An improved Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) for scheduling of real-time task in multiprocessor system

Debanjan Konar, Siddhartha Bhattacharyya, Kalpana Sharma, Sital Sharma, Sri Raj Pradhan

Research output: Contribution to journalArticle

23 Citations (Scopus)


This article concerns an efficient real-time task scheduling assisted by Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) in multiprocessor environment. Relying on concepts and the principles of quantum mechanics, HQIGA explores the computing power of quantum computation. To drive schedule toward better convergence, HQIGA operates using rotation gate for exploration of variable chromosomes described by qubits in Hilbert hyperspace. A fitness function associated with popularly known heuristic earliest deadline first (EDF) is employed and random key distribution is adopted to convert the qubits chromosomes to valid schedule solutions. In addition to this, permutation based trimming technique is applied to diversify the population which yields good quality schedules. To establish the effectiveness of the suggested HQIGA, it demonstrates using various number of real-time tasks and processors along with arbitrary processing time. Simulation result shows that HQIGA outperforms the classical genetic algorithm (CGA) and Hybrid Particle Swarm Optimization (HPSO) in terms of fitness values obtained using less number of generations and also it improves the scheduling time significantly. HQIGA is also tested separately with the heuristic Shortest Computation Time First (SCTF) technique to show the superiority of EDF over SCTF.

Original languageEnglish
Pages (from-to)296-307
Number of pages12
JournalApplied Soft Computing Journal
Publication statusPublished - 01-04-2017
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Software

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