Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization
Tóm tắt
Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problem (MO-MTOP) using evolutionary computation. However, most existing methods tend to directly treat the multiple multi-objective tasks as different problems and optimize them by different populations, which face the difficulty in designing good knowledge transferring strategy among the tasks/populations. Different from existing methods that suffer from the difficult knowledge transfer, this paper proposes to treat the MO-MTOP as a multi-objective multi-criteria optimization problem (MO-MCOP), so that the knowledge of all the tasks can be inherited in a same population to be fully utilized for solving the MO-MTOP more efficiently. To be specific, the fitness evaluation function of each task in the MO-MTOP is treated as an evaluation criterion in the corresponding MO-MCOP, and therefore, the MO-MCOP has multiple relevant evaluation criteria to help the individual selection and evolution in different evolutionary stages. Furthermore, a probability-based criterion selection strategy and an adaptive parameter learning method are also proposed to better select the fitness evaluation function as the criterion. By doing so, the algorithm can use suitable evaluation criteria from different tasks at different evolutionary stages to guide the individual selection and population evolution, so as to find out the Pareto optimal solutions of all tasks. By integrating the above, this paper develops a multi-objective multi-criteria evolutionary algorithm framework for solving MO-MTOP. To investigate the proposed algorithm, extensive experiments are conducted on widely used MO-MTOPs to compare with some state-of-the-art and well-performing algorithms, which have verified the great effectiveness and efficiency of the proposed algorithm. Therefore, treating MO-MTOP as MO-MCOP is a potential and promising direction for solving MO-MTOP.
Tài liệu tham khảo
Tan KC, Feng L, Jiang M (2021) Evolutionary transfer optimization—a new frontier in evolutionary computation research. IEEE Comput Intell Mag 16(1):22–33. https://doi.org/10.1109/MCI.2020.3039066
Ong YS, Gupta A (2019) AIR5: five pillars of artificial intelligence research. IEEE Trans Emerg Top Comput Intell 3(5):411–415. https://doi.org/10.1109/TETCI.2019.2928344
Gupta A, Ong YS, Feng L, Tan KC (2017) Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans Cybern 47(7):1652–1665. https://doi.org/10.1109/TCYB.2016.2554622
Gupta A, Ong YS, Feng L (2016) Multifactorial evolution: toward evolutionary multitasking. IEEE Trans Evol Comput 20(3):343–357. https://doi.org/10.1109/TEVC.2015.2458037
Gupta A, Ong YS, Feng L (2017) Insights on transfer optimization: because experience is the best teacher. IEEE Trans Emerg Top Comput Intell 2(1):51–64. https://doi.org/10.1109/TETCI.2017.2769104
Ong YS, Gupta A (2016) Evolutionary multitasking: a computer science view of cognitive multitasking. Cogn Comput 8(2):125–142. https://doi.org/10.1007/s12559-016-9395-7
Li G, Lin Q, Gao W (2020) Multifactorial optimization via explicit multipopulation evolutionary framework. Inf Sci 512:1555–1570. https://doi.org/10.1016/j.ins.2019.10.066
Feng L, Zhou L, Gupta A, Zhong J, Qin K (2019) Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking. IEEE Trans Cybern 51(6):3171–3184. https://doi.org/10.1109/TCYB.2019.2955599
Feng L et al (2020) Towards faster vehicle routing by transferring knowledge from customer representation. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.3018903
Wang H, Feng L, Jin Y, Doherty J (2020) Surrogate-assisted evolutionary multitasking for expensive minimax optimization in multiple scenarios. IEEE Comput Intell Mag 16(1):34–48. https://doi.org/10.1109/MCI.2020.3039067
Li JY, Zhan ZH, Zhang J (2022) Evolutionary computation for expensive optimization: a survey. Mach Intell Res 19(1):3–23. https://doi.org/10.1007/s11633-022-1317-4
Cheng MY, Gupta A, Ong YS, Ni ZW (2017) Coevolutionary multitasking for concurrent global optimization: With case studies in complex engineering design. Eng Appl Artif Intell 64:13–24. https://doi.org/10.1016/j.engappai.2017.05.008
Bali KK, Gupta A, Ong YS, Tan PS (2021) Cognizant multitasking in multiobjective multifactorial evolution: MO-MFEA-II. IEEE Trans Cybern 51(4):1784–1796. https://doi.org/10.1109/TCYB.2020.2981733
Zhan ZH, Shi L, Tan KC, Zhang J (2021) A survey on evolutionary computation for complex continuous optimization. Artif Intell Rev. https://doi.org/10.1007/s10462-021-10042-y
