QNSGA-II: A Quantum Computing-Inspired Approach to Multi-Objective Optimization

This paper proposes a novel quantum computing-inspired approach to multi-objective optimization, called Quantum Computing Inspired Non-dominated Sorting Genetic Algorithm II (QNSGA-II). Although Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been effectively used in the literature to solve a variety of optimization issues, it may encounter some difficulties especially in handling heavily constrained problems due to its premature convergence. The proposed approach mitigates this difficulty by combining conventional NSGA-II with the concept and principles of quantum computing. QNSGA-II exploits quantum bits and superposition of states to reduce the convergence time and improve search space capability by evolving the probabilistic model. This paper aims to provide more detailed information about our proposed algorithm and its advantages.