G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
G-indicator: an m-ary quality indicator for the evaluation of non-dominated sets
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
A novel group search optimizer for multi-objective optimization
Expert Systems with Applications: An International Journal
New quality measures for multiobjective programming
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Improving multiobjective evolutionary algorithm by adaptive fitness and space division
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Energy and locality aware load balancing in cloud computing
Integrated Computer-Aided Engineering
| Hi-index | 0.00 |
A multiobjective programming algorithm may find multiple nondominated solutions. If these solutions are scattered more uniformly over the Pareto frontier in the objective space, they are more different choices and so their quality is better. In this paper, we propose a quality measure called U-measure to measure the uniformity of a given set of nondominated solutions over the Pareto frontier. This frontier is a nonlinear hyper-surface. We measure the uniformity over this hyper-surface in three main steps: 1) determine the domains of the Pareto frontier over which uniformity is measured, 2) determine the nearest neighbors of each solution in the objective space, and 3) compute the discrepancy among the distances between nearest neighbors. The U-measure is equal to this discrepancy where a smaller discrepancy indicates a better uniformity. We can apply the U-measure to complement the other quality measures so that we can evaluate and compare multiobjective programming algorithms from different perspectives.