Hybridizing Infeasibility Driven and Constrained- Domination Principle with MOEA/D for Constrained Multiobjective Evolutionary Optimization - Technologies and Applications of Artificial Intelligence - page 259

optimization problems. To assess the performance of the proposed approach, real-world
optimization problems of two-stage planetary gear transmission system, as well as three
well-known benchmark problems are used as case studies. The preliminary results indi-
cate that the constraint handling approach of hybridizing infeasibility driven and con-
strained-domination principle is efficient and effective. Since the constraint handling
mechanism is generic, it also can be used in other forms of population based stochastic
algorithms.
References
[1] Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on
Decomposition. IEEE Trans. Evolutionary Computation 11(6), 712-731 (2007)
[2] Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization.
IEEE Trans. Evolutionary Computation 4(3), 284-294 (2000)
[3] 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)
[4] Coello Coello, C.A.: Constraint-handling using an evolutionary multiobjective optimiza-
tion technique. Civil Engineering and Environmental Systems 17(4), 319-346 (2000)
[5] Hamida, S.B., Schoenauer, M.: An adaptive algorithm for constrained optimization prob-
lems. 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)
[6] Cai, Z., Wang, Y.: A Multiobjective Optimization-Based Evolutionary Algorithm for
Constrained Optimization. IEEE Transactions on Evolutionary Computation 10(6),
658-675 (2006)
[7] Ray, T., Singh, H.K., Isaacs, A., Smith, W.: Infeasibility driven evolutionary algorithm
for constrained optimization. In: Mezura-Montes, E. (ed.) Constraint-Handling in Evolu-
tionary Optimization. SCI, vol. 198, pp. 145-165. Springer, Heidelberg (2009)
[8] 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)
[9] 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)
[10] Liu, C.-A.: New Multi-Objective Constrained Optimization Evolutionary Algorithm. In:
Proc. 3rd Int. Conf. Innovative Computing Information and Control ICICIC 2008 (2008)
[11] 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 Computa-
tion CEC 2009, pp. 2913-2918 (2009)
[12] del Castillo, J.M.: The Analytical Expression of the Efficiency of Planetary Gear Trains.
Mechanism and Machine Theory (37), 197-214 (2002)
[13] 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)
[14] 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)
[15] Wang, Y., Cai, Z.: Combining multiobjective optimization withdifferential evolution to
solve constrained optimization problems. IEEE Trans. Evolutionary Computation 16(1),
117-134 (2012a)