Application of Multi-criteria Decision-Making Methods to Select Multi-objective Optimization Based Pareto-Optimal Solutions

This study examines the utilization and comparison of four distinct techniques for multi-criteria decision-making (MCDM). The aim is to select the most favorable pareto-optimal solution derived from multi-objective optimization (MOO). Initially, the set of pareto-optimal solutions is obtained using the fast non-dominated sorting genetic algorithm (NSGA-II). Next, we apply four MCDM techniques such as weighted sum (WS), weighted product (WP), quadratic weighted sum (QWS) and TOPSIS to determine the ranking of the pareto-optimal solutions. This study presents two case studies to demonstrate the comparative analysis of four MCDM techniques on the pareto-optimal solutions. First case study is related with scientific workflow where the cost to perform the scientific workflow should be minimized and reliability should be maximized. Second case study focuses to solve the multi-objective travelling salesman problem (TSP) where the objective is to minimize the costs between two cities. Experimental analysis shows that TOPSIS method outperforms the other three MCDM methods for both of the case studies.