Evolutionary Multiobjective Optimization: Qualitative Analysis and Design Implementation
Gary G. Yen · Birkhäuser Boston eBooks · 2003
In this chapter, the author proposes a novel evolutionary approach to multiobjective optimization problems—dynamic multiobjective evolutionary algorithm (DMOEA). In DMOEA, a cell-based rank and density estimation strategy is proposed to efficiently compute dominance and diversity information when the population size varies dynamically. In addition, a population growing strategy and a population declining strategy are designed to determine if an individual will survive or be eliminated based on some qualitative indicators. Meanwhile, an objective space compression strategy is devised to continuously refine the quality of the resulting Pareto front. By examining the selected performance metrics on a recently designed benchmark function, DMOEA is found to be competitive with, or even superior to, five state-of-the-art MOEAs in terms of keeping the diversity of the individuals along the trade-off surface, tending to extend the Pareto front to new areas and finding a well-approximated Pareto optimal front.. Moreover, DMOEA is evaluated by using different parameter settings on the chosen test function to verify its robustness of converging to an optimal population size, if it exists. From simulation results, DMOEA has shown the potential of autonomously determining the optimal population size, which is found insensitive to the initial population size chosen. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.