Hybrid Evolutionary Multi-Objective Optimization Algorithm for Helping Multi-Criterion Decision Makers

Abstract

ABSTRACT Obtaining a specific region from the efficient front for multi-objective and practical optimization problems helps decision-makers. Reference point approaches are suggested to reach the region of interest. Many evolutionary algorithms integrated with a reference point idea to obtain apart from the efficient front solutions close to the reference solution. This paper integrated a penalty boundary intersection (PBI) with the non-dominated sorting genetic algorithm (NSGA-II) to reach the efficient front close to the reference point. The proposed approach allows theoretically convergent solutions to be found. Also, using the advantage of PBI, our algorithm is able to reach a specific part for a set multi and many-objective optimization problems effectively. The proposed algorithm compared with other evolutionary algorithms, such as the original reference-based NSGA-II (R-NSGA-II), r-NSGA-II, and g-NSGA-II. The R-metric values show that the proposed algorithm outperforms the compared algorithms. Using the proposed algorithm for solving the multi-objective engineering design problem helps find solutions according to the decision-makers interest.