An EDA with swarm intelligence for the multi-objective flexible job-shop problem

The Flexible Job-Shop Problem (FJSP) is one of the most complicated scheduling problems. Particle Swarm Optimization (PSO) is a technique based on the swarm intelligence and Estimation Distribution Algorithms (EDA) are evolutionary algorithms based on probabilistic models. Many evolutionary algorithms have been proposed to solve the FJSP, like DIPSO (Particle Swarm Optimization with Diversity, a hybrid PSO) and SEDA (Simple Estimation of Distribution Algorithm). Both have particular characteristics when searching for a position in the search space and this study, a Simple Estimation Distribution Algorithm with Swarm Intelligence (SEDASI) is proposed for the multi-objective FJSP (MOFJSP). The algorithm is a hybrid evolutionary technique with aspects of PSO and EDA. The results show a good perspective with a small improvement when compared to SEDA.