Abstract
The dynamic of real-world optimization problems raises new challenges to the traditional particle swarm optimization (PSO). Responding to these challenges, the dynamic optimization has received considerable attention over the past decade. This paper introduces a new dynamic multi-objective optimization based particle swarm optimization (Dynamic-MOPSO). The main idea of this paper is to solve such dynamic problem based on a new environment change detection strategy using the advantage of the particle swarm optimization. In this way, our approach has been developed not just to obtain the optimal solution, but also to have a capability to detect the environment changes. Thereby, Dynamic-MOPSO ensures the balance between the exploration and the exploitation in dynamic research space. Our approach is tested through the most popularized dynamic benchmark’s functions to evaluate its performance as a good method.
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
References
Ben Moussa, S., Zahour, A., Benabdelhafid, A., Alimi, M.A.: New features using fractal multi-dimensions for generalized Arabic font recognition. Pattern Recogn. Lett. 31(5), 361–371 (2010)
Bezine, H., Alimi, M.A., Derbel, N.: Handwriting trajectory movements controlled by a bêta-elliptic model. In: 7th IEEE International Conference on Document Analysis and Recognition, pp. 1228–1232. IEEE, Edinburgh, UK (2003)
Alimi, M.A.: Evolutionary computation for the recognition of on-line cursive handwriting. IETE J. Res. 48(5), 385–396 (2002)
Boubaker, H., Kherallah, M., Alimi, M.A.: New algorithm of straight or curved baseline detection for short Arabic handwritten writing. In: 10th International Conference on Document Analysis and Recognition, pp. 778–782. IEEE, Barcelona, Spain (2009)
Slimane, F., Kanoun, S., Hennebert, J., Alimi, M.A., Ingold, R.: A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution. Pattern Recogn. Lett. 34(2), 209–218 (2013)
Elbaati, A., Boubaker, H., Kherallah, M., Alimi, M.A., Ennaji, A., Abed, H.E.: Arabic handwriting recognition using restored stroke chronology. In: 10th International Conference on Document Analysis and Recognition, pp. 411–415. IEEE, Barcelona, Spain (2009)
Baccour, L., Alimi, M.A., John, R.I.: Similarity measures for intuitionistic fuzzy sets: state of the art. J. Intell. Fuzzy Syst. 24(1), 37–49 (2013)
Fdhila, R., Hamdani, T.M., Alimi, M.A.: Distributed MOPSO with a new population subdivision technique for the feature selection. In: The 5th International Symposium Computational Intelligence and Intelligent Informatics, pp. 81–86. IEEE, Floriana, Malta (2011)
Fdhila, R., Hamdani, T.M., Alimi, M.A.: A multi objective particles swarm optimization algorithm for solving the routing pico-satellites problem. In: Systems, Man, and Cybernetics, pp. 1402–1407. IEEE, Seoul, South Korea (2012)
Fdhila, R., Walha, C., Hamdani, T.M., Alimi, M.A.: Hierarchical design for distributed MOPSO using sub-swarms based on a population pareto fronts analysis for the grasp planning problem. In: The 13th International Conference on Hybrid Intelligent Systems, pp. 203–208. IEEE, Gammarth, Tunisia (2013)
Chouikhi, N., Fdhila, R., Ammar, B., Rokbani, N., Alimi, M.A.: Single-and multi-objective particle swarm optimization of reservoir structure in echo state network. In: The International Joint Conference on Neural Networks, pp. 440–447. IEEE, Vancouver, BC, Canada (2016)
Eberhart, R., Kennedy, J.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway, New Jersey (1995)
Fdhila, R., Hamdani. T., Alimi. M.A.: A new distributed approach for MOPSO based on population Pareto fronts analysis and Dynamic. In: Systems Man and Cybernetics (SMC), pp. 947–954. IEEE, Istanbul (2010)
