Abstract
This paper presents a novel multiobjective constraint handling approach, named as MOEA/D-CDP-ID, to tackle constrained optimization problems. In the proposed method, two mechanisms, namely infeasibility driven (ID) and constrained-domination principle (CDP) are embedded into a prominent multiobjective evolutionary algorithm called MOEA/D. Constrained-domination principle defined a domination relation of two solutions in constraint handling problem. Infeasibility driven preserves a proportion of marginally infeasible solutions to join the searching process to evolve offspring. Such a strategy allows the algorithm to approach the constraint boundary from both the feasible and infeasible side of the search space, thus resulting in gaining a Pareto solution set with better distribution and convergence. The efficiency and effectiveness of the proposed approach are tested on several well-known benchmark test functions. In addition, the proposed MOEA/D-CDP-ID is applied to a real world application, namely design optimization of the two-stage planetary gear transmission system. Experimental results suggest that MOEA/D-CDP-ID can outperform other state-of-the-art algorithms for constrained multiobjective evolutionary optimization.
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
Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Trans. Evolutionary Computation 11(6), 712–731 (2007)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evolutionary Computation 4(3), 284–294 (2000)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation 6(2), 182–197 (2002)
Coello Coello, C.A.: Constraint-handling using an evolutionary multiobjective optimization technique. Civil Engineering and Environmental Systems 17(4), 319–346 (2000)
Hamida, S.B., Schoenauer, M.: An adaptive algorithm for constrained optimization problems. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 529–538. Springer, Heidelberg (2000)
Cai, Z., Wang, Y.: A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization. IEEE Transactions on Evolutionary Computation 10(6), 658–675 (2006)
Ray, T., Singh, H.K., Isaacs, A., Smith, W.: Infeasibility driven evolutionary algorithm for constrained optimization. In: Mezura-Montes, E. (ed.) Constraint-Handling in Evolutionary Optimization. SCI, vol. 198, pp. 145–165. Springer, Heidelberg (2009)
Hu, Q., Min, R.: Mult-i objective optimal design study of the two-stage planetary gear transmission system s based on the MATLAB. Modern Manufacturing Engineering (3), 98–101 (2008)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance Assessment of Multiobjective Optimizers: An Analisis and Review. Computer Engineering and Networks Laboratory, ETH Zurich, Tech. Rep. 139 (June 2002)
Liu, C.-A.: New Multi-Objective Constrained Optimization Evolutionary Algorithm. In: Proc. 3rd Int. Conf. Innovative Computing Information and Control ICICIC 2008 (2008)
Liu, M., Zou, X., Chen, Y., Wu, Z.: Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances. In: Proc. IEEE Congress Evolutionary Computation CEC 2009, pp. 2913–2918 (2009)
del Castillo, J.M.: The Analytical Expression of the Efficiency of Planetary Gear Trains. Mechanism and Machine Theory (37), 197–214 (2002)
Coello Coello, C.A., Cruz Cort´es, N.: Solving Multiobjective Optimization Problems using an Artificial Immune System. Genetic Programming and Evolvable Machines 6(2), 163–190 (2005)
Schütze, O., Esquivel, X., Lara, A., Coello, C.A.C.: Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans. Evolutionary Computation 16(4), 504–522 (2012)
Wang, Y., Cai, Z.: Combining multiobjective optimization withdifferential evolution to solve constrained optimization problems. IEEE Trans. Evolutionary Computation 16(1), 117–134 (2012a)
Wang, Y., Cai, Z.: A dynamic hybrid framework for constrained evolutionary optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(1), 203–217 (2012b)
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evolutionary Computation 13(2), 284–302 (2009)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Meth. Appl. Mech. Eng. 186, 311–338 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Lin, H. et al. (2014). Hybridizing Infeasibility Driven and Constrained-Domination Principle with MOEA/D for Constrained Multiobjective Evolutionary Optimization. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-13987-6_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
eBook Packages: Computer ScienceComputer Science (R0)