Heuristic based task scheduling in multiprocessor systems with genetic algorithm by choosing the eligible processor

TL;DR

This paper proposes a heuristic-enhanced genetic algorithm for task scheduling in multiprocessor systems, aiming to reduce computation time while achieving near-optimal task distribution.

Contribution

It introduces a new heuristic for selecting eligible processors in genetic algorithms, improving scheduling efficiency in multiprocessor systems.

Findings

Heuristic-based GA reduces computation time.

Achieves near-optimal task scheduling.

Outperforms traditional GA in efficiency.

Abstract

In multiprocessor systems, one of the main factors of systems' performance is task scheduling. The well the task be distributed among the processors the well be the performance. Again finding the optimal solution of scheduling the tasks into the processors is NP-complete, that is, it will take a lot of time to find the optimal solution. Many evolutionary algorithms (e.g. Genetic Algorithm, Simulated annealing) are used to reach the near optimal solution in linear time. In this paper we propose a heuristic for genetic algorithm based task scheduling in multiprocessor systems by choosing the eligible processor on educated guess. From comparison it is found that this new heuristic based GA takes less computation time to reach the suboptimal solution.