Multi Objective Gravitational Search Algorithm Using Non-dominated Fronts

Nowadays, many techniques based on the heuristic optimization algorithms have been presented for multi-objective optimization problems. Although these techniques don’t guarantee to find the Pareto optimal front, they quest to achieve a good approximation of this set. In this paper, a technique based on gravitational search algorithm (GSA) is presented for optimization of multi-objective problem named multi-objective optimization gravitational search with non-dominated fronts (NFMOGSA). In the proposed method, the non-dominated front and the crowding distance of solutions are used to fitness assignment and diversity preservation. To evaluate the proposed algorithm, some experiments are done and the NFMOGSA is applied to the standard benchmark functions, SCH, KUR, FON, POL, ZDT1, ZDT2, ZDT3 and ZDT6. The results of applying NFMOGSA on the standard benchmark functions and comparison with other well known multi-objective optimization techniques confirm the efficiency and effectiveness of our technique in the multi-objective optimization problems.