Published Paper Details:

Task scheduling is a fundamental problem in computer science that involves allocating computational resources to a set of tasks in an efficient and timely manner. The optimization of task scheduling is crucial in improving system performance, reducing execution time, and maximizing resource utilization. Particle Swarm Optimization (PSO) is a metaheuristic algorithm inspired by the collective behavior of bird flocking or fish schooling. This abstract presents a novel approach to task scheduling using Particle Swarm Optimization. The proposed method aims to find an optimal scheduling solution by leveraging the inherent parallelism and exploration capabilities of PSO. The problem is formulated as a multi-objective optimization task, considering objectives such as minimizing makespan, load balancing, and energy consumption. In the proposed approach, a population of particles represents potential task schedules, where each particle's position corresponds to a specific scheduling solution. The particles collaborate by sharing information about their best-known positions, called personal bests, and the overall best-known position, called the global best. Through a series of iterations, particles update their positions based on their own experience and the collective knowledge of the swarm. To guide the search process, a fitness function is defined that evaluates the quality of a scheduling solution based on the specified objectives. The fitness function considers factors such as task dependencies, resource constraints, and system characteristics. By iteratively evaluating and updating the fitness values, the PSO algorithm gradually converges towards an optimal or near-optimal scheduling solution. Experimental results on benchmark task scheduling problems demonstrate the effectiveness of the proposed PSO-based approach. It achieves significant improvements in terms of makespan reduction, load balancing, and energy efficiency compared to traditional scheduling algorithms. The approach also exhibits robustness and scalability, allowing it to handle larger problem instances efficiently.