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Volume 9 Issue 8
Aug.  2022

IEEE/CAA Journal of Automatica Sinica

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S. F. Han, K. Zhu, M. C. Zhou, X. J. Liu, H. Y. Liu, Y. Al-Turki, and  A. Abusorrah,  “A novel multiobjective fireworks algorithm and its applications to imbalanced distance minimization problems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1476–1489, Aug. 2022. doi: 10.1109/JAS.2022.105752
Citation: S. F. Han, K. Zhu, M. C. Zhou, X. J. Liu, H. Y. Liu, Y. Al-Turki, and  A. Abusorrah,  “A novel multiobjective fireworks algorithm and its applications to imbalanced distance minimization problems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1476–1489, Aug. 2022. doi: 10.1109/JAS.2022.105752

A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

doi: 10.1109/JAS.2022.105752
Funds:  This work was supported in part by the National Natural Science Foundation of China (62071230, 62061146002), the Natural Science Foundation of Jiangsu Province (BK20211567), and the Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia (FP-147-43)
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  • Recently, multimodal multiobjective optimization problems (MMOPs) have received increasing attention. Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible. Although some evolutionary algorithms for them have been proposed, they mainly focus on the convergence rate in the decision space while ignoring solutions diversity. In this paper, we propose a new multiobjective fireworks algorithm for them, which is able to balance exploitation and exploration in the decision space. We first extend a latest single-objective fireworks algorithm to handle MMOPs. Then we make improvements by incorporating an adaptive strategy and special archive guidance into it, where special archives are established for each firework, and two strategies (i.e., explosion and random strategies) are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives. Finally, we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems. Experimental results show that the proposed algorithm is superior to compared algorithms in solving them. Also, its runtime is less than its peers’.

     

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  • [1]
    Q. Kang, X. Y. Song, M. C. Zhou, and L. Li, “A collaborative resource allocation strategy for decomposition-based multiobjective evolutionary algorithms,” IEEE Trans. Syst. Man Cybern. Syst., vol. 49, no. 12, pp. 2416–2423, Dec. 2019. doi: 10.1109/TSMC.2018.2818175
    [2]
    J. H. Wang, Y. Y. Sun, Z. Z. Zhang, and S. C. Gao, “Solving multitrip pickup and delivery problem with time windows and manpower planning using multiobjective algorithms,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1134–1153, Jul. 2020.
    [3]
    Y. C. Hua, Q. Q. Liu, K. R. Hao, and Y. C. Jin, “A survey of evolu-tionary algorithms for multi-objective optimization problems with irre-gular Pareto fronts,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 303–318, Feb. 2021.
    [4]
    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, Apr. 2002. doi: 10.1109/4235.996017
    [5]
    E. Zitzler and S. Künzli, “Indicator-based selection in multiobjective search,” in Proc. 8th Int. Conf. Parallel Problem Solving From Nature-PPSN VIII, Birmingham, UK, 2004, pp. 832–842.
    [6]
    E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm,” Institut Für Technische Informatik und Kommunikationsnetze (TIK), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland, TIK Rep. 103, vol. 103, 2001.
    [7]
    Q. F. Zhang and H. Li, “MOEA/D: A multiobjective evolutionary algorithm based on decomposition,” IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712–731, Dec. 2007. doi: 10.1109/TEVC.2007.892759
    [8]
    X. Q. Zuo, B. Li, X. W. Huang, M. C. Zhou, C. Y. Cheng, and X. C. Zhao, et al., “Optimizing hospital emergency department layout via multiobjective Tabu search,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 3, pp. 1137–1147, Jul. 2019. doi: 10.1109/TASE.2018.2873098
    [9]
    J. C. Liu, Y. C. Liu, Y. C. Jin, and F. Li, “A decision variable assortment-based evolutionary algorithm for dominance robust multiobjective optimization,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 5, pp. 3360–3375, May 2022. doi: 10.1109/TSMC.2021.3067785
    [10]
    Y. X. Feng, M. C. Zhou, G. D. Tian, Z. W. Li, Z. F. Zhang, Q. Zhang, and J. R. Tan, “Target disassembly sequencing and scheme evaluation for CNC machine tools using improved multiobjective ant colony algorithm and fuzzy integral,” IEEE Trans. Syst. Man Cybern. Syst., vol. 49, no. 12, pp. 2438–2451, Dec. 2019.
