Multi-objective optimization using self-adaptive differential evolution algorithm

－ In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Vicky Ling Huang, Shuguang Z. Zhao, Rammohan Mallipeddi, Ponnuthurai N. Suganthan
• Keywords: Artificial Intelligence, CEC 2009, Constrained Multi-Objective Optimization, Numerical Optimization Problems, Optimization Problem