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An evolutionary multitasking optimization algorithm via reference-point based nondominated sorting approach

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Abstract

Multiobjective multifactorial evolutionary algorithm (MOMFEA), which solves multiple tasks simultaneously based on a single population, has received considerable attention in recent decades. However, the negative transmission usually leads to slower convergence or worse distribution. To make use of the potential similarity between different tasks, this paper proposes an enhanced version for the MOMFEA using a reference-point based nondominated sorting approach (denoted as MFEA-RP). By using Multiple Dimensional Scaling, subtasks in different dimensions can be optimized simultaneously with a single set of reference points. The efficiency of the method is substantiated by multiobjective benchmark problems and practical instances. In most of the test probability, MFEA-RP converges faster to the true Pareto front. Better-distributed solutions are successfully found, which indicates the better representativeness to the solution space.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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References

  1. Gupta A, Mańdziuk J, Yew-Soon O (2015) Evolutionary multitasking in bi-level optimization. Complex Intell Syst 1:1–4. https://doi.org/10.1007/s40747-016-0011-y

    Article  CAS  Google Scholar 

  2. Abhishek G, Yew-Soon O, Liang F (2016) Multifactorial evolution: toward evolutionary multitasking. IEEE Trans Evol Comput 20:343–357. https://doi.org/10.1109/TEVC.2015.2458037

    Article  Google Scholar 

  3. Abhishek G, Liang OYSF, Chen TK (2017) Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans Cybern 47:1652–1665. https://doi.org/10.1109/TCYB.2016.2554622

    Article  Google Scholar 

  4. Abhishek G, Yew-Soon O, Liang F et al (2017) Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans Cybern 47:1652–1665. https://doi.org/10.1109/TCYB.2016.2554622

    Article  Google Scholar 

  5. Al-Rahlawee ATH, Rahebi J (2021) Multilevel thresholding of images with improved OTSU thresholding by black widow optimization algorithm. Expert Syst Appl 80(28):217–243. https://doi.org/10.1007/s11042-021-10860-w

    Article  Google Scholar 

  6. Arora S, Anand P (2018) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31:4385–4405. https://doi.org/10.1007/s00521-018-3343-2

    Article  Google Scholar 

  7. Bai L, Lin W, Gupta A et al (2021) From multitask gradient descent to gradient-free evolutionary multitasking: a proof of faster convergence. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3052509

    Article  PubMed  Google Scholar 

  8. Bozorgchenani A, Mashhadi F, Tarchi D et al (2020) Multi-objective computation sharing in energy and delay constrained mobile edge computing environments. IEEE Trans Mob Comput 20:2992–3005. https://doi.org/10.1109/TMC.2020.2994232

    Article  Google Scholar 

  9. Chen K, Xue B, Zhang M et al (2020) An evolutionary multitasking-based feature selection method for high-dimensional classification. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3042243

    Article  PubMed  Google Scholar 

  10. Chen K, Xue B, Zhang M et al (2021) Evolutionary multitasking for feature selection in high-dimensional classification via particle swarm optimisation. IEEE Trans Evolut Comput. https://doi.org/10.1109/TEVC.2021.3100056

    Article  Google Scholar 

  11. Chen Y, Zhong J, Tan M (2018) A fast memetic multi-objective differential evolution for multitasking optimization. In: 2018 IEEE congress on evolutionary computation (CEC) pp 1–8. https://doi.org/10.1109/CEC.2018.8477722

  12. Chen Y, Zhong J, Feng L et al (2019) An adaptive archive-based evolutionary framework for many-task optimization. IEEE Trans Emerg Top Comput Intell 4:369–384. https://doi.org/10.1109/TETCI.2019.2916051

    Article  Google Scholar 

  13. Cox T, Cox M (2001) Multidimensional scaling. Chapman and Hall, London

    Google Scholar 

  14. Deb K, Jayavelmurugan S (2006) Reference point based multi-objective optimization using evolutionary algorithms. Int J Comput Intell Res 2:635–642. https://doi.org/10.5019/j.ijcir.2006.67

    Article  MathSciNet  Google Scholar 

  15. Ding J, Yang C, Jin Y et al (2019) Generalized multitasking for evolutionary optimization of expensive problems. IEEE Trans Evol Comput 23:44–58. https://doi.org/10.1109/TEVC.2017.2785351

    Article  Google Scholar 

  16. Gupta A, Ong YS, Feng L (2016) Multifactorial evolution: toward evolutionary multitasking. IEEE Trans Evol Comput 20:343–357. https://doi.org/10.1109/TEVC.2015.2458037

    Article  Google Scholar 

  17. Hayyolalam V, Kazem AAP (2020) Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Expert Syst Appl 87:103249. https://doi.org/10.1016/j.engappai.2019.103249

    Article  Google Scholar 

  18. Heidari AA, Mirjalili S, Faris H et al (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  19. Houssein EH, Helmy BED, Elngar DOAA (2021) A novel black widow optimization algorithm for multilevel thresholding image segmentation. Expert Syst Appl 167(114):159. https://doi.org/10.1016/j.eswa.2020.114159

    Article  Google Scholar 

  20. Huo Z, Liu S, Ebrahimian H (2022) Aircraft energy management system using chaos red fox optimization algorithm. J Electr Eng Technol 17:179–195. https://doi.org/10.1007/s42835-021-00884-5

    Article  Google Scholar 

  21. Das I, Dennis JE (1998) Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems. Siam J Optim 8:631–657. https://doi.org/10.1137/S1052623496307510

    Article  MathSciNet  Google Scholar 

  22. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evolut Comput 18:577–601. https://doi.org/10.1109/TEVC.2013.2281535

