Context: In requirements engineering phase of the software development life cycle, one of the main concerns of software engineers is to select a set of software requirements for implementation in the next release of the software from many requirements proposed by the customers, while balancing budget and customer satisfaction. Objective: To analyse the efficacy of Quantum-inspired Elitist Multi-objective Evolutionary Algorithm (QE-MEA), Quantum-inspired Multi-objective Differential Evolution Algorithm (QMDEA) and Multi-objective Quantum-inspired Hybrid Differential Evolution (MQHDE) in solving the software requirements selection problem. Method: The paper reports on empirical evaluation of the performance of three quantum-inspired multi objective evolutionary algorithms along with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The comparison includes the obtained Pareto fronts, the three performance metrics - Generational Distance, Spread and Hypervolume, attained boundary solutions, and size of the Pareto front. Results: The results reveal that MQHDE outperformed other methods in producing high quality solutions; while QMDEA is able to produce well distributed solutions with extreme boundary solutions. Conclusion: The hybridization of Differential Evolution with Genetic Algorithms coupled with quantum computing concepts (MQHDE) provided a means to effectively balance the two issues of multi-objective optimization - convergence and diversity. (c) 2016 Elsevier B.V. All rights reserved.