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Hybrid seagull optimization algorithm and its engineering application integrating Yin–Yang Pair idea

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Abstract

Only by balancing the exploitation and exploration ability of the algorithm can the swarm intelligence optimization algorithm have better performance. Therefore, hybrid algorithms have attracted more and more attention in the field of optimization algorithms. Its performance is determined by both intelligent algorithm and constraint-handling technique when the hybrid intelligent algorithm is used to solve constrained optimization problems. In this paper, the original constrained optimization problem is transformed into an unconstrained optimization problem using non-fixed multi-stage mapping penalty function method to deal with the constraint conditions. To improve precision and convergence speed of the seagull optimization algorithm (SOA), the optimal point set theory is introduced to initialize the population individuals. In view of the fact that the Yin–Yang algorithm can avoid falling into local optimum and premature convergence, and balance the imbalance between exploitation and exploration, combined with Yin–Yang Pair (YYP) idea, an improved seagull fusion algorithm named YYPSOA which improves the attack ability of the SOA is proposed. The performance of the proposed algorithm is verified by a set of 23 benchmark functions and CEC2014 benchmark functions used for the single objective real-parameter algorithm competition. Experimental results are compared with other well-known meta-heuristic algorithms. Non-parametric statistical tests on the experimental results demonstrate that YYPSOA provides highly competitive performance in terms of the tested algorithms compared with other optimization algorithms. In addition, the application in large-scale constrained engineering optimization problems shows the practicability and better optimization performance and faster convergence speed of YYPSOA.

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References

  1. Wang J, Li Y, Hu G, Yang MS (2019) Lightweight research in engineering: a review. Appl Sci 9(24):5322. https://doi.org/10.3390/app9245322

    Article  Google Scholar 

  2. Zheng Y, Wang J, Li GC (2014) A collective neurodynamic optimization approach to bound-constrained nonconvex optimization. Neural Netw 55:20–29. https://doi.org/10.1016/j.neunet.2014.03.006

    Article  MATH  Google Scholar 

  3. Dhiman G, Kumar V (2019) Spotted hyena optimizer for solving complex and non-linear constrained engineering problems. Harmony search and nature inspired optimization algorithms. Springer, New York, pp 857–867

    Chapter  Google Scholar 

  4. Dhiman G, Kaur A (2019) A hybrid algorithm based on particle swarm and spotted hyena optimizer for global optimization. In: Bansal J, Das K, Nagar A, Deep K, Ojha A (eds) Soft computing for problem solving advances in intelligent systems and computing, vol 816. Springer, Singapore

    Google Scholar 

  5. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680. https://doi.org/10.1126/science.220.4598.671

    Article  MathSciNet  MATH  Google Scholar 

  6. Goldberg DE (2010) Genetic algorithms in search, optimization, and machine learning. Queen’s University, Belfast

    Google Scholar 

  7. Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: 12th international fuzzy systems association world congress, vol 4529, pp 789–798. https://doi.org/10.1007/978-3-540-72950-1_77

  8. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: A gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  9. Hu G, Wu JL, Li HN, Hu XZ (2020) Shape optimization of generalized developable H-Bézier surfaces using adaptive cuckoo search algorithm. Adv Eng Soft 149:102889. https://doi.org/10.1016/j.advengsoft.2020.102889

    Article  Google Scholar 

  10. Mirjalili S (2015) SCA: A sine cosine algorithm for solving optimization problems. Knowl Based Syst 2015:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  11. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Soft 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  12. Qi CM, Zhou ZB, Sun YC, Song HB, Hu LS, Wang Q (2016) Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification. Neurocomputing 220:181–190. https://doi.org/10.1016/j.neucom.2016.05.103

    Article  Google Scholar 

  13. Jiang QY, Wang L, Lin YY, Hei XH, Yu GL, Lu XF (2017) An efficient multi-objective artificial raindrop algorithm and its application to dynamic optimization problems in chemical processes. Appl Soft Comput 58:354–377. https://doi.org/10.1016/j.asoc.2017.05.003

    Article  Google Scholar 

  14. Tharwat A, Hassanien AE (2018) Chaotic antlion algorithm for parameter optimization of support vector machine. Appl Intell 48:670–686. https://doi.org/10.1007/s10489-017-0994-0

