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research-article

Niching particle swarm optimization techniques for multimodal buckling maximization of composite laminates

Published: 01 August 2017 Publication History

Abstract

Display Omitted 15 global sub-optima of a bucking maximization of composite panels were identified by means of niching PSO techniques.This specific buckling-resistance optimal design of composite panels can be served as a benchmark problem of real-world multimodal optimization.The fitness difference between global optima and global sub-optima of composite structures could be negligible for engineering practices.The ring topology based PSO without any niching parameter is suitable for multimodal optimization of composite structures. It is natural to extend conventional unimodal optimization to challenging multimodal optimization design of composite structures by means of emergent niching particle swarm optimization (PSO), due to multimodal characteristics of composite structures by nature. The advanced multimodal PSO algorithms adopted in the present study include the species-based PSO (SPSO), the fitness Euclidean-distance ratio based PSO (FER-PSO), the ring topology based PSO and the Euclidean distance-based locally informed particle swarm (LIPS) optimizer, which are applied to a multimodal buckling maximization problem of composite panels. SPSO, FER-PSO, the ring topology based PSO and the variant of LIPS succeed to simultaneously indentify not only the first-best-fitness solutions (the global optima) but also the second-best-fitness solutions (the global sub-optima) to this buckling optimization design in a single optimization process. The fifteen second-best-fitness solutions are discovered for the first time and the buckling-resistance difference between the seven first-best-fitness solutions and the fifteen second-best-fitness solutions is definitely negligible from the viewpoint of practical applications. Based on the new findings of twenty-two acceptable solutions, this specific buckling maximization design of composite panels can be served as a benchmark problem of real-world multimodal optimization. In terms of the statistical outcomes on algorithmic performance assessments, the ring topology based PSO without any niching parameter is suitable and reliable for multimodal optimization of composite structures in comparison with SPSO, FER-PSO and the variant of LIPS.

