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A modified hybrid particle swarm optimization based on comprehensive learning and dynamic multi-swarm strategy

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Abstract

Particle swarm optimization (PSO) is a simple yet efficient population-based algorithm that handles various optimization problems. Nevertheless, diversity and convergence are two significant PSO limits, particularly when tackling challenging optimization issues. This paper develops a PSO with comprehensive learning and a modified dynamic multi-swarm strategy (CLDMSL-PSO) to solve these problems. In the beginning, each iteration of CLDMSL-PSO splits the total population into two subpopulations, one for exploration and the other for exploitation. The comprehensive learning (CL) strategy builds exemplars for the exploration subpopulation. The modified dynamic multi-swarm (DMS) strategy is equipped with the Quasi-Newton method to create the exploitation subpopulation. Second, a self-regulation nonlinear inertia weight, which considers the search level of different sub-swarms, is developed to accelerate the search speed in the early stage and strengthen the exploitation ability in the latter stage of the exploitation subpopulation. Third, the exploitation subpopulation uses a dynamic regrouping period parameter to regulate the frequency of information exchange among the sub-swarms. Finally, the Cauchy mutation is adopted to prevent falling into local optima during the search process. CLDMSL-PSO has been tested on extensive benchmark functions and a multifilament melt spinning process problem. Experimental results show that CLDMSL-PSO outperforms other state-of-art evolutionary algorithms on most optimization problems.

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References

  • ALA’M A-Z, Faris H, Alqatawna J, Hassonah MA (2018) Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowl Based Syst 153:91–104

    Google Scholar 

  • Al-Gharaibeh RS, Ali MZ, Daoud MI, Alazrai R, Abdel-Nabi H, Hriez S, Suganthan PN (2021) Real-parameter constrained optimization using enhanced quality-based cultural algorithm with novel influence and selection schemes. Inf Sci 576:242–273

    MathSciNet  Google Scholar 

  • Awad NH, Ali MZ, Mallipeddi R, Suganthan PN (2019) An efficient differential evolution algorithm for stochastic OPF based active–reactive power dispatch problem considering renewable generators. Appl Soft Comput 76:445–458

    Google Scholar 

  • Bharti KK, Singh PK (2016) Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering. Appl Soft Comput 43:20–34

    Google Scholar 

  • BinSaeedan W, Alramlawi S (2021) CS-BPSO: Hybrid feature selection based on chi-square and binary PSO algorithm for Arabic email authorship analysis. Knowl Based Syst 227:107224

    Google Scholar 

  • Chen K, Zhou F, Liu A (2018a) Chaotic dynamic weight particle swarm optimization for numerical function optimization. Knowl Based Syst 139:23–40

    Google Scholar 

  • Chen K, Zhou F, Wang Y, Yin L (2018b) An ameliorated particle swarm optimizer for solving numerical optimization problems. Appl Soft Comput 73:482–496

    Google Scholar 

  • Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43–60

    MathSciNet  Google Scholar 

  • Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144

    MathSciNet  Google Scholar 

  • Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez J-C, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12:171–195

    Google Scholar 

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18

    Google Scholar 

  • Doğan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: vortex search algorithm. Inf Sci 293:125–145

    Google Scholar 

  • Gao Y-J, Shang Q-X, Yang Y-Y, Hu R, Qian B (2023) Improved particle swarm optimization algorithm combined with reinforcement learning for solving flexible job shop scheduling problem. In: International conference on intelligent computing. Springer, pp 288–298

  • Ghosh A, Das S, Das AK, Senkerik R, Viktorin A, Zelinka I, Masegosa AD (2022) Using spatial neighborhoods for parameter adaptation: an improved success history based differential evolution. Swarm Evol Comput 71:101057

    Google Scholar 

  • Gong Y-J, Li J-J, Zhou Y, Li Y, Chung HS-H, Shi Y-H, Zhang J (2015) Genetic learning particle swarm optimization. IEEE Trans Cybern 46:2277–2290

