Enhanced Firefly Algorithm for Spatial Task Scheduling

With the rapid advancement of mobile devices and wireless networks, spatial crowdsourcing has emerged as a transformative e-market platform. It enables requesters to outsource tasks requiring a physical presence at specific locations, while workers autonomously select and complete these tasks for a reward. In this study, we address the task scheduling challenge under the Worker-Selected Tasks mode, where a single worker is assigned to a set of spatially distributed tasks. Each task is characterized by a geographic location and a strict deadline. The primary objective is to maximize the number of tasks completed by the worker while guaranteeing all temporal constraints are met. This involves strategically sequencing tasks to optimize both spatial route efficiency and deadline adherence. We propose a bio-inspired approach based on the Firefly Algorithm (FA). We further introduce an Enhanced Firefly Algorithm that incorporates adaptive parameter control, local search heuristics, and genetic crossover operations to improve convergence speed and solution quality. A comprehensive experimental evaluation was conducted using both real-world and synthetic datasets to assess the performance and computational complexity of the proposed method. Computational experiments demonstrate that the proposed algorithm performs competitively compared to existing solution methods.