A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design

Abstract— Designing efficient algorithms for difficult multiobjective optimization problems is a very challenging problem. In this paper a new clustering multi-objective evolutionary algorithm based on orthogonal and uniform design is proposed. First, the orthogonal design is used to generate initial population of points that are scattered uniformly over the feasible solution space, so that the algorithm can evenly scan the feasible solution space once to locate good points for further exploration in subsequent iterations. Second, to explore the search space efficiently and get uniformly distributed and widely spread solutions in objective space, a new crossover operator is designed. Its exploration focus is mainly put on the sparse part and the boundary part of the obtained non-dominated solutions in objective space. Third, to get desired number of well distributed solutions in objective space, a new clustering method is proposed to select the non-dominated solutions. Finally, ex...

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Yuping Wang, Chuangyin Dang, Hecheng Li, Lixia Han, Jingxuan Wei
• Keywords: Algorithm, Artificial Intelligence, CEC 2009, Feasible Solution Space, Objective Space