    PubMed  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks. IEEE, pp 1942–1948

  • Kıran MS, Gündüz M (2013) A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Appl Soft Comput 13:2188–2203

    Google Scholar 

  • Lai C-M, Yeh W-C, Huang Y-C (2017) Entropic simplified swarm optimization for the task assignment problem. Appl Soft Comput 58:115–127

    Google Scholar 

  • Li W, Meng X, Huang Y, Fu Z-H (2020) Multipopulation cooperative particle swarm optimization with a mixed mutation strategy. Inf Sci 529:179–196

    MathSciNet  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295

    Google Scholar 

  • Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer with local search. In: 2005 IEEE Congress on evolutionary computation. IEEE, pp 522–528

  • Liang J-J, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS 2005. IEEE, pp 124–129

  • Lim WH, Isa NAM (2014) An adaptive two-layer particle swarm optimization with elitist learning strategy. Inf Sci 273:49–72

    ADS  MathSciNet  Google Scholar 

  • Lin A, Sun W, Yu H, Wu G, Tang H (2019) Global genetic learning particle swarm optimization with diversity enhancement by ring topology. Swarm Evol Comput 44:571–583

    Google Scholar 

  • Lynn N, Suganthan PN (2015) Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol Comput 24:11–24

    Google Scholar 

  • Lynn N, Suganthan PN (2017) Ensemble particle swarm optimizer. Appl Soft Comput 55:533–548

    Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513

    Google Scholar 

  • Molaei S, Moazen H, Najjar-Ghabel S, Farzinvash L (2021) Particle swarm optimization with an enhanced learning strategy and crossover operator. Knowl Based Syst 215:106768

    Google Scholar 

  • Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917

    Google Scholar 

  • Nasir M, Das S, Maity D, Sengupta S, Halder U, Suganthan PN (2012) A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inf Sci 209:16–36

    MathSciNet  Google Scholar 

  • Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11:3658–3670

    Google Scholar 

  • Olorunda O, Engelbrecht AP (2008) Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence). IEEE, pp 1128–1134

  • Pan Q-K, Tasgetiren MF, Suganthan PN, Chua TJ (2011) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf Sci 181:2455–2468

    MathSciNet  Google Scholar 

  • Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the 2003 IEEE swarm intelligence symposium. IEEE, pp 174–181

  • Qu BY, Suganthan PN, Das S (2013) A distance-based locally informed particle swarm model for multimodal optimization. IEEE Trans Evol Comput 17:387–402

    Google Scholar 

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8:240–255

    Google Scholar 

  • Rizki M, Umam MIH, Hamzah ML, Sutoyo S (2023) Application self-organizing maps and particle swarm optimization for clustering. In: AIP conference proceedings. AIP Publishing

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the1998 IEEE international conference on evolutionary computation. IEEE, pp 69–73

  • Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 1658–1665

  • Tanweer MR, Suresh S, Sundararajan N (2015) Self regulating particle swarm optimization algorithm. Inf Sci 294:182–202

    MathSciNet  Google Scholar 

  • Tanweer MR, Suresh S, Sundararajan N (2016) Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems. Inf Sci 326:1–24

    Google Scholar 

  • Tao X, Li X, Chen W, Liang T, Li Y, Guo J, Qi L (2021) Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization. Inf Sci 578:457–481

    MathSciNet  Google Scholar 

  • Tian D, Zhao X, Shi Z (2019) Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization. Swarm Evol Comput 51:100573

    Google Scholar 

  • Tsai C-Y, Kao I-W (2011) Particle swarm optimization with selective particle regeneration for data clustering. Expert Syst Appl 38:6565–6576

    Google Scholar 

  • Wang B, Wei J (2023) Particle swarm optimization with genetic evolution for task offloading in device-edge-cloud collaborative computing. In: International conference on intelligent computing. Springer, pp 340–350

  • Wang R, Hao K, Chen L, Wang T, Jiang C (2021) A novel hybrid particle swarm optimization using adaptive strategy. Inf Sci 579:231–250

