Abstract
This paper proposes a Population-P-Systems-inspired Membrane Algorithm (PPSMA) for multi-objective optimization. In the algorithm, the cells of population P systems are divided into two groups to implement different functions and the communications among cells are performed at two levels in order to obtain well converged and distributed solution set. Moreover, differential evolution is employed as search operator in PPSMA. Twelve multi-objective benchmark problems are utilized to test algorithm performance. Experimental results show that PPSMA performs better than five compared algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Nishida, T.Y.: An Approximate Algorithm for NP-complete Optimization Problems Exploiting P Systems. In: BWMC, pp. 185–192 (2004)
Zhang, G., Gheorghe, M., Wu, C.Z.: A Quantum-Inspired Evolutionary Algorithm based on P Systems for Knapsack Problem. Fund. Inform. 87, 93–116 (2008)
Liu, C., Zhang, G., Liu, H., Gheorghe, M., Ipate, F.: An Improved Membrane Algorithm for Solving Time-Frequency Atom Decomposition. In: Păun, G., Pérez-Jiménez, M.J., Riscos-Núñez, A., Rozenberg, G., Salomaa, A. (eds.) WMC 2009. LNCS, vol. 5957, pp. 371–384. Springer, Heidelberg (2010)
Zhang, G., Cheng, J., Gheorghe, M., Meng, Q.: A Hybrid Approach based on Differential Evolution and Tissue Membrane Systems for Solving Constrained Manufacturing Parameter Optimization Problems. Appl. Soft Comput. 13, 1528–1542 (2013)
Zhang, G., Rong, H., Cheng, J., Qin, Y.: A Population-Membrane-System-Inspired Evolutionary Algorithm for Distribution Network Reconfiguration. Chinese J. Electron. 23, 437–441
Zhang, G., Gheorghe, M., Pan, L., Pérez-Jiménez, M.J.: Evolutionary Membrane Computing: A Comprehensive Survey. Inform Sciences 279, 528–551 (2014)
Bernardini, F., Gheorghe, M.: Population P systems. J. Univers. Comput. Sci. 10, 509–539 (2004)
Storn, R., Price, K.: Differential evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Global Optim. 11, 341–359 (1997)
Zhang, J.Q., Sanderson, A.C.: JADE: Adaptive Differential Evolution with Optional External Archive. IEEE T. Evolut. Comput. 13, 945–958 (2009)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE T. Evolut. Comput. 6, 182–197 (2002)
Kukkonen, S., Lampinen, J.: GDE3: The Third Evolution Step of Generalized Differential Evolution. In: IEEE CEC, pp. 443–450 (2005)
Robic, T., Filipic, B.: DEMO: Differential Evolution for Multiobjective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)
Deb, K., Mohan, M., Mishra, S.: Evaluating the ε-domination based Multi-objective Evolutionary Algorithm for a Quick Computation of Pareto-optimal Solutions. Evol. Comput. 13, 501–525 (2005)
Li, H., Zhang, Q.F.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE T. Evolut. Comput. 13, 284–302 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, J., Zhang, G., Qin, Y. (2014). A Population-P-Systems-Inspired Membrane Algorithm for Multi-objective Optimization. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-662-45049-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45048-2
Online ISBN: 978-3-662-45049-9
eBook Packages: Computer ScienceComputer Science (R0)