Quantum-Inspired Evolutionary Algorithm for Multi-Objective Resource Allocation

Abstract

This study proposes a Quantum-Inspired Evolutionary Algorithm (QIEA) to address high-dimensional multi-objective resource allocation problems. The probabilistic representation of solutions based on quantum bits and quantum rotation gates is used to guide the iterative search, and various diversity protection mechanisms are introduced to enhance the diversity of the population and the quality of the solutions. Many benchmark experiments were conducted on standard multi-objective problems (such as DTLZ2 and DTLZ7) and real-world resource allocation scenarios (such as power scheduling, bandwidth scheduling, and logistics). The results show that the proposed QIEA quickly converges to the Pareto optimal front; after 30 generations, the average inverted generational distance is less than 0.15, and the hypervolume value exceeds 0.92. This algorithm outperforms traditional evolutionary algorithms in terms of convergence speed, diversity distribution, and computational efficiency. Further analysis indicates that the algorithm is robust to noise and changes in objectives, and it can be well-scaled to larger problems. Based on the above analysis, QIEA is likely to be used in practice to solve resource allocation problems with constraints and conflicting objectives. Research indicates that quantum-inspired operators can be used in evolutionary strategies to address decision-making in large-scale, real-world optimization problems.