Interactive Multi-Objective Particle Swarm Optimisation using Decision Space Interaction

被引：0
|
作者：
Heuenhausen, Jan [1 ]
Lewis, Andrew [1 ]
Randall, Marcus [2 ]
Kipouros, Timoleon [3 ]
机构：
[1] Griffith Univ, Inst Integrated & Intelligent Syst, Brisbane, Qld 4111, Australia
[2] Bond Univ, Fac Business, Gold Coast, Australia
[3] Univ Cambridge, Dept Engn, Cambridge, England
来源：
2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2013年
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The most common approach to decision making in multi-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interacts with the algorithm at regular intervals. This paper presents an interactive approach to multi-objective particle swarm optimisation (MOPSO) using a novel technique to preference articulation based on decision space interaction and visual preference articulation. The approach is tested on a 2D aerofoil design case study and comparisons are drawn to non-interactive MOPSO.
引用
收藏
页码：3411 / 3418
页数：8
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