TY - GEN
T1 - Multi-objective Differential Evolution with adaptive control of parameters and operators
AU - LI, Ke
AU - FIALHO, Álvaro
AU - KWONG, Sam
PY - 2011
Y1 - 2011
N2 - Differential Evolution (DE) is a simple yet powerful evolutionary algorithm, whose performance highly depends on the setting of some parameters. In this paper, we propose an adaptive DE algorithm for multi-objective optimization problems. Firstly, a novel tree neighborhood density estimator is proposed to enforce a higher spread between the non-dominated solutions, while the Pareto dominance strength is used to promote a higher convergence to the Pareto front. These two metrics are then used by an original replacement mechanism based on a three-step comparison procedure; and also to port two existing adaptive mechanisms to the multi-objective domain, one being used for the autonomous selection of the operators, and the other for the adaptive control of DE parameters CR and F. Experimental results confirm the superior performance of the proposed algorithm, referred to as Adap-MODE, when compared to two state-of-the-art baseline approaches, and to its static and partially-adaptive variants. © Springer-Verlag Berlin Heidelberg 2011.
AB - Differential Evolution (DE) is a simple yet powerful evolutionary algorithm, whose performance highly depends on the setting of some parameters. In this paper, we propose an adaptive DE algorithm for multi-objective optimization problems. Firstly, a novel tree neighborhood density estimator is proposed to enforce a higher spread between the non-dominated solutions, while the Pareto dominance strength is used to promote a higher convergence to the Pareto front. These two metrics are then used by an original replacement mechanism based on a three-step comparison procedure; and also to port two existing adaptive mechanisms to the multi-objective domain, one being used for the autonomous selection of the operators, and the other for the adaptive control of DE parameters CR and F. Experimental results confirm the superior performance of the proposed algorithm, referred to as Adap-MODE, when compared to two state-of-the-art baseline approaches, and to its static and partially-adaptive variants. © Springer-Verlag Berlin Heidelberg 2011.
KW - Adaptive operator selection
KW - Differential Evolution
KW - Multi-objective optimization
KW - Parameter control
KW - Tree neighborhood density
UR - http://www.scopus.com/inward/record.url?scp=84868519398&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25566-3_37
DO - 10.1007/978-3-642-25566-3_37
M3 - Conference paper (refereed)
SN - 9783642255656
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 473
EP - 487
BT - Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011, Selected Papers
PB - Springer Berlin Heidelberg
T2 - 5th International Conference on Learning and Intelligent Optimization
Y2 - 17 January 2011 through 21 January 2011
ER -