论文信息 - Optimizing Interval Multi-objective Problems Using IEAs with Preference Direction

Optimizing Interval Multi-objective Problems Using IEAs with Preference Direction

Interval multi-objective optimization problems (MOPs) are popular and important in real-world applications. We present a novel interactive evolutionary algorithm (IEA) incorporating an optimization-cum-decision-making procedure to obtain the most preferred solution that fits a decision-maker (DM)'s preferences. Our method is applied to two interval MOPs and compared with PPIMOEA and the posteriori method, and the experimental results confirm the superiorities of our method.

[1] Jyrki Wallenius,et al. Interactive evolutionary multi-objective optimization for quasi-concave preference functions , 2010, Eur. J. Oper. Res..

[2] Xu Han,et al. A nonlinear interval-based optimization method with local-densifying approximation technique , 2010 .

[3] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4] Xiaoyan Sun,et al. Solving interval multi-objective optimization problems using evolutionary algorithms with preference polyhedron , 2011, GECCO '11.

[5] K. Miettinen,et al. Incorporating preference information in interactive reference point methods for multiobjective optimization , 2009 .

[6] Kalyanmoy Deb,et al. Multiobjective optimization , 1997 .

[7] R. Baker Kearfott,et al. Introduction to Interval Analysis , 2009 .

[8] Kalyanmoy Deb,et al. Interactive evolutionary multi-objective optimization and decision-making using reference direction method , 2007, GECCO '07.