Solving Multiobjective Optimization Problems using Evolutionary Algorithm

Being capable of finding a set of pareto–optimal solutions in a single run, which is a necessary feature for multi–criteria decision making, Evolutionary Algorithms (EAs) has attracted many researchers and practitioners to address the solution of Multiobjective Optimization Problems (MOPs). In a previous work, we developed a Pareto Differential Evolution (PDE) algorithm to handle multiobjective optimization problems. Despite the overwhelming number of Multiobjective Evolutionary Algorithms (MEAs) in the literature, little work has been done to identify the best MEA using an appropriate assessment methodology. In this paper, we compare our algorithm with twelve other well-known MEAs, using a popular assessment methodology, by solving two bench-mark problems. The comparison shows the superiority of our algorithm over others.