PERFORMANCE STUDY OF MULTIOBJECTIVE OPTIMIZER METHOD BASED ON GREY WOLF ATTACK TECHNICS30

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Résumé

This paper proposes a performance study for the Multiobjective Op-
timizer based on the Grey Wolf Attack technics (MOGWAT). It is a method of
solving multiobjective optimization problems. The method consists of the reso-
lution of an unconstrained single objective optimization problem, which is de-
rived from the aggregation of objective functions by the ϵ-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 suc-
cessfully dealt with, and the obtained results have been compared to two other
methods. We have evaluated two performance parameters, including the gen-
erational 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.

Mots-clés

Multiobjective optimization, Grey Wolf Optimizer, Pareto optimality.