    Article  Google Scholar 

  15. Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl Based Syst 165:169–196. https://doi.org/10.1016/j.knosys.2018.11.024

    Article  Google Scholar 

  16. Niknamfar AH, Niaki STA, Niaki SAA (2017) Opposition-based learning for competitive hub location: a bi-objective biogeography-based optimization algorithm. Knowl Based Syst 128:1–19. https://doi.org/10.1016/j.knosys.2017.04.017

    Article  Google Scholar 

  17. Amirsadri S, Mousavirad SJ, Ebrahimpour-Komleh H (2017) A levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training. Neural Comput Appl 30(12):3707–3720. https://doi.org/10.1007/s00521-017-2952-5

    Article  Google Scholar 

  18. Xu LW, Li YZ, Li KC, Beng GH, Jiang ZQ, Wang C, Liu N (2018) Enhanced moth-flame optimization based on cultural learning and Gaussian mutation. J Bionic Eng 15(4):751–763. https://doi.org/10.1007/s42235-018-0063-3

    Article  Google Scholar 

  19. Marinakis Y, Migdalas A, Sifaleras A (2017) A hybrid particle swarm optimization variable neighborhood search algorithm for constrained shortest path problems. Eur J Oper Res 261(3):819–834. https://doi.org/10.1016/j.ejor.2017.03.031

    Article  MathSciNet  MATH  Google Scholar 

  20. Pathak VK, Singh AK (2017) Form error evaluation of noncontact scan data using constriction factor particle swarm optimization. J Adv Manuf Syst 16(03):205–226. https://doi.org/10.1142/S0219686717500135

    Article  Google Scholar 

  21. Pathak VK, Singh AK (2017) Optimization of morphological process parameters in contactless laser scanning system using modified particle swarm algorithm. Measurement 109:27–35. https://doi.org/10.1016/j.measurement.2017.05.049

    Article  Google Scholar 

  22. Pathak VK, Singh R, Gangwar S (2019) Optimization of three-dimensional scanning process conditions using preference selection index and metaheuristic method – Science Direct. Measurement 146:653–667. https://doi.org/10.1016/j.measurement.2019.07.013

    Article  Google Scholar 

  23. Pathak VK, Srivastava AK (2020) A novel upgraded bat algorithm based on cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems. Eng Comput. https://doi.org/10.1007/s00366-020-01127-3

    Article  Google Scholar 

  24. Gangwar S, Pathak VK (2020) Dry sliding wear characteristics evaluation and prediction of vacuum casted marble dust (MD) reinforced ZA-27 alloy composites using hybrid improved bat algorithm and ANN. Mater Today Commun 25:101615. https://doi.org/10.1016/j.mtcomm.2020.101615

    Article  Google Scholar 

  25. Jia HM, Xing ZK, Song WL (2019) A new hybrid seagull optimization algorithm for feature selection. IEEE Access 7:2169–3536. https://doi.org/10.1109/ACCESS.2019.2909945

    Article  Google Scholar 

  26. Jiang H, Yang Y, Ping W, Dong Y (2020) A novel hybrid classification method based on the opposition-based seagull optimization algorithm. IEEE Access (99), 1-1. https://doi.org/10.1109/ACCESS.2020.2997791

  27. Cao Y, Li Y, Zhang G, Jermsittiparsert K, Razmjooy N (2019) Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Rep 5:1616–1625. https://doi.org/10.1016/j.egyr.2019.11.013

    Article  Google Scholar 

  28. Varun P, Prakash K (2016) Yin–Yang-pair optimization: a novel lightweight optimization algorithm. Eng Appl Artif Intell 54:62–79. https://doi.org/10.1016/j.engappai.2016.04.004

    Article  Google Scholar 

  29. Haupt RL, Haupt SE (1998) Practical genetic algorithm. John Wiley & Sons, New York

    MATH  Google Scholar 

  30. Zhang L, Zhang B (2001) Good point set based genetic algorithm. Chinese J Comp 24(9):917–922. https://doi.org/10.3321/j.issn:0254-4164.2001.09.004

    Article  MathSciNet  Google Scholar 

  31. Wang M, Tang MZ (2016) Novel grey wolf optimization algorithm based on non-linear convergence factor. Appl Res Comput 33(12):3648–3653. https://doi.org/10.3969/j.issn.1001-3695.2016.12.029

