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. The system has the exploration power of the genetic algorithm and the intensification ability of the Kangaroo algorithm. Because the genetic algorithm generates a population of solutions and the Kangaroo algorithm treats a solution at a time, a parallel version of this one was considered. The TWA problem was 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