Grey Wolves Attack Process for the Pareto Optimal Front Construction in the Multiobjective Optimization


  • Wendinda Bamogo
  • Kounhinir Some Université Norbert ZONGO
  • Joseph Poda



Optimization multiobjective, Metaheuristics, Pareto optimality


We propose a new metaheuristic, HmGWOGA-MO, for solving multiobjective optimization problems operating with a population of solutions. The method is a hybridization of the HmGWOGA method, which is a single objective optimization method, and the ϵ-constraint approach, which is an aggregation technique. The ϵ-constraint technique is one of the best ways to transform a problem with many objective functions into a single objective problem because it works even if the problem has any kind of Pareto optimal front. Previously, the HmGWOGA method was designed to optimize a positive single-objective function without constraints. The obtained solutions are good. That is why, in this current work, we combined have it with the ϵ-constraint approach for the resolution of multiobjective optimization problems. Our new method proceeds by transforming a given multiobjective optimization problem with constraints into an unconstrained optimization of a single objective function. With the HmGWOGA method, five different test problems with varying Pareto fronts have been successfully solved, and the results are compared with those of NSGA-II regarding convergence towards the Pareto front and the distribution of solutions on the Pareto front. This numerical study indicates that HmGWOGA-MO is the best choice for solving a multiobjective optimization problem when convergence is the most important performance parameter.






Nonlinear Analysis

How to Cite

Grey Wolves Attack Process for the Pareto Optimal Front Construction in the Multiobjective Optimization. (2023). European Journal of Pure and Applied Mathematics, 16(1), 595-608.

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