TY - GEN
T1 - T-MOEA/D
T2 - 2010 International Conference of Information Science and Management Engineering, ISME 2010
AU - Liu, Hai Lin
AU - Gu, Fang Qing
AU - CHEUNG, Yiu Ming
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - To approximate the Pareto optimal solutions of a multi-objective optimization problem, Zhang and Li [8] have recently developed a novel multi-objective evolutionary algorithm based on decomposition (MOEA/D). It can work well if the curve shape of the Pareto-optimal front is friendly. Otherwise, it might fail. In this paper, we propose an improved MOEA/D algorithm (denoted as TMOEA/D), which utilizes a monotonic increasing function to transform each individual objective function into the one so that the curve shape of the non-dominant solutions of the transformed multi-objective problem is close to the hyper-plane whose intercept of coordinate axes is equal to one in the original objective function space. Consequently, we can approximate the Pareto optimal solutions that are uniformly distributed over the Pareto front using the advanced decomposition technique of MOEA/D. Numerical results show that the proposed algorithm has a good performance.
AB - To approximate the Pareto optimal solutions of a multi-objective optimization problem, Zhang and Li [8] have recently developed a novel multi-objective evolutionary algorithm based on decomposition (MOEA/D). It can work well if the curve shape of the Pareto-optimal front is friendly. Otherwise, it might fail. In this paper, we propose an improved MOEA/D algorithm (denoted as TMOEA/D), which utilizes a monotonic increasing function to transform each individual objective function into the one so that the curve shape of the non-dominant solutions of the transformed multi-objective problem is close to the hyper-plane whose intercept of coordinate axes is equal to one in the original objective function space. Consequently, we can approximate the Pareto optimal solutions that are uniformly distributed over the Pareto front using the advanced decomposition technique of MOEA/D. Numerical results show that the proposed algorithm has a good performance.
KW - Evolutionary algorithm
KW - Multi-objective optimization
KW - Pareto front
KW - Uniformly distribution
UR - http://www.scopus.com/inward/record.url?scp=78049332539&partnerID=8YFLogxK
U2 - 10.1109/ISME.2010.274
DO - 10.1109/ISME.2010.274
M3 - Conference contribution
AN - SCOPUS:78049332539
SN - 9780769541327
T3 - Proceedings - 2010 International Conference of Information Science and Management Engineering, ISME 2010
SP - 282
EP - 285
BT - Proceedings - 2010 International Conference of Information Science and Management Engineering, ISME 2010
Y2 - 7 August 2010 through 8 August 2010
ER -