Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters: A Case Study Implementation

TL;DR

This paper presents a case study on adapting the Fitness Dependent Optimizer (FDO) for IoT healthcare, demonstrating its superior performance over other algorithms and providing implementation guidance for real-world applications.

Contribution

The paper introduces an adapted FDO algorithm tailored for IoT healthcare, with detailed implementation steps and parameter tuning strategies to enhance its effectiveness.

Findings

FDO outperforms GA, PSO, SSA, DA, and WOA in IoT healthcare tasks.

Step-by-step FDO implementation facilitates practical application.

Parameter adaptation improves FDO's performance in big data IoT healthcare systems.

Abstract

This discusses a case study on Fitness Dependent Optimizer or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare. The reproductive way is sparked by the bee swarm and the collaborative decision-making of FDO. As opposed to the honey bee or artificial bee colony algorithms, this algorithm has no connection to them. In FDO, the search agent's position is updated using speed or velocity, but it's done differently. It creates weights based on the fitness function value of the problem, which assists lead the agents through the exploration and exploitation processes. Other algorithms are evaluated and compared to FDO as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the original work. The key current algorithms:The Salp-Swarm Algorithms (SSA), Dragonfly Algorithm (DA), and Whale Optimization Algorithm (WOA) have been evaluated against FDO in terms of their results. Using these FDO experimental findings, we may conclude that FDO outperforms the other techniques stated. There are two primary goals for this chapter: first, the implementation of FDO will be shown step-by-step so that readers can better comprehend the algorithm method and apply FDO to solve real-world applications quickly. The second issue deals with how to tweak the FDO settings to make the meta-heuristic evolutionary algorithm better in the IoT health service system at evaluating big quantities of information. Ultimately, the target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems.