Abstract
Multi-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization.
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References
T. Back, U. Hammel, H.P. Schwefel, Evolutionary computation: comments on the history and current state, IEEE Trans. Evol. Comput. 1 (1997), 3–17
C.M. Fonseca, P.J.Fleming, An overview of evolutionary algorithms in multiobjective optimization, Evol. Comput. 3 (1995), 1–16
Y.S. Ong, Towards evolutionary multitasking: a new paradigm in evolutionary computation, in Proceedings of International Conference on Computational Intelligence, Cyber Security and Computational Models, Coimbatore, 2015, pp. 25–26.
A. Gupta, Y.S. Ong, L. Feng, Insights on transfer optimization: because experience is the best teacher, IEEE Trans. Emerg. Top. Comput. Intell. 2 (2018), 51–64
Y.S. Ong, A. Gupta, Evolutionary multitasking: a computer science view of cognitive multitasking, Cogn. Comput. 8 (2016), 125–142
A. Gupta, Y.S. Ong, L. Feng, Multifactorial evolution: toward evolutionary multitasking, IEEE Trans. Evol. Comput. 20 (2016), 343–357
H.P. Ma, S.G. Shen, M. Yu, Z.L. Yang, M.R. Fei, H.Y. Zhou, Multi-population techniques in nature inspired optimization algorithms: a comprehensive survey, Swarm Evol. Comput. 44 (2019), 365–387
R. Hashimoto, H. Ishibuchi, N. Masuyama, Y. Nojima, Analysis of evolutionary multi-tasking as an island model, in Proceedings of Genetic and Evolutionary Computation Conference Companion, Kyoto, 2018, pp. 1894–1897.
D.E. Goldberg, K. Sastry, A practical schema theorem for genetic algorithm design and tuning, in Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, San Francisco, 2001, pp. 328–335.
C.B. James, Genetic algorithms and random keys for sequencing and optimization, ORSA J. Comput. 6 (1994), 154–160
Q.Z. Xu, J.H. Zhang, R. Fei, W. Li, Parameter analysis on multifactorial evolutionary algorithm, J. Eng. (Accepted)
R.T. Liaw, C.K. Ting, Evolutionary many-tasking based on biocoenosis through symbiosis: a framework and benchmark problems, in Proceedings of IEEE Congress on Evolutionary Computation, San Sebastian, 2017, pp. 2266–2273.
G.H. Li, Q.F. Zhang, W.F. Gao, Multipopulation evolution framework for multifactorial optimization, in Proceedings of Genetic and Evolutionary Computation Conference, Kyoto, 2018, pp. 215–216.
A. Gupta, J. Mańdziuk, Y.S. Ong, Evolutionary multitasking in bi-level optimization, Complex Intell. Syst. 1 (2015), 83–95
M.G. Gong, Z.D. Tang, H. Li, J. Zhang, Evolutionary multitasking with dynamic resource allocating strategy, IEEE Trans. Evol. Comput. 23 (2019), 858–869
S.W. Jiang, C. Xu, A. Gupta, L. Feng, Y.S. Ong, A.N. Zhang, P.S. Tan, Complex and intelligent systems in manufacturing, IEEE Potentials. 35 (2016), 23–28
K.K. Bali, Y.S. Ong, A. Gupta, P.S. Tan, Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II, IEEE Trans. Evol. Comput. (Online First)
A. Gupta, Y.S. Ong, L. Feng, K.C. Tan, Multiobjective multi-factorial optimization in evolutionary multitasking, IEEE Trans. Cybern. 47 (2017), 1652–1665
R. Sagarna, Y.S. Ong, Concurrently searching branches in software tests generation through multitask evolution, in Proceedings of IEEE Symposium Series on Computational Intelligence, Athens, 2016, pp. 1–8.
L. Bao, Y.T. Qi, M.Q. Shen, X.X. Bu, J.S. Yu, Q. Li, P. Chen, An evolutionary multitasking algorithm for cloud computing service composition, in Proceedings of World Congress on Services, Seattle, 2018, pp. 130–144.
A. Rauniyar, R. Nath, P.K.Muhuri, Multi-factorial evolutionary algorithm based novel solution approach for multi-objective pollution routing problem, Comput. Ind. Eng. 130 (2019), 757–771
C.E. Yang, J.L. Ding, Y.C. Jin, C.Z. Wang, T.Y. Chai, Multitasking multiobjective evolutionary operational indices optimization of beneficiation processes, IEEE Trans. Autom. Sci. Eng. 16 (2019), 1046–1057
J.H. Zhong, L. Feng, W.T. Cai, Y.S. Ong, Multifactorial genetic programming for symbolic regression problems, IEEE Trans. Syst. Man, Cybern. Syst. (Online First)
A. Gupta, Y.S. Ong, Back to the roots: multi-x evolutionary computation, Cogn. Comput. 11 (2019), 1–17
B.S. Da, A. Gupta, Y.S. Ong, L. Feng, Evolutionary multitasking across single and multi-objective formulations for improved problem solving, in Proceedings of IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 1695–1701.
