research-article

An Enhanced Competitive Swarm Optimizer With Strongly Convex Sparse Operator for Large-Scale Multiobjective Optimization

Authors:

Yaochu JinAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 26, Issue 5

Pages 859 - 871

https://doi.org/10.1109/TEVC.2021.3111209

Published: 01 October 2022 Publication History

Abstract

Sparse multiobjective optimization problems (MOPs) have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensional feature selection. However, little attention has been paid to sparse large-scale MOPs, whose Pareto-optimal sets are sparse, i.e., with many decision variables equal to zero. To address this issue, this article proposes an enhanced competitive swarm optimization algorithm assisted by a strongly convex sparse operator (SCSparse). A tricompetition mechanism is introduced into competitive swarm optimization, aiming to strike a better balance between exploration and exploitation. In addition, the SCSparse is embedded in the position updating of the particles to generate sparse solutions. Our simulation results show that the proposed algorithm outperforms the state-of-the-art methods on both sparse test problems and application examples.

References

[1]

A. Ponsich, A. L. Jaimes, and C. A. C. Coello, “A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications,” IEEE Trans. Evol. Comput., vol. 17, no. 3, pp. 321–344, Jun. 2013.

Digital Library

[2]

J. B. Kollat, P. M. Reed, and R. Maxwell, “Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics,” Water Resources Res., vol. 47, no. 2, 2011, Art. no. 10.1029/2010WR009194.

[3]

H. Han, X. Wu, L. Zhang, Y. Tian, and J. Qiao, “Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization,” IEEE Trans. Cybern., vol. 49, no. 1, pp. 69–82, Jan. 2019.

[4]

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw., vol. 4. Perth, WA, Australia, 1995, pp. 1942–1948.

[5]

R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Mach. Hum. Sci., Nagoya, Japan, 1995, pp. 39–43.

[6]

T. Blackwell and J. Kennedy, “Impact of communication topology in particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 23, no. 4, pp. 689–702, Aug. 2019.

[7]

M. Løvbjerg, T. K. Rasmussen, and T. Krink, “Hybrid particle swarm optimiser with breeding and subpopulations,” in Proc. 3rd Annu. Genet. Evol. Comput. Conf. (GECCO), 2001, pp. 469–476.

[8]

J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proc. Congr. Evol. Comput., Honolulu, HI, USA, 2002, pp. 1671–1676.

[9]

J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” in Proc. IEEE Swarm Intell. Symp., Pasadena, CA, USA, 2005, pp. 124–129.

[10]

G. G. Yen and W. F. Leong, “Dynamic multiple swarms in multiobjective particle swarm optimization,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 39, no. 4, pp. 890–911, Jul. 2009.

Digital Library

[11]

Z.-H. Zhan, J. Li, J. Cao, J. Zhang, H. S.-H. Chung, and Y.-H. Shi, “Multiple populations for multiple objectives: A coevolutionary technique for solving multiobjective optimization problems,” IEEE Trans. Cybern., vol. 43, no. 2, pp. 445–463, Apr. 2013.

[12]

Z.-H. Zhan, J. Zhang, Y. Li, and H. S.-H. Chung, “Adaptive particle swarm optimization,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 39, no. 6, pp. 1362–1381, Dec. 2009.

Digital Library

[13]

H. Wang and G. G. Yen, “Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system,” IEEE Trans. Evol. Comput., vol. 19, no. 1, pp. 1–18, Feb. 2015.

Digital Library

[14]

H. Han, W. Lu, L. Zhang, and J. Qiao, “Adaptive gradient multiobjective particle swarm optimization,” IEEE Trans. Cybern., vol. 48, no. 11, pp. 3067–3079, Nov. 2018.

[15]

J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Basker, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281–295, Jun. 2006.

Digital Library

[16]

R. Cheng and Y. Jin, “A social learning particle swarm optimization algorithm for scalable optimization,” Inf. Sci., vol. 291, pp. 43–60, Jan. 2015.

Digital Library

[17]

R. Liu, J. Liu, Y. Li, and J. Liu, “A random dynamic grouping based weight optimization framework for large-scale multi-objective optimization problems,” Swarm Evol. Comput., vol. 55, Jun. 2020, Art. no.

[18]

C. Heet al., “Accelerating large-scale multiobjective optimization via problem reformulation,” IEEE Trans. Evol. Comput., vol. 23, no. 6, pp. 949–961, Dec. 2019.

Digital Library

[19]

X. Maet al., “A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables,” IEEE Trans. Evol. Comput., vol. 20, no. 2, pp. 275–298, Apr. 2016.

Digital Library

[20]

Y. Tian, X. Zheng, X. Zhang, and Y. Jin, “Efficient large-scale multiobjective optimization based on a competitive swarm optimizer,” IEEE Trans. Cybern., vol. 50, no. 8, pp. 3696–3708, Aug. 2020.

[21]

L. M. Antonio and C. A. C. Coello, “Use of cooperative coevolution for solving large scale multiobjective optimization problems,” in Proc. IEEE Congr. Evol. Comput., Cancun, Mexico, 2013, pp. 2758–2765.

[22]

S. Kukkonen and J. Lampinen, “GDE3: The third evolution step of generalized differential evolution,” in Proc. IEEE Congr. Evol. Comput., vol. 1. Edinburgh, U.K., 2005, pp. 443–450.

[23]

R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE Trans. Cybern., vol. 45, no. 2, pp. 191–204, Feb. 2015.

[24]

J. Zhou, W. Fang, X. Wu, J. Sun, and S. Cheng, “An opposition-based learning competitive particle swarm optimizer,” in Proc. IEEE CEC, Vancouver, BC, Canada, 2016, pp. 515–521.

