Multi-Objective Binary PSO with Kernel P System on GPU

  • Naeimeh Elkhani Centre for Cyber Security Faculty of Information Sciene and Technology Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia
  • Ravie Chandren Muniyandi Centre for Cyber Security Faculty of Information Sciene and Technology Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia
  • Gexiang Zhang School of Electrical Engineering Southwest Jiaotong University Chengdu 610031 Sichuan, P.R. China


Computational cost is a big challenge for almost all intelligent algorithms which are run on CPU. In this regard, our proposed kernel P system multi-objective binary particle swarm optimization feature selection and classification method should perform with an efficient time that we aimed to settle via using potentials of membrane computing in parallel processing and nondeterminism. Moreover, GPUs perform better with latency-tolerant, highly parallel and independent tasks. In this study, to meet all the potentials of a membrane-inspired model particularly parallelism and to improve the time cost, feature selection method implemented on GPU. The time cost of the proposed method on CPU, GPU and Multicore indicates a significant improvement via implementing method on GPU.


[1] Alelyani, S.; Tang, J.; Liu, H. (2013); Feature Selection for Clustering: A Review, Data Clustering: Algorithms and Applications, 29, 110-121, 2013.

[2] Alhazov, A.; Freund, R.; Heikenwalder, H.; Oswald, M; Rogozhin, Y.; Verlan, S. (2012); Sequential P systems with regular control, Paper presented at the International Conference on Membrane Computing, 2012.

[3] Cabarle, F. G. C.; Adorna, H.; Martinez-Del-Amor, M. A.; Perez-Jimenez, M. J. (2012); Improving GPU simulations of spiking neural P systems, Romanian Journal of Information Science and Technology, 15(1), 5-20, 2012.

[4] Cecilia, J. M.; Garcia, J. M.; Guerrero, G. D.; Martinez-del-Amor, M. A.; Perez-Hurtado, I.; Perez-Jimenez, M. J. (2009), Simulation of P systems with active membranes on CUDA, Briefings in bioinformatics, 11(3), 313-322, 2009.

[5] Cecilia, J. M.; Garcia, J. M.; Guerrero, G. D.; Martinez-del-Amor, M. A.; Perez-Hurtado, I.; Perez-Jimenez, M. J. (2010); Simulating a P system based efficient solution to SAT by using GPUs, The Journal of Logic and Algebraic Programming, 79(6), 317-325, 2010.

[6] Cano, A.; Zafra, A.; Ventura, S. (2010); Solving classification problems using genetic programming algorithms on GPUs, Hybrid Artificial Intelligence Systems, 17-26, 2010.

[7] Dematte, L.; Prandi, D. (2010); GPU computing for systems biology, Briefings in bioinformatics, 11(3), 323-333, 2010.

[8] Elkhani, N.; Chandren Muniyandi, R. (2017); A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework, Journal of Optimization, 2017.

[9] Elkhani, N.; Muniyandi, R. C. (2015); Membrane computing to model feature selection of microarray cancer data, Proceedings of the ASE BigData & SocialInformatics, 2015.

[10] Garcia-Quismondo, M.; Perez-Jimenez, M. J. Implementing ENPS by Means of GPUs for AI Applications, Proc. Beyond AI: Interdisciplinary Aspects of Artificial Intelligence, 27-33, 2011.

[11] Gheorghe, M.; Ceterchi, R.; Ipate, F.; Konur, S.; Lefticaru, R. (2018); Kernel P systems: from modelling to verification and testing, Theoretical Computer Science, 724, 45-60, 2018.

[12] Gheorghe, M.; Ipate, F.; Dragomir, C.; Mierla, L.; Valencia-Cabrera, L.; Garcia-Quismondo, M.; Perez-Jimenez, M. J. (2013); Kernel P Systems-Version I, Membrane Computing, Eleventh Brainstorming Week, BWMC, 97-124, 2013.

