Comparison of Genetic Algorithm and Memetic Algorithm for Bicriteria Permutation Flowshop Scheduling Problem


Flowshop scheduling is a well-known research field for many years. As the problem size gets bigger, an analytical solution becomes impossible. Here, heuristic solutions come to the stage. In the literature, generally solutions regarding a multi-objective are developed; and multi-objective is generally used for three machines. In this paper, the weighted mean completion times and weighted mean tardiness flowshop machine scheduling have been considered, so heuristic methods have used: Genetic Algorithms (GA) are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, such pure genetic algorithms that makes them incapable of searching numerous solutions of the problem domain. A Memetic Algorithm (MA) is an extension of the traditional genetic algorithm. That uses a local search technique to reduce the Variable Neighborhood Search (VNS). The methods were tested and gave various experimental results which shows that a pure memetic algorithm performs better than the pure genetic algorithms for such type of NP-Hard combinatorial problem. And the hybrid genetic algorithms versions with VNS, give good solutions better than hybrid MA and both were better than pure algorithms.