    MathSciNet  Google Scholar 

  • Wang R, Hao K, Wang H, Wang C, Chen L, Xie R (2022) Non-isothermal viscoelastic melt spinning with stress-induced crystallization: numerical simulation and parametric analysis. Int Polym Proc 37:25–37

    CAS  Google Scholar 

  • Xu X, Tang Y, Li J, Hua C, Guan X (2015) Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy. Appl Soft Comput 29:169–183

    Google Scholar 

  • Xu G, Cui Q, Shi X, Ge H, Zhan Z-H, Lee HP, Liang Y, Tai R, Wu C (2019) Particle swarm optimization based on dimensional learning strategy. Swarm Evol Comput 45:33–51

    Google Scholar 

  • Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169–174

    Google Scholar 

  • Yang C, Gao W, Liu N, Song C (2015) Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight. Appl Soft Comput 29:386–394

    Google Scholar 

  • Young-Pyo J, Cox CL (2009) Simulation of multifilament semicrystalline polymer fiber melt-spinning. J Eng Fibers Fabr 4:34–43

    Google Scholar 

  • Zhan Z-H, Zhang J, Li Y, Shi Y-H (2010) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15:832–847

    Google Scholar 

  • Zhang C, Wang C, Wang H, Zhang Y (2007) Multifilament model of PET melt spinning and prediction of as-spun fiber’s quality. J Macromol Sci Part B Phys 46:793–806

    ADS  CAS  Google Scholar 

  • Zhang K, Qiujun H, Zhang Y (2019) Enhancing comprehensive learning particle swarm optimization with local optima topology. Inf Sci 471:1–18

    ADS  Google Scholar 

  • Zhang Y, Liu X, Bao F, Chi J, Zhang C, Liu P (2020) Particle swarm optimization with adaptive learning strategy. Knowl Based Syst 196:105789

    Google Scholar 

  • Zhang C, Li J-Y, Chen C-H, Li Y, Zhan Z-H (2023) Region-based evaluation particle swarm optimization with dual solution libraries for real-time traffic signal timing optimization. In: Proceedings of the genetic and evolutionary computation conference, pp 111–118

  • Zheng Y-L, Ma L-H, Zhang L-Y, Qian J-X (2003) On the convergence analysis and parameter selection in particle swarm optimization. In: Proceedings of the 2003 international conference on machine learning and cybernetics (IEEE Cat. No. 03EX693). IEEE, pp 1802–1807

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Acknowledgements

This work was partly supported by the National Key Research and Development Plan from the Ministry of Science and Technology (2016YFB0302701) and the Graduate Student Innovation Fund of Donghua University (CUSF-DH-D-2021050).

Funding

This work was supported in part by the Fundamental Research Funds for the Central Universities (2232021A-10, 2232022D-08, 2232021D-36), Shanghai Sailing Program, China (Grant No. 22YF1401500), Project Funded by China Postdoctoral Science Foundation (Grant No. 2022M711090), the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission (22CGA36), and the Graduate Student Innovation Fund of Donghua University (CUSF-DH-D-2021050).

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Authors and Affiliations

  1. College of Information Science and Technology, Donghua University, Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Shanghai, 201620, China

    Rui Wang, Kuangrong Hao, Lei Chen, Xiaoyan Liu, Xiuli Zhu & Chenwei Zhao

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  1. Rui Wang
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Contributions

RW: conceptualization, methodology, software, data curation, writing original draft, writing review and editing. KH: funding acquisition, supervision, writing review and editing. LC: data curation. XL: investigation. XZ: writing review and editing. CZ: formal analysis, editing.

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Correspondence to Kuangrong Hao.

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Wang, R., Hao, K., Chen, L. et al. A modified hybrid particle swarm optimization based on comprehensive learning and dynamic multi-swarm strategy. Soft Comput 28, 3879–3903 (2024). https://doi.org/10.1007/s00500-023-09332-0

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