Abstract
In recent years, many hybrid metaheuristic approaches have been proposed to solve multiobjective optimization problems (MOPs). In this paper, we present a novel multiobjective algorithm, so-called MOPSOEO, which combines particle swarm optimization (PSO) with extremal optimization (EO) to solve MOPs. The hybrid approach takes full advantage of the exploration ability of PSO and the exploitation ability of EO, which can overcome the premature convergence of PSO when it is applied to MOPs. The proposed approach is validated by using five benchmark functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Experimental results indicate that the approach is highly competitive with the state-of-the-art evolutionary multiobjective algorithms, and thus, MOPSOEO can be considered a viable alternative to solve MOPs.
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
Similar content being viewed by others
References
Coello CAC (2006) Evolutionary multiobjective optimization: a historical view of the field. IEEE Comput Intell Mag 1(1):28–36
Deb K, Pratab A, Agrawal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGAC-II. IEEE Trans Evol Comput 6(2):182–197
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: 1995 IEEE international conference on neural networks. IEEE Press, Australia, pp 1942–1948
Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8:256–279
Reyes-Sierra M, Coello CAC (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2:287–308
Boettcher S, Percus AG (1999) Extremal optimization: methods derived from coevolution. In: 1999 Genetic and evolutionary computation conference. Springer, Orlando , pp 825–C832
Boettcher S, Percus AG (2000) Natures way of optimising. Artif Intell 119:275–286
Yu CL, Lu YZ, Chu J (2013) A hybrid algorithm based on particle swarm optimization and extremal optimization for numerical multi-objective problems. J Convergence Inf Technol
Chen MR, Lu YZ (2008) A novel elitist multiobjective optimization algorithm: multiobjective extremal optimization. Eur J Oper Res 188:637–651
Chen MR, Li X, Zhang X, Lu YZ (2010) A novel particle swarm optimizer hybridized with extremal optimization. Appl Soft Comput 10(2):367–373
Chen MR, Lu YZ, Yang GK (2007) Multiobjective extremal optimization with applications to engineering design. J Zhejiang Univ Sci A 8:1905–1911
Chen MR, Lu YZ, Yang GK (2008) Multiobjective optimization using population-based extremal optimization. J Neural Comput Appl 7:101–109
Chen MR, Weng J, Li X (2009) Multiobjective extremal optimization for portfolio optimization problem. In: 2009 IEEE international conference on intelligent computing and intelligent systems (ICIS 2009). IEEE Press, Shanghai, pp 552–556
Chen MR, Weng J, Li X (2010) A novel multiobjective optimization algorithm for 0/1 multiobjective knapsack problems. In: 4th IEEE conference on industrial electronics and applications (ICIEA 2010). IEEE Press, Taiwan, pp 1511–1516
Yu CL, Lu YZ, Chu J (2012) Multi-objective optimization with combination of particle swarm and extremal optimization for constrained engineering design. WSEAS Trans Syst Control 4(7):129–138
Yu CL, Lu YZ, Chu J (2011) Hybrid multi-objective optimization with particle swarm optimization and extremal optimization for engineering design. In: 2011 IEEE international conference on computer science and automation engineering (CSAE). IEEE Press, Shanghai, pp 776–782
Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant Nos. 61005049, 61373158, 61171124, 51207112, 61301298 and 61272413, Shenzhen Fundamental Research Program of Technology Research and Development Funds under Grant No. JC201105170617A, JC201105170613A, JCYJ20120613161222123, JCYJ20120613115442060 and C201005250085A, the Fok Ying Tung Education Foundation under Grant No. 131066, and the Program for New Century Excellent Talents in University under Grant No. NCET-12-0680.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, MR., Weng, J., Li, X., Zhang, X. (2014). Handling Multiple Objectives with Integration of Particle Swarm Optimization and Extremal Optimization. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-54924-3_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54923-6
Online ISBN: 978-3-642-54924-3
eBook Packages: EngineeringEngineering (R0)