[25]

P. Mohapatra, K. N. Das, and S. Roy, “A modified competitive swarm optimizer for large scale optimization problems,” Appl. Soft. Comput., vol. 59, pp. 340–362, Oct. 2017.

Digital Library

[26]

J. Wang, Q. Chang, Q. Chang, Y. Liu, and N. R. Pal, “Weight noise injection-based MLPs with group lasso penalty: Asymptotic convergence and application to node pruning,” IEEE Trans. Cybern., vol. 49, no. 12, pp. 4346–4364, Dec. 2019.

[27]

Q. Chang, J. Wang, H. Zhang, L. Shi, J. Wang, and N. R. Pal, “Structure optimization of neural networks with L1 regularization on gates,” in Proc. IEEE Symp. Series Comput. Intell. (SSCI), Bangalore, India, 2018, pp. 196–203.

[28]

Y. Tian, X. Zhang, C. Wang, and Y. Jin, “An evolutionary algorithm for large-scale sparse multiobjective optimization problems,” IEEE Trans. Evol. Comput., vol. 24, no. 2, pp. 380–393, Apr. 2020.

[29]

Y. Tian, C. Lu, X. Zhang, K. C. Tan, and Y. Jin, “Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks,” IEEE Trans. Cybern., vol. 51, no. 6, pp. 3115–3128, Jun. 2021. 10.1109/TCYB.2020.2979930.

[30]

Y. Tian, C. Lu, X. Zhang, F. Cheng, and Y. Jin, “A pattern mining-based evolutionary algorithm for large-scale sparse multiobjective optimization problems,” IEEE Trans. Cybern., early access, Dec. 30, 2021. 10.1109/TCYB.2020.3041325.

[31]

Y. Tianet al., “Evolutionary large-scale multi-objective optimization: A survey,” ACM Comput. Surveys, to be published.

[32]

X. Zhang, Y. Tian, R. Cheng, and Y. Jin, “A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization,” IEEE Trans. Evol. Comput., vol. 22, no. 1, pp. 97–112, Feb. 2018.

[33]

H. Zille, H. Ishibuchi, S. Mostaghim, and Y. Nojima, “A framework for large-scale multiobjective optimization based on problem transformation,” IEEE Trans. Evol. Comput., vol. 22, no. 2, pp. 260–275, Apr. 2018.

[34]

H. Qian and Y. Yu, “Solving high-dimensional multi-objective optimization problems with low effective dimensions,” in Proc. 31st AAAI Conf. Artif. Intell., 2017, pp. 875–881.

[35]

R. Liu, R. Ren, J. Liu, and J. Liu, “A clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems,” Appl. Soft Comput., vol. 89, Apr. 2020, Art. no.

Digital Library

[36]

C. He, S. Huang, R. Cheng, K. C. Tan, and Y. Jin, “Evolutionary multiobjective optimization driven by generative adversarial networks (GANs),” IEEE Trans. Cybern., vol. 51, no. 6, pp. 3129–3142, Jun. 2021.

[37]

S. Boyd, J. Duchi, and L. Vandenberghe, “Subgradients,” Lecture Notes EE364b, Stanford Univ., Stanford, CA, USA, 2014.

[38]

N. Higashi and H. Iba, “Particle swarm optimization with gaussian mutation,” in Proc. IEEE Swarm Intell. Symp., Indianapolis, IN, USA, 2003, pp. 72–79.

[39]

E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm,” in Proc. 5th Conf. Evol. Methods Design Optim. Control Appl. Ind. Problems, 2001, pp. 95–100.

[40]

Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE ICEC Conf., Anchorage, AK, USA, 1998, pp. 69–73.

[41]

S. Robert, J. Torrie, and D. Dickey, Principles and Procedures of Statistics: A Biometrical Approach. New York, NY, USA: McGraw-Hill, 1997.

[42]

M. Hollander and D. A. Wolfe, Nonparametric Statistical Methods. New York, NY, USA: Wiley, 1973.

[43]

J. D. Gibbons, Nonparametric Statistical Inference, 2nd ed. New York, NY, USA: M. Dekker, 1985.

[44]

M. Clerc and J. Kennedy, “The particle swarm—Explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58–73, Feb. 2002.

Digital Library

[45]

K. Deb and R. B. Agrawal, “Simulated binary crossover for continuous search space,” Complex Syst., vol. 9, no. 4, pp. 115–148, 1995.

[46]

K. Deb and M. Goyal, “A combined genetic adaptive search (GeneAS) for engineering design,” Comput. Sci. Informat., vol. 26, no. 4, pp. 30–45, 1996.

[47]

Y. Tian, R. Cheng, X. Zhang, and Y. Jin, “PlatEMO: A MATLAB platform for evolutionary multi-objective optimization,” IEEE Comput. Intell. Mag., vol. 12, no. 4, pp. 73–87, Nov. 2017.

Digital Library

[48]

Y. Jin and B. Sendhoff, “Pareto-based multiobjective machine learning: An overview and case studies,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 38, no. 3, pp. 397–415, May 2008.

Digital Library

[49]

L. While, P. Hingston, L. Barone, and S. Huband, “A faster algorithm for calculating hypervolume,” IEEE Trans. Evol. Comput., vol. 10, no. 1, pp. 29–38, Feb. 2006.

Digital Library

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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 26, Issue 5

Oct. 2022

398 pages

ISSN:1089-778X

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1089-778X © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 October 2022

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Gao XSong S(2024)A switching competitive swarm optimizer for multi-objective optimization with irregular Pareto frontsExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124641255:PBOnline publication date: 1-Dec-2024
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