zbMATH — the first resource for mathematics

A multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy. (English) Zbl 1456.90152
Summary: Multiobjective multifactorial optimization (MO-MFO), i.e., multiple multiobjective tasks are simultaneously optimized by a single population, has received considerable attention in recent years. Traditional algorithms for the MO-MFO usually allocate equal computing resources to each task, however, this may not be reasonable due to the fact that different tasks usually have different degrees of difficulty. Motivated by the idea that the limited computing resources should be adaptively allocated to different tasks according to their difficulties, this paper proposes an algorithm for the MO-MFO based on decomposition and dynamic resource allocation strategy (denoted as MFEA/D-DRA). In the MFEA/D-DRA, each multiobjective optimization task is firstly decomposed into a series of single-objective subproblems. Thereafter, a single population is used to evolve all the single-objective subproblems. In the process of evolution, subproblems with fast evolution rate will have the opportunity to get more rewards, i.e., computing resources. The evolution rate is measured by a utility function and updated periodically. Moreover, different multiobjective optimization tasks can communicate with each other according to a random mating probability. Finally, a set of evenly distributed approximate Pareto optimal solutions is obtained for each multiobjective optimization task. The statistical analysis of experimental results illustrates the superiority of the proposed MFEA/D-DRA algorithm on a variety of benchmark MO-MFO problems.
90C29 Multi-objective and goal programming
Full Text: DOI
[3] Bali, K. K.; Ong, Y.; Gupta, A.; Tan, P. S., Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II, IEEE Trans. Evol. Comput. (2019)
[4] Basseur, M.; Zitzler, E., Handling uncertainty in indicator-based multiobjective optimization, Int. J. Comput. Intell.Res., 2, 3, 255-272 (2006)
[5] Cai, X.; Sun, H.; Fan, Z., A diversity indicator based on reference vectors for many-objective optimization, Inf. Sci., 430-431, 467-486 (2018)
[8] Chen, X.; Ong, Y.; Lim, M.; Tan, K. C., A multi-facet survey on memetic computation, IEEE Trans. Evol. Comput., 15, 5, 591-607 (2011)
[9] Chen, Y.; Zhong, J.; Feng, L.; Zhang, J., An adaptive archive-based evolutionary framework for many-task optimization, IEEE Trans. Emerging Top.Comput. Intell., 1-16 (2019)
[10] Chen, Y.; Zhong, J.; Tan, M., A fast memetic multi-objective differential evolution for multi-tasking optimization, 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8 (2018)
[11] Das, I.; Dennis, J. E., Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems, SIAM J. Optim., 8, 3, 631-657 (1998) · Zbl 0911.90287
[12] Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 2, 182-197 (2002)
[13] Deb, K.; Tiwari, S., Omni-optimizer: a generic evolutionary algorithm for single and multi-objective optimization, Eur. J. Oper. Res., 185, 3, 1062-1087 (2008) · Zbl 1146.90509
[14] Ding, J.; Yang, C.; Jin, Y.; Chai, T., Generalized multitasking for evolutionary optimization of expensive problems, IEEE Trans. Evol. Comput., 23, 1, 44-58 (2019)
[15] Durillo, J. J.; Nebro, A. J.; Luna, F.; Alba, E., On the effect of the steady-state selection scheme in multi-objective genetic algorithms, (Ehrgott, M.; Fonseca, C. M.; Gandibleux, X.; Hao, J.-K.; Sevaux, M., Evolutionary Multi-Criterion Optimization (2009), Springer Berlin Heidelberg: Springer Berlin Heidelberg Berlin, Heidelberg), 183-197
[16] Emmerich, M.; Beume, N.; Naujoks, B., An EMO algorithm using the hypervolume measure as selection criterion, Proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization. Proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, EMO’05, 62-76 (2005), Springer-Verlag: Springer-Verlag Berlin, Heidelberg · Zbl 1109.68595
[17] Feng, L.; Zhou, L.; Zhong, J.; Gupta, A.; Ong, Y.; Tan, K.; Qin, A. K., Evolutionary multitasking via explicit autoencoding, IEEE Trans. Cybern., 49, 9, 3457-3470 (2019)
[18] Gong, M.; Tang, Z.; Li, H.; Zhang, J., Evolutionary multitasking with dynamic resource allocating strategy, IEEE Trans. Evol. Comput. (2019)
