A fast algorithm to derive Pareto Optimal Solutions
when we are dealing with multi-criteria and multi-objective problems in meta-heuristic algorithms, deriving pareto optimal solutions is one of the techniques to choose the best answers (optimal solutions). These solutions are neither globally dominant nor completely non-dominant. Here, a Java class is prepared to for this technique, using the costs matrix. Rows are the solutions and columns are the costs in different objective functions for minimization. This method of achieving pareto optimal solutions is derived from the following research:
Deb, Kalyanmoy, et al. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE transactions on evolutionary computation 6.2 (2002): 182-197.