ABSTRACT
In fitness-dependent optimizer (FDO), the search agent’s position is updated using speed or velocity, but it is 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 salp-swarm algorithm (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: (1) 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. (2) It deals with how to tweak the FDO settings to make the metaheuristic 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.
