
[112] L. Feng et al., " Explicit evolutionary multitasking for combinatorial optimization:
A case study on capacitated vehicle routing problem, " IEEE Trans. Cybern., 2020, doi:
10.1109/TCYB.2019.2962865.
[113] M. Harman, S. A. Mansouri, and Y. Zhang, " Search-based software engineering:
Trends, techniques and applications, " ACM Comput. Surveys (CSUR), vol. 45, no. 1, pp.
1-61, 2012, doi: 10.1145/2379776.2379787.
[114] L. Bao et al., " An evolutionary multitasking algorithm for cloud computing service
composition, " in Proc. World Congr. Services, 2018, pp. 130-144.
[115] C. Wang, H. Ma, G. Chen, and S. Hartmann, " Evolutionary multitasking for semantic
web service composition, " in Proc. IEEE Congr. Evol. Comput. (CEC), 2019, pp.
2490-2497, doi: 10.1109/CEC.2019.8790085.
[116] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, " Numerical
recipes in C, " 1988.
[117] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, " Evolutionary many-objective optimization:
A short review, " in Proc. IEEE Congr. Evol. Comput. (IEEE World Congr. Comput.
Intell.), 2008, pp. 2419-2426.
[118] A. Gupta, Y.-S. Ong, M. Shakeri, X. Chi, and A. Z. NengSheng, " The blessing of
dimensionality in many-objective search: An inverse machine learning insight, " in Proc.
IEEE Int. Conf. Big Data (Big Data), 2019, pp. 3896-3902, doi: 10.1109/BigData47090.2019.
9005525.
[119] L. Chen, H. Wang, and W. Ma, " Two-stage multi-tasking transform framework for
large-scale many-objective optimization problems, " Complex Intell. Syst., vol. 7, no. 3, pp.
1499-1513, 2021, doi: 10.1007/s40747-021-00273-5.
[120] K. Qiao, K. Yu, B. Qu, J. Liang, H. Song, and C. Yue, " An evolutionary multitasking
optimization framework for constrained multi-objective optimization problems, "
IEEE Trans. Evol. Comput., 2022, doi: 10.1109/TEVC.2022.3145582.
[121] P. Liao, C. Sun, G. Zhang, and Y. Jin, " Multi-surrogate multi-tasking optimization
of expensive problems, " Knowl.-Based Syst., vol. 205, p. 106,262, 2020.
[122] J. Ding, C. Yang, Y. Jin, and T. Chai, " Generalized multitasking for evolutionary
optimization of expensive problems, " IEEE Trans. Evol. Comput., vol. 23, no. 1, pp.
44-58, 2017, doi: 10.1109/TEVC.2017.2785351.
[123] A. Sinha, P. Malo, and K. Deb, " A review on bilevel optimization: From classical
to evolutionary approaches and applications, " IEEE Trans. Evol. Comput., vol. 22, pp.
276-295, 2017, doi: 10.1109/TEVC.2017.2712906.
[124] A. Gupta, J. Man´dziuk, and Y.-S. Ong, " Evolutionary multitasking in bi-level optimization, "
Complex Intell. Syst., vol. 1, nos. 1-4, pp. 83-95, 2015, doi: 10.1007/s40747016-0011-y.
[125]
H. Wang, L. Feng, Y. Jin, and J. Doherty, " Surrogate-assisted evolutionary multitasking
for expensive minimax optimization in multiple scenarios, " IEEE Comput. Intell.
Mag., vol. 16, no. 1, pp. 34-48, 2021, doi: 10.1109/MCI.2020.3039067.
[126] E. Eisenstadt and A. Moshaiov, " Novel solution approach for multi-objective attackdefense
cyber games with unknown utilities of the opponent, " IEEE Trans. Emerg. Topics
Comput. Intell., vol. 1, no. 1, pp. 16-26, 2016, doi: 10.1109/TETCI.2016.2637410.
[127] A. Z· ychowski, A. Gupta, J. Man´dziuk, and Y. S. Ong, " Addressing expensive
multi-objective games with postponed preference articulation via memetic coevolution, "
Knowl.-Based Syst., vol. 154, pp. 17-31, 2018, doi: 10.1016/j.knosys.2018.
05.012.
[128] G. Fadel, I. Haque, V. Blouin, and M. Wiecek, " Multi-criteria multi-scenario approaches
in the design of vehicles, " in Proc. 6th World Congr. Struct. Multidiscipl. Optimizat.,
2005.
[129] M. M. Wiecek, V. Singh, and V. Blouin, " Multi-scenario multi-criteria optimization
in engineering design, " Clemson Univ. Sci. Dept of Mathematical Sci., Tech. Rep.,
2007.
[130] L. Sampath, A. Gupta, Y.-S. Ong, and H. Gooi, " Evolutionary multitasking to support
optimal power flow under rapid load variations, " Southern Power Syst. Technol. China,
vol. 11, no. 10, 2017.
[131] L. Luo, X. Wang, J. Ma, and Y.-S. Ong, " Grpavoid: Multigroup collision-avoidance
control and optimization for UAV swarm, " IEEE Trans. Cybern., 2021, doi: 10.1109/
TCYB.2021.3132044.
[132] C. Zhong et al., " Surrogate-reformulation-assisted multitasking knowledge transfer
for production optimization, " J. Petroleum Sci. Eng., vol. 208, p. 109,486, 2022.
[133] Y.-F. Ge, M. Orlowska, J. Cao, H. Wang, and Y. Zhang, " MDDE: Multitasking
distributed differential evolution for privacy-preserving database fragmentation, " VLDB
J., pp. 1-19, 2022.
[134] J. Shi, X. Zhang, X. Liu, Y. Lei, and G. Jeon, " Multicriteria semi-supervised hyperspectral
band selection based on evolutionary multitask optimization, " Knowledge-Based
Systems, p. 107934, 2022.
66 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2022
IEEE Computational Intelligence Magazine - May 2022

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - May 2022