[13] Guzzi, P. H.; Agapito, G.; Cannataro, M. (2014); coreSNP: Parallel processing of microarray data, IEEE Transactions on Computers, 63(12), 2961-2974, 2014.

[14] Kentzoglanakis, K.; Poole, M. (2012); A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(2), 358-371, 2012.

[15] Li, J.-M.; Wang, X.-J.; He, R.-S.; Chi, Z.-X. (2007); An efficient fine-grained parallel genetic algorithm based on GPU-accelerated, Network and parallel computing workshops, 2007, NPC workshops, IFIP international conference on, 855-862, 2007.

[16] Liu, J.; Iba, H.; Ishizuka, M. (2001); Selecting informative genes with parallel genetic algorithms in tissue classification, Genome Informatics, 12, 14-23, 2009.

[17] Maroosi, A.; Muniyandi, R. C. (2013); Accelerated simulation of membrane computing to solve the n-queens problem on multi-core, International Conference on Swarm, Evolutionary, and Memetic Computing, 257-267, 2013.

[18] Maroosi, A.; Muniyandi, R. C. (2013); Membrane computing inspired genetic, Journal of Computer Science, 9(2), 264-270, 2013.

[19] Mussi, L.; Daolio, F.; Cagnoni, S. (2011); Evaluation of parallel particle swarm optimization algorithms within the CUDA(TM) architecture, Information Sciences, 181(20), 4642-4657, 2011.

[20] Nobile, M.; Besozzi, D.; Cazzaniga, P.; Mauri, G.; Pescini, D. (2012); A GPU-based multiswarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, 74-85, 2012.

[21] Nobile, M. S.; Besozzi, D., Cazzaniga; P., Pescini, D.; Mauri, G. (2013); Reverse engineering of kinetic reaction networks by means of Cartesian Genetic Programming and Particle Swarm Optimization, Evolutionary Computation (CEC), 2013 IEEE Congress on, 1594- 1601, 2013.

[22] Pospichal, P.; Jaros, J.; Schwarz, J. (2010); Parallel genetic algorithm on the cuda architecture, Applications of Evolutionary Computation, 442-451, 2010.

[23] Sarkar, B. K.; Sana, S. S.; Chaudhuri, K. (2011); Selecting informative rules with parallel genetic algorithm in classification problem, Applied Mathematics and Computation, 218(7), 3247-3264, 2011.

[24] Slavik, M.; Zhu, X.; Mahgoub, I.; Shoaib, M. (2009); Parallel Selection of Informative Genes for Classification, Bioinformatics and Computational Biology. Lecture Notes in Computer Science, 5462, 388-399, 2009.

[25] Van Nguyen, D. K.; Gioiosa, G. (2010); A region-oriented hardware implementation for membrane computing applications, Membrane Computing. WMC 2009. Lecture Notes in Computer Science, 5957, 385-409, 2009.

[26] Zhang, G.; Perez-Jimenez, M. J.; Gheorghe, M. (2017), Real-life applications with membrane computing, (Vol. 25): Springer, 2017.

[27] Zhang, G.; Cheng, J.; Gheorghe, M.; Meng, Q. (2013), A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems, Applied Soft Computing, 13(3), 1528-1542, 2013.

[28] Zhou, Y.; Tan, Y. (2009); GPU-based parallel particle swarm optimization, Evolutionary Computation, 2009, CEC'09. IEEE Congress on, 1493-1500, 2009.
How to Cite
ELKHANI, Naeimeh; MUNIYANDI, Ravie Chandren; ZHANG, Gexiang. Multi-Objective Binary PSO with Kernel P System on GPU. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 3, p. 323-336, may 2018. ISSN 1841-9844. Available at: <>. Date accessed: 04 july 2020. doi:


parallel membrane computing, GPU based membrane computing, kernel P system, parallel multi-objective binary PSO, parallel kernel P system-multi objective binary PSO