Optimizing different parameters of a discrete firefly algorithm for solving the permutation flow shop problem

The permutation flow shop problem has been known for over 60 years. Many heuristic approaches were already used for solving this type of combinatorial problem. Due to their increasing application in computing designs, flow shop problems became popular again. In 2008, a new nature-inspired algorithm called Firefly Algorithm (FA) was introduced and then extended to tackle these kinds of discrete problems. Recent literature presented experiments, in which the FA outperformed other popular algorithms such as the Particle Swarm Optimization algorithm. In this paper, the parameters of the FA are analyzed and improved for solving the permutation flow shop problem. A quantitative analysis shows that only a few parameters, such as the number of fireflies, have a noticeable influence on the performance of the FA, including its execution time.

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