    Article  MathSciNet  Google Scholar 

  32. Wei ZL, Zhao H, Li MD, Wang Y (2016) Gray wolf optimization algorithm for non-linear adjustment of control parameter values. J Air Force Eng Univ (Nat Sci Edn). 17(3):68–72. https://doi.org/10.3969/j.issn.1009-3516.2016.03.013

    Article  Google Scholar 

  33. Guo ZZ, Liu R, Gong CQ, Zhao L (2017) Study on improvement of Gray Wolf algorithm. Appl Res Comput 34(12):3603–3606. https://doi.org/10.3969/j.issn.1001-3695.2017.12.019

    Article  Google Scholar 

  34. Wang WC, Xu L, Chau KW, Zhao Y, Xu DM (2021) An orthogonal opposition-based-learning Yin–Yang-pair optimization algorithm for engineering optimization. Eng Comput. https://doi.org/10.1007/s00366-020-01248-9.

  35. Wang WC, Xu L, Chau KW, Zhao Y, Xu DM (2020) Yin-Yang firefly algorithm based on dimensionally Cauchy mutation. Expert Syst Appl 150:113216. https://doi.org/10.1016/j.eswa.2020.113216

    Article  Google Scholar 

  36. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Soft 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  37. Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory and Nanyang Technological University, China and Singapore, Tech. Rep. 201311

  38. 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(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  39. Carrasco J, García S, Rueda MM, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comput 54:100665. https://doi.org/10.1016/j.swevo.2020.100665

    Article  Google Scholar 

  40. Derrac J, García S, Hui S, Suganthan PN, Herrera F (2014) Analyzing convergence performance of evolutionary algorithms: a statistical approach. Inf Sci 289:41–58. https://doi.org/10.1016/j.ins.2014.06.009

    Article  Google Scholar 

  41. Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287. https://doi.org/10.1016/S0045-7825(01)00323-1

    Article  MathSciNet  MATH  Google Scholar 

  42. Wu LH, Wang YN, Zhou SW, Yuan XF (2007) Differential evolution for non-linear constrained optimization using non-stationary multi-stage assignment penalty function. Syst Eng Theory Practice 27(3):128–133. https://doi.org/10.3321/j.issn:1000-6788.2007.03.019

    Article  Google Scholar 

  43. Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method for constrained optimization problem. Front Artif Intell Appl 76:214–220

    Google Scholar 

  44. Liu H, Cai ZX, Wang Y (2009) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640. https://doi.org/10.1016/.asoc.2009.08.031

    Article  Google Scholar 

  45. Chen H, Wang M, Zhao X (2020) A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Appl Math Comput 369:124872. https://doi.org/10.1016/j.amc.2019.124872

    Article  MathSciNet  MATH  Google Scholar 

  46. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35. https://doi.org/10.1007/s00366-011-0241-y

    Article  Google Scholar 

  47. Mezura-Montes E, Coello CAC (2005) Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: International conference on Micai: advances in artificial intell. DBLP, pp 652–662. https://doi.org/10.1007/11579427_66

  48. Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization. Part ii: numerical results. Int J Num Methods Eng 21(9):1601–1623. https://doi.org/10.1002/nme.1620210905

    Article  MATH  Google Scholar 

  49. Kannan BK, Kramer SN (1994) An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411. https://doi.org/10.1115/1.2919393

    Article  Google Scholar 

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Acknowledgements

We thank to the anonymous reviewers for their insightful suggestions and recommendations, which led to the improvements of presentation and content of the paper. This study was funded by the National Natural Science Foundation of China (51875454, 51475366).

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  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, No. 5 South Jinhua Road, Xi’an, 710048, People’s Republic of China

    Jiao Wang, Yan Li & Gang Hu

  2. Department of Applied Mathematics, Xi’an University of Technology, Xi’an, 710054, People’s Republic of China

    Gang Hu

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Wang, J., Li, Y. & Hu, G. Hybrid seagull optimization algorithm and its engineering application integrating Yin–Yang Pair idea. Engineering with Computers 38, 2821–2857 (2022). https://doi.org/10.1007/s00366-021-01508-2

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