Y.W. Wen, C.K. Ting, Learning ensemble of decision trees through multifactorial genetic programming, in Proceedings of IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 5293–5300.
N.Q. Tuan, T.D. Hoang, H.T.T. Binh, A guided differential evolutionary multi-tasking with powell search method for solving multi-objective continuous optimization, in Proceedings of IEEE Congress on Evolutionary Computation, Rio de Janeiro, 2018, pp. 1–8.
Y. Yuan, Y.S. Ong, A. Gupta, P.S. Tan, H. Xu, Evolutionary multitasking in permutation-based combinatorial optimization problems: realization with TSP, QAP, LOP, and JSP, in Proceedings of IEEE Region 10 Conference, Singapore, 2016, pp. 3157–3164.
P.D. Thanh, D.A. Dung, T.N. Tien, H.T.T.Binh, Binh, An effective representation scheme in multifactorial evolutionary algorithm for solving cluster shortest-path tree problem, in Proceedings of IEEE Congress on Evolutionary Computation, Rio de Janerio, 2018, pp. 1–8.
Y.L. Chen, J.H. Zhong, M.K. Tan, A fast memetic multi-objective differential evolution for multi-tasking optimization, in Proceedings of IEEE Congress on Evolutionary Computation, Rio de Janeiro, 2018, pp. 1–8.
L. Feng, W. Zhou, L. Zhou, S.W. Jiang, J.H. Zhong, B.S. Da, Z.X. Zhu, Y. Wang, An empirical study of multifactorial PSO and multifactorial DE, in Proceedings of IEEE Congress on Evolutionary Computation, San Sebastian, 2017, pp. 921–928.
D.N. Liu, S.J. Huang, J.H. Zhong, Surrogate-assisted multi-tasking memetic algorithm, in Proceedings of IEEE Congress on Evolutionary Computation, Rio de Janeiro, 2018, pp. 1–8.
M.Y. Cheng, A. Gupta, Y.S. Ong, Z.W. Ni, Coevolutionary multitasking for concurrent global optimization: with case studies in complex engineering design, Eng. Appl. Artif. Intell. 64 (2017), 13–24
B.Y. Zhang, A.K. Qin, T. Sellis, Evolutionary feature subspaces generation for ensemble classification, in Proceedings of Genetic and Evolutionary Computation Conference, Kyoto, 2018, pp. 577–584.
Z.D. Tang, M.G. Gong, Adaptive multifactorial particle swarm optimisation, CAAI Trans. Intell. Technol. 4 (2019), 37–46
L. Feng, L. Zhou, J.H. Zhong, A. Gupta, Y.S. Ong, K.C. Tan, A.K. Qin, Evolutionary multitasking via explicit autoencoding, IEEE Trans. Cybern. 49 (2019), 3457–3470
Z.P. Liang, J. Zhang, L. Feng, Z.X. Zhu, A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multitasking, Expert Syst. Appl. 138 (2019), 1–18
Y.L. Chen, J.H. Zhong, L. Feng, J. Zhang, An adaptive archive-based evolutionary framework for many-task optimization, IEEE Trans. Emerg. Top. Comput. Intell. (Online First)
C.E. Yang, J.L. Ding, K.C. Tan, Y.C. Jin, Two-stage assortative mating for multi-objective multifactorial evolutionary optimization, in Proceedings of IEEE 56th Annual Conference on Decision and Control, Melbourne, 2017, pp. 76–81.
Q.J. Chen, X.L. Ma, Y.W. Sun, Z.X. Zhu, Adaptive memetic algorithm based evolutionary multi-tasking single-objective optimization, in Proceedings of Asia-Pacific Conference on Simulated Evolution and Learning, Shenzhen, 2017, pp. 462–472.
B.S. Da, Y.S. Ong, L. Feng, A.K. Qin, A. Gupta, Z.X. Zhu, C.K. Ting, K.Tang, X.Yao, Evolutionary multitasking for single-objective continuous optimization: bench-mark problems, performance metric and baseline results, Nanyang Technological University, Singapore, 2016.
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Wang, N., Xu, Q., Fei, R. et al. Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model. Int J Comput Intell Syst 12, 1121–1133 (2019). https://doi.org/10.2991/ijcis.d.191004.001
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DOI: https://doi.org/10.2991/ijcis.d.191004.001