    [11]
    J. J. Liang, C. T. Yue, and B. Y. Qu, “Multimodal multi-objective optimization: A preliminary study,” in Proc. IEEE Congr. Evolutionary Computation, Vancouver, Canada, 2016, pp. 2454–2461.
    [12]
    S. Oliveto, D. Sudholt, and C. Zarges, “On the benefits and risks of using fitness sharing for multimodal optimisation,” Theor. Comput. Sci., vol. 773, pp. 53–70, Jun. 2019. doi: 10.1016/j.tcs.2018.07.007
    [13]
    J. J. Liang, S. T. Ma, B. Y. Qu, and B. Niu, “Strategy adaptative memetic crowding differential evolution for multimodal optimization,” in Proc. IEEE Congr. Evolutionary Computation, Brisbane, Australia, 2012, pp. 1–7.
    [14]
    M. M. H. Ellabaan and Y. S. Ong, “Valley-adaptive clearing scheme for multimodal optimization evolutionary search,” in Proc. 9th Int. Conf. Intelligent Systems Design and Applications, Pisa, Italy, 2009, pp. 1–6.
    [15]
    X. D. Li, “Efficient differential evolution using speciation for multimodal function optimization,” in Proc. 7th Annu. Conf. Genetic and Evolutionary Computation, Washington, USA, 2005, pp. 873–880.
    [16]
    B. Y. Qu, G. S. Li, Q. Q. Guo, L. Yan, X. Z. Chai, and Z. Q. Guo, “A niching multi-objective harmony search algorithm for multimodal multi-objective problems,” in Proc. IEEE Congr. on Evolutionary Computation, Wellington, New Zealand, 2019, pp. 1267–1274.
    [17]
    C. T. Yue, B. Y. Qu, and J. Liang, “A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems,” IEEE Trans. Evol. Comput., vol. 22, no. 5, pp. 805–817, Oct. 2018. doi: 10.1109/TEVC.2017.2754271
    [18]
    Y. Hu, J. Wang, J. Liang, K. J. Yu, H. Song, Q. Q. Guo, C. T. Yue, and Y. L. Wang, “A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm,” Sci. China Inf. Sci., vol. 62, no. 7, p. 70206, May 2019.
    [19]
    J. Liang, Q. Q. Guo, C. T. Yue, B. Y. Qu, and K. J. Yu, “A self-organizing multi-objective particle swarm optimization algorithm for multimodal multi-objective problems,” in Proc. 9th Int. Conf. Advances in Swarm Intelligence, Shanghai, China, 2018, pp. 550–560.
    [20]
    W. Z. Zhang, G. Q. Li, W. W. Zhang, J. Liang, and G. G. Yen, “A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization,” Swarm Evol. Comput., vol. 50, p. 100569, Nov. 2019.
    [21]
    Y. Tan and Y. C. Zhu, “Fireworks algorithm for optimization,” in Proc. 1st Int. Conf. Advances in Swarm Intelligence, Beijing, China, 2010, pp. 355–364.
    [22]
    J. Liu, S. Zheng, and Y. Tan, “The improvement on controlling exploration and exploitation of firework algorithm,” in Proc. 4th Int. Conf. Advances in Swarm Intelligence, Harbin, China, 2013, pp. 11–23.