    Article  Google Scholar 

  23. Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Trans Evolut Comput 6:182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  24. Kou YN, Zheng JH, Li MS, et al (2016) Reference point based non-dominated sorting approach for multi-objective optimization of power flow. In: 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA) https://doi.org/10.1109/ISGT-Asia.2015.7386970

  25. Kumar BK, Abhishek G, Yew-Son O et al (2020) Cognizant multitasking in multiobjective multifactorial evolution: Mo-mfea-II. IEEE Trans Cybern 51:1–13. https://doi.org/10.1109/TCYB.2020.2981733

    Article  Google Scholar 

  26. Kumar BK, Yew-Soon O, Abhishek G et al (2020) Multifactorial evolutionary algorithm with online transfer parameter estimation: Mfea-II. IEEE Trans Evol Comput 24:69–83. https://doi.org/10.1109/TEVC.2019.2906927

    Article  Google Scholar 

  27. Lei Z, Liang F, Jinghui Z et al (2016) Evolutionary multitasking in combinatorial search spaces: a case study in capacitated vehicle routing problem. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI) pp 1–8. https://doi.org/10.1109/SSCI.2016.7850039

  28. Liang B, Yutao Q, Mengqing S et al (2018) An evolutionary multitasking algorithm for cloud computing service composition. In: 14th World Congress on Services (SERVICES) held as Part of the Services Conference Federation (SCF) pp 130–144. https://doi.org/10.1007/978-3-319-94472-2_10

  29. Liang F, Lei Z, Jinghui Z et al (2019) Evolutionary multitasking via explicit autoencoding. IEEE Trans Cybern 49:3457–3470. https://doi.org/10.1109/TCYB.2018.2845361

    Article  Google Scholar 

  30. Liaw R, Ting C (2017) Evolutionary many-tasking based on biocoenosis through symbiosis: a framework and benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation (CEC) pp 2266–2273. https://doi.org/10.1109/CEC.2017.7969579

  31. Maoguo G, Zedong T, Hao L et al (2019) Evolutionary multitasking with dynamic resource allocating strategy. IEEE Trans Evol Comput 23:858–869. https://doi.org/10.1109/TEVC.2019.2893614

    Article  Google Scholar 

  32. Muhammad I, Brownie WN, Mengjie Z (2014) Reusing building blocks of extracted knowledge to solve complex, large-scale Boolean problems. IEEE Trans Evolut Comput 18:465–480. https://doi.org/10.1109/TEVC.2013.2281537

    Article  Google Scholar 

  33. Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evolut Comput 2:221–248. https://doi.org/10.1162/evco.1994.2.3.221

    Article  Google Scholar 

  34. Ong G (2016) Evolutionary multitasking: a computer science view of cognitive multitasking. Cogn Comput 8:125–142. https://doi.org/10.1007/s12559-016-9395-7

    Article  Google Scholar 

  35. Osaba E, Ser JD, Martinez AD (2021) AT-MFCGA: an adaptive transfer-guided multifactorial cellular genetic algorithm for evolutionary multitasking. Inform Sci. https://doi.org/10.1016/j.ins.2021.05.005

    Article  MathSciNet  Google Scholar 

  36. Połap D, Woźniak M (2021) Red fox optimization algorithm. Expert Syst Appl 166:114107. https://doi.org/10.1016/j.eswa.2020.114107

    Article  Google Scholar 

  37. Qingfu Z, Hui L (2007) Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11:712–731. https://doi.org/10.1109/TEVC.2007.892759

    Article  Google Scholar 

  38. Shuangshuang Y, Zhiming D, Xianpeng W et al (2020) A multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy. Inf Sci 511:18–35. https://doi.org/10.1016/j.ins.2019.09.058

    Article  MathSciNet  Google Scholar 

  39. Xu Z, Liu X, Zhang K et al (2021) Cultural transmission based multi-objective evolution strategy for evolutionary multitasking. Inform Sci. https://doi.org/10.1016/j.ins.2021.09.007

    Article  PubMed  Google Scholar 

  40. Yew-Soon O, Abhishek G (2016) Evolutionary multitasking: a computer science view of cognitive multitasking. Cogn Comput 8:125–142. https://doi.org/10.1007/s12559-016-9395-7

    Article  Google Scholar 

  41. Zervoudakis K, Tsafarakis S (2020) A mayfy optimization algorithm. Comput Ind Eng 145:106559. https://doi.org/10.1016/j.cie.2020.106559

    Article  Google Scholar 

  42. Zheng X, Qin A, Gong M et al (2019) Self-regulated evolutionary multitask optimization. IEEE Trans Evol Comput 24:16–28. https://doi.org/10.1109/TEVC.2019.2904696

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant nos. 61972456, 62172298); Natural Science Foundation of Tianjin (No. 20JCYBJC00140); Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education, P.R.China (KFKT-2020101).

Funding

This work was supported by the National Key R&D Program of China (2019YFB1706302); the National Natural Science Foundation of China (Grant nos. 61972456, 62172298); Natural Science Foundation of Tianjin (No. 20JCYBJC00140); Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education, P.R.China (KFKT-2020101).

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  1. School of Computer Science and Technology, Tiangong University, No.399 BinShuiXi Road, Tianjin, 300387, Tianjin, China

    YuQi Zheng & ZhengYi Chai

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Zheng, Y., Chai, Z. An evolutionary multitasking optimization algorithm via reference-point based nondominated sorting approach. Evol. Intel. 17, 1095–1109 (2024). https://doi.org/10.1007/s12065-022-00788-x

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