۱- Introduction

۲- Related work

۳- Proposed work

۴- Comparative study

۵- Conclusion

References

Abstract

The Firefly algorithm is a population-based optimization algorithm. It has become popular in the field of optimization and has been applied to engineering practices. Recent works have failed to address how to find the global minimum because their algorithm was trapped in the local minimum. Also, they were not able to provide a balance between exploration and exploitation. In this paper, the Tidal Force formula has been applied to modify the Firefly algorithm, which describes the effect of a massive body that gravitationally affects another massive body. The proposed algorithm brings a new strategy into the optimization field. It is applied by using exploitation (Tidal Force) and keeping a balance between the exploration and exploitation on function suitability. Plate shaped, Steep Ridges, Unimodal and Multimodal benchmark functions were used to compare experimental results. The study findings indicate that the Tidal Force Firefly algorithm outperforms the other existing modified Firefly algorithms.

Introduction

Population-based optimization techniques have become widespread in the last two decades. Optimization problems are examined in a variety of fields, which consist of highly nonlinear, multimodal, multidimensional, and differentiable functions. However, traditional optimization techniques have not been able to solve these problems. The population-based techniques, with their own robustness and flexible behavior in solving optimization problems, bring novel insights in order to solve the problems instead of using traditional optimization techniques. The PSO algorithm is inspired by birds’ behavior [1], which defines that all birds move towards the best bird. The PSO works with two populations, such as best position and current positions. Diversity solutions are one of the advantages of the PSO, which performs better than the single point algorithms. An ant colony is another popular algorithm that was designed for optimization problems. The algorithm is based on the behavior of ants and was proposed by Dorigo. Ants search for a best path solution between their colony and a food source. Each ant randomly moves towards a destination ant. The paths are followed by ants, based on the probability of pheromones [2]. Differential evolution based on individual’s differences (called the DE algorithm) is similar to the GA algorithm that defines specified crossover, mutation and selection [3]. It computes parallels and takes the best result in a ∗ Corresponding author. E-mail addresses: arefyelghi@ktu.edu.tr (A. Yelghi), ckose@ktu.edu.tr (C. Köse). few dimensions. The Harmony Search (HS) algorithm was modeled by taking inspiration from the improvisation of musicians [4]. The algorithm uses musical Pitch Range, Harmony, Aesthetics, Practice and experience in the algorithm, which links to decision variables, iteration concepts and so on. The harmony (solution) is produced randomly and checks with stored solutions to place better solutions in their place.