Zhan ZH et al (2021) Matrix-based evolutionary computation. IEEE Trans Emerg Top Comput Intell. https://doi.org/10.1109/TETCI.2020.3047410
Li JY, Zhan ZH, Tan KC, Zhang J (2021) A meta-knowledge transfer-based differential evolution for multitask optimization. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2021.3131236
Li JY, Zhan ZH, Xu J, Kwong S, Zhang J (2021) Surrogate-assisted hybrid-model estimation of distribution algorithm for mixed-variable hyperparameters optimization in convolutional neural networks. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3106399
Yi J, Bai J, He H, Zhou W, Yao L (2020) A multifactorial evolutionary algorithm for multitasking under interval uncertainties. IEEE Trans Evol Comput 24(5):908–922. https://doi.org/10.1109/TEVC.2020.2975381
Li JY, Zhan ZH, Wang H, Zhang J (2021) Data-driven evolutionary algorithm with perturbation-based ensemble surrogates. IEEE Trans Cybern 51(8):3925–3937. https://doi.org/10.1109/tcyb.2020.3008280
Li JY, Zhan ZH, Wang C, Jin H, Zhang J (2020) Boosting data-driven evolutionary algorithm with localized data generation. IEEE Trans Evol Comput 24(5):923–937. https://doi.org/10.1109/TEVC.2020.2979740
Lin Q et al (2018) Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems. IEEE Trans Evol Comput 22(1):32–46. https://doi.org/10.1109/TEVC.2016.2631279
Zhang X, Du KJ, Zhan ZH, Kwong S, Gu TL, Zhang J (2020) Cooperative co-evolutionary bare-bones particle swarm optimization with function independent decomposition for large-scale supply chain network design with uncertainties. IEEE Trans Cybern 50(10):4454–4468. https://doi.org/10.1109/TCYB.2019.2937565
Li JY, Zhan ZH, Liu RD, Wang C, Kwong S, Zhang J (2021) Generation-level parallelism for evolutionary computation: a pipeline-based parallel particle swarm optimization. IEEE Trans Cybern 51(10):4848–4859
Sun J, Liu X, Bäck T, Xu Z (2021) Learning adaptive differential evolution algorithm from optimization experiences by policy gradient. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2021.3060811
Zhan ZH, Wang ZJ, Jin H, Zhang J (2020) Adaptive distributed differential evolution. IEEE Trans Cybern 50(11):4633–4647. https://doi.org/10.1109/TCYB.2019.2944873
Liu XF, Zhan ZH, Zhang J (2021) Resource-aware distributed differential evolution for training expensive neural-network-based controller in power electronic circuit. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3075205
Yang M, Zhou A, Li C, Yao X (2021) An efficient recursive differential grouping for large-scale continuous problems. IEEE Trans Evol Comput 25(1):159–171. https://doi.org/10.1109/TEVC.2020.3009390
Wang F, Li Y, Zhou A, Tang K (2019) An estimation of distribution algorithm for mixed-variable newsvendor problems. IEEE Trans Evol Comput 24(3):479–493. https://doi.org/10.1109/TEVC.2019.2932624
Chen ZG, Lin Y, Gong YJ et al (2021) Maximizing lifetime of range-adjustable wireless sensor networks: a neighborhood-based estimation of distribution algorithm. IEEE Trans Cybern 51:5433–5444. https://doi.org/10.1109/TCYB.2020.2977858
Sun Y, Yen GG, Yi Z (2018) Improved regularity model-based eda for many-objective optimization. IEEE Trans Evol Comput 22(5):662–678. https://doi.org/10.1109/TEVC.2018.2794319
Zhang X, Zhan ZH, Fang W, Qian P, Zhang J (2021) Multi population ant colony system with knowledge based local searches for multiobjective supply chain configuration. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2021.3097339
Zhou S-Z, Zhan Z-H, Chen Z-G et al (2020) A multi-objective ant colony system algorithm for airline crew rostering problem with fairness and satisfaction. IEEE Trans Intell Transp Syst 22:6784–6798. https://doi.org/10.1109/tits.2020.2994779
Liang D, Zhan ZH, Zhang Y, Zhang J (2020) An efficient ant colony system approach for new energy vehicle dispatch problem. IEEE Trans Intell Transp Syst 21(11):4784–4797. https://doi.org/10.1109/TITS.2019.2946711
Zhou L, Feng L, Gupta A, Ong YS (2021) Learnable evolutionary search across heterogeneous problems via kernelized autoencoding. IEEE Trans Evol Comput 25(3):567–581. https://doi.org/10.1109/TEVC.2021.3056514
Feng L, Zhou W, Liu W, Ong YS, Tan KC (2020) Solving dynamic multiobjective problem via autoencoding evolutionary search. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3017017
Zheng Y, Zhu Z, Qi Y, Wang L, Ma X (2020) Multi-objective multifactorial evolutionary algorithm enhanced with the weighting helper-task. In: 2nd Int. Conf. Ind. Artif. Intell. IAI. https://doi.org/10.1109/IAI50351.2020.9262200.
