A Model for Multi-processor Task Scheduling Problem Using Quantum Genetic Algorithm

Multiprocessor task scheduling problem is a well-known NP-hard and an important problem in the field of parallel computing. In order to solve this problem optimally, researchers have applied various heuristics and meta-heuristics. However, Genetic Algorithm (GA) is one of the widely opted meta-heuristic approaches to solve combinatorial optimization problems. In order to increase the probability of finding an optimal solution in GA, a new approach known as Quantum Genetic Algorithm (QGA) has been adopted. QGA increases the speed and efficiency of computation of a conventional GA by introducing the concept of parallelism of quantum computing in GA. In this paper, Quantum behavior inspired GA is introduced to solve multiprocessor task scheduling problem. The proposed QGA has been modified at certain points with some new operators to make it compatible for the same problem. The performance of proposed QGA is verified on a standard problem of linear algebra i.e., Gauss Jordan Elimination (GJE). The results have been compared with the state of the arts to prove its effectiveness.