Is the Fitness Dependent Optimizer Ready for the Future of Optimization?

TL;DR

This paper reviews the Fitness Dependent Optimizer (FDO), analyzing its variations, applications, and performance to determine its readiness for future optimization challenges and suggest directions for improvement.

Contribution

It provides a comprehensive review and systematic analysis of FDO, highlighting its strengths, weaknesses, and potential future research avenues.

Findings

FDO shows competitive performance in various optimization problems.

Variations of FDO improve efficiency and solution quality.

Identifies key challenges and future research directions for FDO.

Abstract

Metaheuristic algorithms are optimization methods that are inspired by real phenomena in nature or the behavior of living beings, e.g., animals, to be used for solving complex problems, as in engineering, energy optimization, health care, etc. One of them was the creation of the Fitness Dependent Optimizer (FDO) in 2019, which is based on bee-inspired swarm intelligence and provides efficient optimization. This paper aims to introduce a comprehensive review of FDO, including its basic concepts, main variations, and applications from the beginning. It systematically gathers and examines every relevant paper, providing significant insights into the algorithm's pros and cons. The objective is to assess FDO's performance in several dimensions and to identify its strengths and weaknesses. This study uses a comparative analysis to show how well FDO and its variations work at solving real-world optimization problems, which helps us understand what they can do. Finally, this paper proposes future research directions that can help researchers further enhance the performance of FDO.