    [23]
    B. Zhang, Y. J. Zheng, M. X. Zhang, and S. Y. Chen, “Fireworks algorithm with enhanced fireworks interaction,” IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 14, no. 1, pp. 42–55, Jan.−Feb. 2017. doi: 10.1109/TCBB.2015.2446487
    [24]
    X. G. Li, S. F. Han, and C. Q. Gong, “Analysis and improvement of fireworks algorithm,” Algorithms, vol. 10, no. 1, p. 26, Feb. 2017.
    [25]
    X. G. Li, S. F. Han, L. Zhao, C. Q. Gong, and X. J. Liu, “Adaptive mutation dynamic search fireworks algorithm,” Algorithms, vol. 10, no. 2, p. 48, Apr. 2017.
    [26]
    J. Z. Li and Y. Tan, “Loser-out tournament-based fireworks algorithm for multimodal function optimization,” IEEE Trans. Evol. Comput., vol. 22, no. 5, pp. 679–691, Oct. 2018. doi: 10.1109/TEVC.2017.2787042
    [27]
    L. Yan, G. S. Li, Y. C. Jiao, B. Y. Qu, C. T. Yue, and S. K. Qu, “A performance enhanced niching multi-objective bat algorithm for multimodal multi-objective problems,” in Proc. IEEE Congr. Evolutionary Computation, Wellington, New Zealand, 2019, pp. 1275–1282.
    [28]
    J. Liang, K. J. Qiao, C. T. Yue, K. J. Yu, B. Y. Qu, R. H. Xu, Z. M. Li, and Y. Hu, “A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems,” Swarm Evol. Comput., vol. 60, p. 100788, Feb. 2021.
    [29]
    B. Y. Qu, C. Li, J. Liang, L. Yan, K. J. Yu, and Y. S. Zhu, “A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems,” Appl. Soft Comput., vol. 86, p. 105886, Jan. 2020.
    [30]
    Q. Q. Fan and X. F. Yan, “Solving multimodal multiobjective problems through zoning search,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 8, pp. 4836–4847, Aug. 2021. doi: 10.1109/TSMC.2019.2944338
    [31]
    Q. Q. Fan and O. K. Ersoy, “Zoning search with adaptive resource allocating method for balanced and imbalanced multimodal multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1163–1176, Jun. 2021. doi: 10.1109/JAS.2021.1004027
    [32]
    K. Deb and S. Tiwari, “Omni-optimizer: A procedure for single and multi-objective optimization,” in Proc. 3rd Int. Conf. Evolutionary Multi-Criterion Optimization, Guanajuato, Mexico, 2005, pp. 47–61.
    [33]
    K. P. Chan and T. Ray, “An evolutionary algorithm to maintain diversity in the parametric and the objective space,” in Proc. Int. Conf. Computational Robotics and Autonomous Systems, Singapore, 2005, pp. 13–16.
    [34]
    A. M. Zhou, Q. F. Zhang, and Y. C. Jin, “Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm,” IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 1167–1189, Oct. 2009. doi: 10.1109/TEVC.2009.2021467
    [35]
    Y. Liu, H. Ishibuchi, G. G. Yen, Y. Nojima, and N. Masuyama, “Handling imbalance between convergence and diversity in the decision space in evolutionary multimodal multiobjective optimization,” IEEE Trans. Evol. Comput., vol. 24, no. 3, pp. 551–565, Jun. 2020.
    [36]
    Y. Liu, G. G. Yen, and D. W. Gong, “A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies,” IEEE Trans. Evol. Comput., vol. 23, no. 4, pp. 660–674, Aug. 2019. doi: 10.1109/TEVC.2018.2879406
    [37]
    Q. Z. Lin, W. Lin, Z. X. Zhu, M. G. Gong, J. Q. Li, and C. A. C. Coello, “Multimodal multiobjective evolutionary optimization with dual clustering in decision and objective spaces,” IEEE Trans. Evol. Comput., vol. 25, no. 1, pp. 130–144, Feb. 2021. doi: 10.1109/TEVC.2020.3008822
    [38]
    S. Q. Zheng, A. Janecek, and Y. Tan, “Enhanced fireworks algorithm,” in Proc. IEEE Congr. Evolutionary Computation, Cancun, Mexico, 2013, pp. 2069–2077.
