Modified Firefly Algorithm using Iterated Descent Method to Solve Machine Scheduling Problems

Abstract

One of the most efficient metaheuristic algorithms that is used to solve hard optimization problems is the firefly algorithm (FFA). In this paper we use this algorithm to solve a single machine scheduling problem, we aim to minimize the sum of the two cost functions: the maximum tardiness and the maximum earliness. This problem (P) is NP-hard so we solve this problem using FFA as a metaheuristic algorithm. To explore the search space and get a good solution to a problem (Q), we hybridize FFA by Iterated Descent Method (IDM) in three ways and the results are FFA1, FFA2, and FFA3. In the computational test, we evaluate these algorithms (FFA, FFA1, FFA2, FFA3) compared with the genetic algorithm (GA) through a simulation process with job sizes from 10 jobs to 100 jobs. The results indicate that these modifications improve the performance of the original FFA and one of them (FFA3) gives better performance than others.