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Effectiveness of new Multiple-PSO based Membrane Optimization Algorithms on CEC 2014 benchmarks and Iris classification

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This paper proposes a new Multiple-PSO based Membrane Algorithm (M_PSO_MA), which is a one-layered parallel and distributed membrane structure with seven different membranes, i.e., a skin membrane containing six elementary membranes using update rules of different PSOs. The idea is inspired from Frankenstein’s PSO (Oca et al. in IEEE Trans Evol Comput 13:1120–1132, 2009) which shows that by integrating components in novel ways effective optimizers can be designed. M_PSO_MA, is designed using the variants of PSO as given in Oca et al. (2009), which although are not hybridized but are applied simultaneously on the same population set. Each elementary membrane sends the best solution and the corresponding particle to the skin membrane, where the best solution is determined among all the collected solutions, which happens to be the best solution of the complete swarm. This global best solution and the corresponding particle are then communicated to all other membranes by the skin membrane besides retaining a copy for itself. The proposed algorithm, M_PSO_MA provides good quality solutions, but may sometime require slightly additional computational time. Therefore, its modified variant namely, MM_PSO_MA is also presented with an objective to minimize the computational time. Both, the proposed variants are compared with the existing best membrane algorithm determined in Singh et al. (Appl Math Comput 246:546–560, 2014) on the basis of CEC-2014 benchmark optimization problem set. In the second part of our paper M_PSO_MA and MM_PSO_MA are applied to solve a real life problem of Iris classification and the results are compared with Mutated PSO, namely MO-MPSO (Wang et al. in Int J Innov Comput Inform Control 9:2963–2977, 2013). The extensive results prove the supremacy of M_PSO_MA over other PSO based Membrane Algorithms. It is concluded that the proposed algorithm can be effectively used for solving complex real life problems as well.

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References

  • Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in Particle Swarm Optimization. In: Proceedings of 2011 third world congress on nature and biologically inspired computing (NaBIC), pp 633–640

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  • Deep K, Thakur M (2007a) A new crossover operator for real coded genetic algorithms. Appl Math Comput 188(1):895–911

    MathSciNet  MATH  Google Scholar 

  • Deep K, Thakur M (2007b) A new mutation operator for real coded genetic algorithms. Appl Math Comput 193(1):211–230

    MathSciNet  MATH  Google Scholar 

  • Eberhart RC, Shi Y (2002) Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the IEEE congress on evolutionary computation 2001, vol 1, pp 94–100

  • Eberhart R, Simpson P, Dobbins R (1996) Computational intelligence PC tools. AP Professional, San Diego, CA, Chap. 6, pp 212–226

  • Feng Y, Teng GF, Wang AX, Yao YM (2007) Chaotic inertia weight in Particle Swarm Optimization. In: Second international conference on innovative computing, information and control, 2007. ICICIC’07. IEEE. p 475

  • Huang L, Wang N (2006) An optimization algorithm inspired by membrane computing. ICNC 2006, Lecture notes in computer science, vol 4222, pp 49–52

  • Iris data set. https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

  • Jiang Y, Peng H, Huang X, Zhang J, Shi P (2014) A novel clustering algorithm based on P systems. Int J Innov Comput Inform Control 10(2):753–765

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle Swarm Optimization. In: Proceedings of international conference on neural networks, pp 1942–1948

  • Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore

  • Luan J, Liu XY (2014) Logic operation in spiking neural P system with chain structure. In: Frontier and future development of information technology in medicine and education. Springer, Netherlands, pp 11–20

  • Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

    Article  Google Scholar 

  • Nishida TY (2004) An application of P-system: a new algorithm for NP-complete optimization problems. In: The 8th world multi-conference on systems, cybernetics and informatics, pp 109–112

  • Oca MAM, Stutzle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evol Comput 13:1120–1132

    Article  Google Scholar 

  • Paun G (1998) Computing with membranes. Technical report. Finland: Turku Center for Computer Science

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

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

  • Singh G, Deep K (2014) Hybridization of P systems and Particle Swarm Optimization for function optimization. In: 3rd international conference on soft computing for problem solving. advances in intelligent systems and computing, vol. 258, pp 395–401

  • Singh G, Deep K, Nagar AK (2014) Cell–like P-systems based on rules of Particle Swarm Optimization. Appl Math Comput 246:546–560

    MathSciNet  MATH  Google Scholar 

  • Thangaraj R, Pant M, Abraham A, Bouvry P (2011) Particle Swarm Optimization: hybridization perspectives and experimental illustrations. J Appl Math Comput 217(12):5208–5226

    Article  MATH  Google Scholar 

  • Wang J, Wang T, Shi P, Tu M, Yang F (2013) Membrane optimization algorithm based on mutated PSO and its application in nonlinear control systems. Int J Innov Comput Inform Control 9:2963–2977

    Google Scholar 

  • Zhang GX, Gheorghe M, Wu CZ (2008) A quantum-inspired evolutionary algorithm based on p systems for a class of combinatorial optimization. Fundam Inform 87:93–116

    MATH  Google Scholar 

  • Zhang G, Gheorghe M, Pan L, Pérez-Jiménez MJ (2014) Evolutionary membrane computing: a comprehensive survey and new results. Inf Sci 279:528–551

    Article  Google Scholar 

Download references

Acknowledgments

The first author would like to acknowledge Ministry of Human Resource Development, Government of India, for funding the research under Grant No. MHRD 02-23-200-304. The authors are thankful to the reviewers for their valuable comments.

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Correspondence to Kusum Deep.

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Singh, G., Deep, K. Effectiveness of new Multiple-PSO based Membrane Optimization Algorithms on CEC 2014 benchmarks and Iris classification. Nat Comput 16, 473–496 (2017). https://doi.org/10.1007/s11047-016-9573-2

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