Rank-density based multiobjective genetic algorithm
Haiming Lu, Gary G. Yen · 2003
In this paper, a new evolutionary approach, the rank-density based genetic algorithm (RDGA), to multiobjective optimization problems is proposed. In RDGA, a new ranking method, called an automatic accumulated ranking strategy and a "forbidden region" concept are introduced, completed by the revised adaptive cell density evaluation scheme and rank-density based fitness assignment technique. By examining the selected performance indicators on two benchmark problems, RDGA is found to be statistically competitive with two state-of-the-art multiobjective evolutionary algorithms, in terms of keeping the diversity of the individuals along the trade-off surface, extending the Pareto front to new areas, and finding a well-approximated Pareto optimal front.