Incremental population firefly optimization algorithm

The Firefly Algorithm (FA) is a metaheuristic optimization algorithm inspired by the light-emitting and mate-finding behaviors of fireflies in nature. In this study, Incremental Population Firefly Optimization Algorithm (IFA) was developed to reduce the probability of the FA getting stuck in local optima and to enhance its optimization performance. The concept of incremental population refers to the periodic inclusion of new solutions into the population. Various methods are employed to determine the new solutions to be added. In this study, four different methods were used to generate new solutions, which were then incorporated into the population at intervals of 5 and 10 iterations. Additionally, to investigate the impact of social learning on IFA, a variant called the Social Learning-Based Incremental Population Firefly Optimization Algorithm (SLIFA) was implemented, where the increment process begins after the 100th iteration. According to the obtained results, it was demonstrated that the IFA and SLIFA yielded better performance than the standard FA in many scenarios.