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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 349781, 17 pages
Research Article

A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

1School of Science, Southwest University of Science and Technology, Mianyang 621010, China
2Australasian Joint Research Centre for Building Information Modelling School of Built Environment, Curtin University, Perth, WA 6845, Australia
3Department of Housing and Interior Design, Kyung Hee University, Seoul 136701, Republic of Korea
4School of Mathematics, Anhui Normal University, Wuhu 430000, China
5School of Mathematics, Chongqing Normal University, Chongqing 404100, China

Received 12 November 2014; Revised 15 January 2015; Accepted 20 January 2015

Academic Editor: Jianxiong Ye

Copyright © 2015 Qiang Long et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust.