The most important issue in multi-objective optimization problems is to determine the Pareto points along the Pareto frontier. If the optimization problem involves multiple conflicting objectives, the results obtained from the Pareto-optimality will have the trade-off solutions that shaping the Pareto frontier. Each of these solutions lies at the boundary of the Pareto frontier, such that the i...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

A core challenge ofMultiobjective Evolutionary Algorithms (MOEAs) is to attain evenly distributed Pareto optimal solutions along the Pareto front. In this paper, we propose a novel asymmetric Pareto-adaptive (apa) scheme for the identification of well distributed Pareto optimal solutions based on the geometrical characteristics of the Pareto front. The apa scheme applies to problem with symmetr...