Multi-Objective Evolutionary Algorithms: Introducing Bias Among Pareto-Optimal Solutions

Since the beginning of Nineties, research and application of multi-objective evolutionary algorithms (MOEAs) have found increasing attention. This is mainly due to the ability of evolutionary algorithms to find multiple Pareto-optimal solutions in one single simulation run. In this paper, we present an overview of the multi-objective evolutionary algorithms and then discuss a particular algorithm in details. Although MOEAs can find multiple Pareto-optimal solutions, often, users need to impose a particular order of priority to objectives. In this paper, we present a few classical techniques to identify a preferred or a compromised solution, and finally suggest a biased sharing technique which can be used during the optimization phase to find a biased distribution of Pareto-optimal solutions in the region of interest. The results are encouraging and suggest further application of the proposed strategy to more complex multi-objective optimization problems.