PERFORMANCE STUDY OF MULTIOBJECTIVE OPTIMIZER METHOD BASED ON GREY WOLF ATTACK TECHNICS

This paper proposes a performance study for the Multiobjective Optimizer based on the Grey Wolf Attack technics (MOGWAT). It is a method of solving multiobjective optimization problems. The method consists of the resolution of an unconstrained single objective optimization problem, which is derived from the aggregation of objective functions by the $\epsilon$-constraint approach and the penalization of constraints by a Lagrangian function. Then, Pareto-optimal solutions are obtained using the stochastic method based on the Grey Wolf Optimizer. To evaluate the method, three theorems have been formulated to demonstrate the convergence of the proposed algorithm and the optimality of the obtained solutions. Six test problems from the literature have been successfully dealt with, and the obtained results have been compared to two other methods. We have evaluated two performance parameters, including the generational distance for the approximation error and the spread for the coverage of the Pareto front. Based on these numerical findings, it can be concluded that MOGWAT outperforms two other methods.