Binary quantum-inspired gravitational search algorithm-based multi-criteria scheduling for multi-processor computing systems

A quantum-inspired hybrid scheduling technique is proposed for multi-processor computing systems. The proposed algorithm is a hybridization of principles of quantum mechanics (QM) and a nature-inspired intelligence, gravitational search algorithm (GSA). The principles of QM such as quantum bit, superposition and rotation gate help to design an efficient agent representation as well as intense exploration capability of GSA enhances toward better converging rate. The fitness function is designed with the aim to minimize makespan, adequate balancing of loads and proper utilization of the deployed resources during the evaluation of agents. Several standard benchmarks as well as synthetic data sets are used to analyze and validate the work. The performance improvement of the proposed algorithm is compared with recently designed algorithms like quantum genetic algorithm, particle swarm optimization-based multi-criteria scheduling, Improved-GA, GSA and Cloudy-GSA. The significance of the algorithm is tested using a hypothesis analysis of variance.