[19] Gupta, A.; Mańdziuk, J.; Ong, Y.-S., Evolutionary multitasking in bi-level optimization, Complex Intell. Syst., 1, 1, 83-95 (2015)
[20] Gupta, A.; Ong, Y.; Feng, L., Multifactorial evolution: toward evolutionary multitasking, IEEE Trans. Evol. Comput., 20, 3, 343-357 (2016)
[21] Gupta, A.; Ong, Y.; Feng, L.; Tan, K. C., Multiobjective multifactorial optimization in evolutionary multitasking, IEEE Trans. Cybern., 47, 7, 1652-1665 (2017)
[22] Han, D.; Du, W.; Du, W.; Jin, Y.; Wu, C., An adaptive decomposition-based evolutionary algorithm for many-objective optimization, Inf. Sci., 491, 204-222 (2019)
[26] Liaw, R.; Ting, C., Evolutionary many-tasking based on biocoenosis through symbiosis: a framework and benchmark problems, 2017 IEEE Congress on Evolutionary Computation (CEC), 2266-2273 (2017)
[27] Liu, Z.-Z.; Wang, Y.; Huang, P.-Q., AnD: a many-objective evolutionary algorithm with angle-based selection and shift-based density estimation, Inf. Sci. (2018)
[28] Mashwani, W. K.; Salhi, A., A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation, Appl. Soft Comput., 12, 9, 2765-2780 (2012)
[29] Mashwani, W. K.; Salhi, A., Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation, Appl. Soft Comput., 39, C, 292-309 (2016)
[31] Miller, B. L.; Goldberg, D. E., Genetic algorithms, tournament selection, and the effects of noise, Complex Syst., 9, 3, 193-212 (1995)
[32] Mo, J.; Fan, Z.; Li, W.; Fang, Y.; You, Y.; Cai, X., Multi-factorial evolutionary algorithm based on M2M decomposition, (Shi, Y.; Tan, K. C.; Zhang, M.; Tang, K.; Li, X.; Zhang, Q.; Tan, Y.; Middendorf, M.; Jin, Y., Simulated Evolution and Learning (2017), Springer International Publishing: Springer International Publishing Cham), 134-144
[34] Schuetze, O.; Equivel, X.; Lara, A.; Coello Coello, C. A., Some comments on GD and IGD and relations to the Hausdorff distance, Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, 1971-1974 (2010), ACM
[35] Storn, R.; Price, K., Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 4, 341-359 (1997) · Zbl 0888.90135
[36] Sun, J.; Zhang, H.; Zhou, A.; Zhang, Q., Learning from a stream of non-stationary and dependent data in multiobjective evolutionary optimization, IEEE Trans. Evol. Comput. (2018)
[37] Tang, L.; Wang, X.; Dong, Z., Adaptive multiobjective differential evolution with reference axis vicinity mechanism, IEEE Trans. Cybern., 49, 9, 3571-3585 (2019)
[38] Tang, Z.; Gong, M.; Zhang, M., Evolutionary multi-task learning for modular extremal learning machine, 2017 IEEE Congress on Evolutionary Computation (CEC), 474-479 (2017)
[39] Wang, X.; Dong, Z.; Tang, L., Multiobjective differential evolution with personal archive and biased self-adaptive mutation selection, IEEE Trans. Syst. Man. Cybern.: Syst., 1-13 (2018)
[40] Wang, X.; Tang, L., An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization, Inf. Sci., 348, 124-141 (2016)
[42] Zhang, J.; Zhou, A.; Tang, K.; Zhang, G., Preselection via classification: a case study on evolutionary multiobjective optimization, Inf. Sci., 465, 388-403 (2018)
[43] Zhang, Q.; Li, H., MOEA/D: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 6, 712-731 (2007)
[44] Zhang, Q.; Liu, W.; Li, H., The performance of a new version of MOEA/D on CEC09 unconstrained mop test instances, 2009 IEEE Congress on Evolutionary Computation, 203-208 (2009)
[45] Zheng, X.; Qin, A. K.; Gong, M.; Zhou, D., Self-regulated evolutionary multi-task optimization, IEEE Trans. Evol. Comput. (2019)
[46] Zhou, A.; Zhang, Q., Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms, IEEE Trans. Evol. Comput., 20, 52-64 (2016)
[47] Zhou, L.; Feng, L.; Zhong, J.; Ong, Y.-S.; Zhu, Z.; Sha, E., Evolutionary multitasking in combinatorial search spaces: a case study in capacitated vehicle routing problem, 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8 (2016), IEEE
[48] Zitzler, E.; Thiele, L.; Bader, J., On set-based multiobjective optimization, IEEE Trans. Evol. Comput., 14, 1, 58-79 (2010)
[49] Zou, J.; Fu, L.; Yang, S.; Zheng, J.; Ruan, G.; Pei, T.; Wang, L., An adaptation reference-point-based multiobjective evolutionary algorithm, Inf. Sci., 488, 41-57 (2019)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.