Fdhila, R., Hamdani, T.M., Alimi, M.A.: A new hierarchical approach for MOPSO based on dynamic subdivision of the population using Pareto fronts. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 947–954. IEEE, Istanbul, Turkey (2010)
Fdhila, R., Hamdani, T.M., Alimi, M.A.: Population-based distribution of MOPSO with continuous flying pareto fronts particles. J. Inf. Process. Syst. (2016, accepted paper)
Fdhila, R., Ouarda, W., Alimi, M.A., Abraham, A.: A new scheme for face recognition system using a new 2-level parallelized hierarchical multi objective particle swarm optimization algorithm. J. Inf. Assur. Secur. 11(6), 385–394 (2016)
Helbig, M., Engelbrecht, A.P.: Dynamic multi-objective optimization using PSO. In: Alba, E., Nakib, A., Siarry, P. (eds.) Metaheuristics for Dynamic Optimization. SCI, vol. 433, pp. 147–188. Springer, Heidelberg (2013). doi:10.1007/978-3-642-30665-5_8
Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: test cases, approximations, and applications. In: Transactions on Evolutionary Computation, pp. 425–442. IEEE, USA (2004)
Fdhila, R., Hamdani, T.M., Alimi, M.A.: Optimization algorithms, benchmarks and performance measures: from static to dynamic environment. In: The 15th International Conference on Intelligent Systems Design and Applications, pp. 597–603. IEEE, Marrakech, Morocco (2015)
Aboud, A., Fdhila, R., Alimi, M.A.: MOPSO for dynamic feature selection problem based big data fusion. In: the IEEE International Conference on Systems, Man, and Cybernetics, pp. 003918–003923. IEEE, Budapest, Hungary (2016)
Fdhila, R., Elloumi, W., Hamdani, T.M.: Distributed MOPSO with dynamic Pareto front driven population analysis for TSP problem. In: the 6th International Conference Soft Computing and Pattern Recognition, pp. 294–299. IEEE, Tunis, Tunisia (2014)
Hu, X., Eberhart, R.: Tracking dynamic systems with PSO: where’s the cheese? In: Proceedings of the workshop on particle swarm optimization. Purdue School of Engineering and Technology. IEEE, Indianapolis (2001)
Du, W., Li, B.: Multi-strategy ensemble particle swarm optimization for dynamic optimization. In: Information Sciences, pp. 3096–3109. Elsevier, Huangshan Road, Hefei, Anhui, China (2008)
Branke, J., Kaussler, T., Smidt, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Parmee, I.C. (ed.) Evolutionary Design and Manufacture. Springer, London (2000). doi:10.1007/978-1-4471-0519-0_24
Dhahri, H., Alimi, M.A.: The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction. In: IEEE International Conference on Neural Networks - Conference Proceedings, pp. 2938–2943. IEEE, Vancouver, BC, Canada (2006)
Bouaziz, S., Dhahri, H., Alimi, M.A., Abraham, A.: A hybrid learning algorithm for evolving flexible beta basis function neural tree model. Neurocomputing 117, 107–117 (2013)
Deb, K., Rao, N.U.B., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007). doi:10.1007/978-3-540-70928-2_60
Chen, H., Li, M., Chen, X.: Using diversity as an additional-objective in dynamic multiobjective optimization algorithms. In: Second International Symposium on Electronic Commerce and Security, pp. 484–487. IEEE, Nanchang City, China (2009)
Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1201–1208. ACM, Seattle, Washington, USA (2006)
Hu, X., Eberhart, R.: Adaptive particle swarm optimisation: detection and response to dynamic systems. In: IEEE Congress on Evolutionary Computation, pp. 1666–1670. IEEE, Honolulu, HI, USA, USA (2002)
Zhou, A., Jin, Y., Zhang, Q.: A population prediction strategy for evolutionary dynamic multiobjective optimization. Trans. Cybern. 44(1), 40–53 (2014)
Acknowledgements
The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Aboud, A., Fdhila, R., Alimi, A.M. (2017). Dynamic Multi Objective Particle Swarm Optimization Based on a New Environment Change Detection Strategy. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)