Quantum-inspired multi-objective African vultures optimization algorithm with hierarchical structure for software requirement.

The software requirement selection problem endeavors to ascertain the optimal set of software requirements with the dual objectives of minimizing software cost and maximizing customer satisfaction. The intricate nature of this problem stems from the interdependencies among individual software requirements, rendering it a complicated NP-hard problem. In this paper, we introduce a novel multi-objective optimization algorithm christened the Quantum -Inspired Multi-Objective African Vulture Optimization Algorithm with Hierarchical Structures (QMO_HSAVOA), where hierarchical structure and in-quantum computation ideas are introduced to improve the performance of the algorithm in QMO_HSAVOA. To gauge the efficacy of QMO_HSAVOA in tackling the software requirement selection problem, we empirically apply it to the problem, orchestrating three distinct simulation experiments. The ensuing evaluation of QMO_HSAVOA's performance is conducted with meticulous scrutiny through the application of Friedman's statistical test to the experimental outcomes. These results decisively demonstrate that the proposed QMO_HSAVOA not only delivers exceptionally competitive outcomes but also outshines alternative algorithms. This finding provision is an innovative and highly efficient solution for addressing the software requirement selection problem. [ABSTRACT FROM AUTHOR]