    [39]
    J. Z. Li, S. Q. Zheng, and Y. Tan, “Adaptive fireworks algorithm,” in Proc. IEEE Congr. Evolutionary Computation, Beijing, China, 2014, pp. 3214–3221.
    [40]
    S. Q. Zheng, A. Janecek, J. Z. Li, and Y. Tan, “Dynamic search in fireworks algorithm,” in Proc. IEEE Congr. Evolutionary Computation, Beijing, China, 2014, pp. 3222–3229.
    [41]
    J. Z. Li, S. Q. Zheng, and Y. Tan, “The effect of information utilization: Introducing a novel guiding spark in the fireworks algorithm,” IEEE Trans. Evol. Comput., vol. 21, no. 1, pp. 153–166, Feb. 2017. doi: 10.1109/TEVC.2016.2589821
    [42]
    X. Yang and Y. Tan, “Sample index based encoding for clustering using evolutionary computation,” in Proc. 5th Int. Conf. Advances in Swarm Intelligence, Hefei, China, 2014, pp. 489–498.
    [43]
    K. Ding, Y. Y. Chen, Y. B. Wang, and Y. Tan, “Regional seismic waveform inversion using swarm intelligence algorithms,” in Proc. IEEE Congr. Evolutionary Computation, Sendai, Japan, 2015, pp. 1235–1241.
    [44]
    N. Bacanin and M. Tuba, “Fireworks algorithm applied to constrained portfolio optimization problem,” in Proc. IEEE Congr. Evolutionary Computation, Sendai, Japan, 2015, pp. 1242–1249.
    [45]
    H. A. Bouarara, R. M. Hamou, A. Amine, and A. Rahmani, “A fireworks algorithm for modern web information retrieval with visual results mining,” Int. J. Swarm Intell. Res., vol. 6, no. 3, pp. 1–23, Jul. 2015. doi: 10.4018/IJSIR.2015070101
    [46]
    A. Rahmani, A. Amine, R. M. Hamou, M. E. Rahmani, and H. A. Bouarara, “Privacy preserving through fireworks algorithm based model for image perturbation in big data,” Int. J. Swarm Intell. Res., vol. 6, no. 3, pp. 41–58, Jul. 2015. doi: 10.4018/IJSIR.2015070103
    [47]
    L. Liu, S. Q. Zheng, and Y. Tan, “S-metric based multi-objective fireworks algorithm,” in Proc. IEEE Congr. Evolutionary Computation, Sendai, Japan, 2015, pp. 1257–1264.
    [48]
    S. I. Bejinariu, H. Costin, F. Rotaru, R. Luca, and C. Niţă, “Fireworks algorithm based single and multi-objective optimization,” Bull. Polytech. Inst. Iasi,Autom. Contr. Comput. Sci. Sec., vol. 62, no. 66, pp. 19–34, Jul. 2016.
    [49]
    S. C. Gao, Y. Yu, Y. R. Wang, J. H. Wang, J. J. Cheng, and M. C. Zhou, “Chaotic local search-based differential evolution algorithms for optimization,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 6, pp. 3954–3967, Jun. 2021. doi: 10.1109/TSMC.2019.2956121
    [50]
    W. Y. Dong and M. C. Zhou, “A supervised learning and control method to improve particle swarm optimization algorithms,” IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 7, pp. 1135–1148, Jul. 2017. doi: 10.1109/TSMC.2016.2560128
    [51]
    Z. Zhao, S. Liu, M. Zhou, and A. Abusorrah, “Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1199–1209, Jun. 2021.
    [52]
    J. Yu, H. Takagi, and Y. Tan, “Fireworks algorithm for multimodal optimization using a distance-based exclusive strategy,” in Proc. IEEE Congr. Evolutionary Computation, Wellington, New Zealand, 2019, pp. 2215–2220.
