Some aspects concerning the implementation of a parallel hybrid metaheuristic

This paper presents a discrete optimization system implementing a parallel hybrid metaheuristic. This is obtained by joining a genetic algorithm and a parallel version of a stochastic descent method called ‘Kangaroo’. Two real problems in the manufacturing field were solved using the proposed metaheuristic. This offered the opportunity to underline some aspects regarding the implementation of this hybrid system. The impact of the precedence constraints upon the implementation of the genetic operators (crossover and mutation) is also considered.