Comparative Analysis of Nature-Inspired Algorithms for Task Assignment Problem

In modern task assignment scenarios, the effective allocation of tasks to individuals with suitable skill levels is crucial to ensure efficient execution and resource utilization. This research addresses the problem of task assignment optimization, focusing on minimizing the discrepancy between the assigned skill levels of individuals and the required skill levels for each task. In this research, a comprehensive comparative analysis of nature-inspired algorithms applied to the TAP, to iteratively refine task assignments and improve skill matching. Nature-inspired algorithms (NIA) draw inspiration from biological and ecological processes, mimicking patterns observed in nature to solve complex problems. This study investigates four prominent nature-inspired algorithms: Jaya algorithm and Ant Colony Optimization (ACO) algorithm from Swarm Intelligence, genetic algorithm (GA), and Differential evolution from Evolutionary algorithm. These NIA approaches demonstrate its potential to provide practical solutions to complex task assignment challenges by mitigating skill mismatches and fostering efficient task execution. The significance of this research lies in its applicability to real-world scenarios such as project management, workforce scheduling, and resource allocation. By optimizing task assignments based on skill levels, organizations can enhance productivity, minimize skill wastage, and improve resource utilization.