    [53]
    J. Liang, B. Qu, D. Gong, and C. Yue, “Problem definitions and evaluation criteria for the CEC 2019 special session on multimodal multiobjective optimization,” in Proc. IEEE Congr. Evol. Comput.,Wellington, New Zealand, Jun. 2019.
    [54]
    C. T. Yue, B. Y. Qu, K. J. Yu, J. Liang, and X. D. Li, “A novel scalable test problem suite for multimodal multiobjective optimization,” Swarm Evol. Comput., vol. 48, pp. 62–71, Aug. 2019. doi: 10.1016/j.swevo.2019.03.011
    [55]
    E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. da Fonseca, “Performance assessment of multiobjective optimizers: An analysis and review,” IEEE Trans. Evol. Comput., vol. 7, no. 2, pp. 117–132, Apr. 2003. doi: 10.1109/TEVC.2003.810758
    [56]
    J. Liang, W. W. Xu, C. T. Yue, K. J. Yu, H. Song, O. D. Crisalle, and B. Y. Qu, “Multimodal multiobjective optimization with differential evolution,” Swarm Evol. Comput., vol. 44, pp. 1028–1059, Feb. 2019. doi: 10.1016/j.swevo.2018.10.016
    [57]
    J. J. Liang, B. Qu, and D. Gong. Publishing the results of the competition on multimodal multiobjective optimization [Online]. Available: http://www5.zzu.edu.cn/ecilab/info/1036/1211.htm. Accessed on: Jan. 10, 2020.
    [58]
    K. Maity, R. Sengupta, and S. Saha, “MM-NAEMO: Multimodal neighborhood-sensitive archived evolutionary many-objective optimization algorithm,” in Proc. IEEE Congr. Evolutionary Computation, Wellington, New Zealand, 2019, pp. 286–294.
    [59]
    W. Zhang, J. Wang, and F. Lan, “Dynamic hand gesture recognition based on short-term sampling neural networks,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 110–120, Jan. 2021.
    [60]
    S. Harford, F. Karim, and H. Darabi, “Generating adversarial samples on multivariate time series using variational autoencoders,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1523–1538, Sept. 2021.
    [61]
    E. F. Ohata G. M. Bezerra, J. V. S. das Chagas, A. V. L. Neto, A. B. Albuquerque, and V. H. C. de Albuquerque, “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 239–248, Jan. 2021.
    [62]
    X. W. Guo, M. C. Zhou, S. X. Liu, and L. Qi, “Lexicographic multiobjective scatter search for the optimization of sequencedependent selective disassembly subject to multiresource constraints,” IEEE Trans. Cybern., vol. 50, no. 7, pp. 3307–3317, Jul. 2020. doi: 10.1109/TCYB.2019.2901834
    [63]
    M. Emmons, A. A. Maciejewski, C. Anderson, and E. K. P. Chong, “Classifying environmental features from local observations of emergent swarm behavior,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 674–682, May 2020.
    [64]
    M. Ghahramani, Y. Qiao, M. C. Zhou, A. O’ Hagan, and J. Sweeney, “AI-based modeling and data-driven evaluation for smart manufacturing processes,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1026–1037, Jul. 2020.
    [65]
    H. Zhang, L. Jin, and C. Ye, “An RGB-D camera based visual positioning system for assistive navigation by a robotic navigation aid,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1389–1400, Aug. 2021.
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    Highlights

    • A new multiobjective fireworks algorithm is proposed
    • A special archive for each firework is established to guide firework explosion
    • An adaptive strategy is designed to ensure fast convergence and high solution diversity in decision space
    • Extensive experimental results indicate the superiority of the proposed algorithm over its state-of-the-art peers

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