References

[1]
J. Park, J.H. Hwang, C.S. Lee, W. Hwang, Stacking sequence design of composite laminates for maximum strength using genetic algorithms, Compos. Struct., 52 (2001) 217-231.
[2]
R. Kathiravan, R. Ganguli, Strength design of composite beam using gradient and particle swarm optimization, Compos. Struct., 81 (2007) 471-479.
[3]
N.G. Narayana, S.N. Omkar, D. Mudigere, S. Gopalakrishnan, Nature inspired optimization techniques for the design optimization of laminated composite structures using failure criteria, Expert Syst. Appl., 38 (2011) 2489-2499.
[4]
R. Kayikci, F.O. Sonmez, Design of composite laminates for optimum frequency response, J. Sound Vib., 331 (2012) 1759-1776.
[5]
R. Spallino, G. Thierauf, Thermal buckling optimization of composite laminates by evolution strategies, Comput. Struct., 78 (2000) 691-697.
[6]
Z.K. Awad, T. Aravinthan, Y. Zhuge, F. Gonzalez, A review of optimization techniques used in the design of fibre composite structures for civil engineering applications, Mater. Des., 33 (2012) 534-544.
[7]
S. Das, S. Maity, B.Y. Qu, P.N. Suganthan, Real-parameter evolutionary multimodal optimization a survey of the state-of-the-art, Swarm Evol. Comput., 1 (2011) 71-88.
[8]
J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.
[9]
K.E. Parsopoulos, M.N. Vrahatis, Recent approaches to global optimization problems through particle swarm optimization, Nat. Comput., 1 (2002) 235-306.
[10]
R. Brits, A.P. Engelbrecht, F.V. Bergh, A niching particle swarm optimizer, in: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, 2002, pp. 692-696.
[11]
A. Nickabadi, M.M. Ebadzadeh, R. Safabakhsh, DNPSO: a dynamic niching particle swarm optimizer for multi-modal optimization, in: evolutionary Computation, in: IEEE World Congress on Computational Intelligence (CEC 2008), 2008, pp. 26-32.
[12]
A. Passaro, A. Starita, Particle swarm optimization for multimodal functions: a clustering approach, Journal of Artificial Evolution and Applications., 2008 (2008) 1-15.
[13]
J.H. Seo, C.H. Im, C.G. Heo, J.K. Kim, H.K. Jung, C.G. Lee, Multimodal function optimization based on particle swarm optimization, IEEE Trans. Magn., 42 (2006) 1095-1098.
[14]
Y. Liu, X. Ling, Z. Shi, M. Lv, J. Fang, L. Zhang, A Survey on particle swarm optimization algorithms for multimodal function optimization, J. Softw., 6 (2011) 2449-2455.
[15]
X. Li, Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization, In: Genetic and Evolutionary Computation, Berlin: Springer, 2004.
[16]
D. Beasley, D.R. Bull, R.R. Martin, A sequential niche technique for multimodal function optimization, Evol. Comput., 1 (1993) 101-125.
[17]
J.P. Li, M.E. Balazs, G.T. Parks, P.J. Clarkson, A species conserving genetic algorithm for multimodal function optimization, Evol. Comput., 10 (2002) 207-234.
[18]
S. Bird, X. Li, Adaptively choosing niching parameters in a PSO, in: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation Conference, 2006, pp. 3-10.
[19]
X. Li, A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio, in: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation Conference, 2007, pp. 78-85.
[20]
X. Li, Niching without niching parameters: particle swarm optimization using a ring topology, IEEE Trans. Evol. Comput., 14 (2010) 150-169.
[21]
B.Y. Qu, P.N. Suganthan, S. Das, A distance-Based locally informed particle swarm model for multimodal optimization, IEEE Trans. Evol. Comput., 17 (2013) 387-402.
[22]
D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997) 67-82.
[23]
F.R. Fulginei, A. Salvini, Comparative analysis between modern heuristics and hybrid algorithms, COMPEL: Int. J. Comput. Math. Electr. Electron. Eng., 26 (2007) 259-268.
[24]
E.J. Griffiths, P. Orponen, Optimization, block designs and no free lunch theorems, Inf. Process. Lett., 94 (2005) 55-61.
[25]
D.B. Chen, C.X. Zhao, Particle swarm optimization with adaptive population size and its application, Appl. Soft Comput., 9 (2009) 39-48.
[26]
A. Khare, S. Rangnekar, A review of particle swarm optimization and its applications in solar photovoltaic system, Appl. Soft Comput., 13 (2013) 2997-3006.
[27]
P.W. Jansen, R.E. Perez, Constrained structural design optimization via a parallel augmented Lagrangian particle swarm optimization approach, Comput. Struct., 89 (2011) 1352-1366.
[28]
M. Marinaki, Y. Marinakis, G.E. Stavroulakis, Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization, Expert Syst. Appl., 38 (2011) 6872-6883.
[29]
M.S. Innocente, S.M.B. Afonso, J. Sienz, H.M. Davies, Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields, Appl. Soft Comput., 34 (2015) 463-484.
[30]
I. Fister, M. Perc, K. Ljubi, S.M. Kamal, A. Iglesias I.Fister, Particle swarm optimization for automatic creation of complex graphic characters, Chaos Solitons Fract., 73 (2015) 29-35.
[31]
S. Hajforoosh, M.A.S. Masoum, S.M. Islam, Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization, Electr. Power Syst. Res., 128 (2015) 19-29.
[32]
R.L. Riche, R.T. Haftka, Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm, AIAA J., 31 (1993) 951-956.
[33]
G. Soremekun, Z. Gurdal, R.T. Haftka, L.T. Watson, Composite laminate design optimization by genetic algorithm with generalized elitist selection, Comput. Struct., 79 (2001) 131-143.
[34]
O. Erdal, F.O. Sonmez, Optimum design of composite laminates for maximum buckling load capacity using simulated annealing, Compos. Struct., 71 (2005) 45-52.
[35]
R. Thangaraj, M. Pant, A. Abraham, P. Bouvry, Particle swarm optimization: hybridization perspectives and experimental illustrations, Appl. Math. Comput., 217 (2011) 5208-5226.
[36]
Y. Shi, R. Eberhart, Parameter selection in particle swarm optimization, in: Evolutionary Programming VII, 1998, pp. 591-600.
[37]
K. Deb, D.E. Goldberg, An investigation of niche and species formation in genetic function optimization, in: Proceedings of the 3rd International Conference on Genetic Algorithms, 1989, pp. 42-50.
[38]
K. Veeramachaneni, T. Peram, C. Mohan, L. Osadciw, Optimization using particle swarm with near neighbor interactions, in: Proceedings of Genetic and Evolutionary Compotation Conference, 2003, pp. 110-121.
[39]
R. Mendes, J. Kennedy, J. Neves, The fully informed particle swarm: simpler, maybe better, IEEE Trans. Evol. Comput., 8 (2004) 204-210.

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  1. Niching particle swarm optimization techniques for multimodal buckling maximization of composite laminates

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      Published In

      cover image Applied Soft Computing
      Applied Soft Computing  Volume 57, Issue C
      August 2017
      746 pages

      Publisher

      Elsevier Science Publishers B. V.

      Netherlands

      Publication History

      Published: 01 August 2017

      Author Tags

      1. Buckling resistance
      2. Composite structures
      3. Multimodal optimization design
      4. Niching particle swarm optimization

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      • (2023)Adaptive niching particle swarm optimization with local search for multimodal optimizationApplied Soft Computing10.1016/j.asoc.2022.109923133:COnline publication date: 1-Jan-2023
      • (2022)Ranking-based biased learning swarm optimizer for large-scale optimizationInformation Sciences: an International Journal10.1016/j.ins.2019.04.037493:C(120-137)Online publication date: 20-Apr-2022
      • (2022)Self-adaptive salp swarm algorithm for optimization problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07280-926:18(9349-9368)Online publication date: 1-Sep-2022
      • (2021)KDT-SPSOApplied Soft Computing10.1016/j.asoc.2021.107156103:COnline publication date: 30-Dec-2021
      • (2020)Fibonacci multi-modal optimization algorithm in noisy environmentApplied Soft Computing10.1016/j.asoc.2019.10587488:COnline publication date: 1-Mar-2020

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