Yang C, Ding J, Tan KC, Jin Y (2017) Two-stage assortative mating for multi-objective multifactorial evolutionary optimization. In: 2017 IEEE 56th annu. conf. decis. control.https://doi.org/10.1109/CDC.2017.8263646
Binh HTT, Tuan NQ, Long DCT (2019) A multi-objective multi-factorial evolutionary algorithm with reference-point-based approach. In: Proc. IEEE Congr. Evol. Comput. https://doi.org/10.1109/CEC.2019.8790034
Lin J, Liu HL, Xue B, Zhang M, Gu F (2019) Multiobjective multi-tasking optimization based on incremental learning. IEEE Trans Evol Comput 24(5):824–838. https://doi.org/10.1109/TEVC.2019.2962747
Liang Z, Zhang J, Feng L, Zhu Z (2019) A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2019.07.015
Feng L, Zhou L, Zhong JH et al (2019) Evolutionary multitasking via explicit autoencoding. IEEE Trans Cybern 49(9):3457–3470. https://doi.org/10.1109/TCYB.2018.2845361
Li JY, Du KJ, Zhan ZH, Wang H, Zhang J (2021) Multi-criteria differential evolution: treating multitask optimization as multi-criteria optimization. In: Proc. genet. evol. comput. conf. https://doi.org/10.1145/3449726.3459456.
Zhan ZH, Li J, Cao J, Zhang J, Chung HS, Shi Y (2013) Multiple populations for multiple objectives: a coevolutionary technique for solving multiobjective optimization problems. IEEE Trans Cybern 43(2):445–463. https://doi.org/10.1109/TSMCB.2012.2209115
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017
Wang H, Jin Y, Doherty J (2018) A generic test suite for evolutionary multifidelity optimization. IEEE Trans Evol Comput 22(6):836–850. https://doi.org/10.1109/TEVC.2017.2758360
Wu SH, Zhan ZH, Zhang J (2021) SAFE: scale-adaptive fitness evaluation method for expensive optimization problems. IEEE Trans Evol Comput 25(3):478–491. https://doi.org/10.1109/TEVC.2021.3051608
Zhou ZH, Yang Y, Chao Q (2019) Evolutionary learning: advances in theories and algorithms. Springer, Singapore
Huang H, Su J, Zhang Y, Hao Z (2020) An experimental method to estimate running time of evolutionary algorithms for continuous optimization. IEEE Trans Evol Comput 24(2):275–289. https://doi.org/10.1109/TEVC.2019.2921547
Lin Q, Lin W, Zhu Z, Gong M, Li J, Coello CAC (2020) Multimodal multiobjective evolutionary optimization with dual clustering in decision and objective spaces. IEEE Trans Evol Comput 25(1):130–144. https://doi.org/10.1109/TEVC.2020.3008822
Liu S, Lin Q, Tan KC, Gong M, Coello CAC (2020) A fuzzy decomposition-based multi/many-objective evolutionary algorithm. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3008697
Liu S, Lin Q, Wong KC et al (2020) A self-guided reference vector strategy for many-objective optimization. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.2971638
Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271
Yuan Y, Ong YS, Feng L et al (2017) Evolutionary multitasking for multi-objective continuous optimization: benchmark problems, performance metrics and baseline results. In: Technical Report. DIALOG. https://arxiv.org/abs/1706.02766
Jin Y, Wang H, Sun C (2021) Data-driven evolutionary optimization. Springer, Cham
Tian J, Tan Y, Zeng J, Sun C, Jin Y (2019) Multiobjective infill criterion driven gaussian process-assisted particle swarm optimization of high-dimensional expensive problems. IEEE Trans Evol Comput 23(3):459–472. https://doi.org/10.1109/TEVC.2018.2869247
Lin Q, Wu X, Ma L, Li J, Gong M, Coello CAC (2021) An ensemble surrogate-based framework for expensive multiobjective evolutionary optimization. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2021.3103936
Song Z, Wang H, He C, Jin Y (2021) A Kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2021.3073648
Wang H, Feng L, Jin Y, Doherty J (2021) Surrogate-assisted evolutionary multitasking for expensive minimax optimization in multiple scenarios. IEEE Comput Intell Mag 16(1):34–48. https://doi.org/10.1109/MCI.2020.3039067
Lin Q, Lin W, Zhu Z, Gong M, Li J, Coello CAC (2021) Multimodal multiobjective evolutionary optimization with dual clustering in decision and objective spaces. IEEE Trans Evol Comput 25(1):130–144. https://doi.org/10.1109/TEVC.2020.3008822
Liu S, Lin Q, Tian Y, Tan KC (2021) A variable importance-based differential evolution for large-scale multiobjective optimization. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3098186
Wang ZJ, Zhan ZH, Kwong S, Jin H, Zhang J (2021) Adaptive granularity learning distributed particle swarm optimization for large-scale optimization. IEEE Trans Cybern 51(3):1175–1188. https://doi.org/10.1109/TCYB.2020.2977956
Liu SC, Zhan ZH, Tan KC, Zhang J (2021) A multiobjective framework for many-objective optimization. IEEE Trans Cybern. https://doi.org/10.1109/tcyb.2021.3082200
Liu XF, Zhan ZH, Gao Y, Zhang J, Kwong S, Zhang J (2019) Coevolutionary particle swarm optimization with bottleneck objective learning strategy for many-objective optimization. IEEE Trans Evol Comput 23(4):587–602. https://doi.org/10.1109/TEVC.2018.2875430