A new multiobjective genetic algorithm with heterogeneous population for solving flowshop scheduling problems

The present paper discusses the application of a new genetic algorithm (GA) featuring heterogeneous population to solve multiobjective flowshop scheduling problems. Many GAs have been developed to solve multiobjective scheduling problems, but they used a non-heterogeneous population approach, which could lead to premature convergence and local Pareto-optimum solutions. Our experiments with a 20-job and 20-machine benchmark problem given in Taillard (1993) show that the heterogeneous multiobjective genetic algorithm (hMGA) developed in this research outperforms NSGA-II (Deb 2001) one of the widely used algorithms with non-heterogeneous population. Moreover, in this paper we also present the comparison of hMGA with another meta-heuristic method, i. e. multi-objective simulated annealing (MOSA), proposed by Varadharajan and Rajendran (2005). This research concludes that hMGA developed in this work is promising as it can produce a new set of Pareto-optimum solutions that have not been found by MOSA before.