Title :
A new technique for assessing the diversity of close-Pareto-optimal front
Author :
Zeng, Sanyou ; Chen, Guang ; Wang, Rui ; Li, Hui ; Shi, Hui ; Ding, Lixin ; Kang, Lishan
Author_Institution :
Dept. of Comput. Sci., China Univ. of Geosci., Wuham
Abstract :
The quality of an approximation set usually includes two aspects-- approaching distance and spreading diversity. This paper introduces a new technique for assessing the diversity of an approximation to an exact Pareto-optimal front. This diversity is assessed by using an ldquoexposure degreerdquo of the exact Pareto-optimal front against the approximation set. This new technique has three advantages: Firstly, The ldquoexposure degreerdquo combines the uniformity and the width of the spread into a direct physical sense. Secondly, it makes the approaching distance independent from the spreading diversity at the most. Thirdly, the new technique works well for problems with any number of objectives, while the widely used diversity metric proposed by Deb would work poor in problems with 3 objectives or over. Experimental computational results show that the new technique assesses the diversity well.
Keywords :
Pareto optimisation; approximation theory; approaching distance; approximation set; close-Pareto-optimal front; diversity metric; exposure degree; spreading diversity; Evolutionary computation; Pareto optimization; Quality assessment; Multi-objective Evolutionary Algorithm; Pareto optimal front; Quality assessment; approximation set; multi-objective optimization;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4630820