Local Search and Initialization in the Firefly Algorithm: Performance Analysis in Solving the Flexible Job-Shop Scheduling Problem

Abstract

Hybrid metaheuristics are becoming a widely used alternative to solve some combinatorial optimization problems such as the Flexible Job-shop Scheduling Problem (FJSP). The inherent complexity of this type of problem requires methods that can find near optimal solutions in a reasonable computational time, since exact methods may be impractical in the real industry because of their exhaustive nature. Here is where metaheuristics, which have been proved to be very time-efficient in providing quality solutions, play a key role. Nevertheless, they also present some shortcomings like premature convergence and local optima stagnation. Hybrid versions are commonly used to avoid these issues and increase its search capability. In this paper, we conduct a comparative study of the performance of the Firefly Algorithm and two variants, one improved with an initialization phase and another that integrates both this initialization and multiple local search structures, in solving state-of-the-art FJSP instances. The study demonstrates how local search and initialization can notably enhance the performance of the algorithm.