Develop a sub-population Memetic Algorithm for multi-objective scheduling problems

Memetic Algorithm is a population-based approach for heuristic search in optimization problems. It has shown that this mechanic performs better than traditional Genetic Algorithms for some problem. In order to apply in the multi-objective problem, the basic local search heuristics are combined with crossover operator in the sub-population in this research. This approach proposed is named as Sub-population with Memetic Algorithm, which is applied to deal with multi-objective Flowshop Scheduling Problems. Besides, the Artificial Chromosome with probability matrix will be introduced when the algorithm evolves to certain iteration for injecting to individual to search better combination of chromosomes, this mechanism will make faster convergent time for evolving. Compares with MOSA, the experiments result show that this algorithm possess fast convergence and average scatter of Pareto solutions simultaneously for solving multi-objective Flowshop Scheduling Problems in test instances.