This article presents a comparative analysis of GPU-parallelized implementations of the quantum-inspired evolutionary optimization (QIEO) approach and one of the well-known classical metaheuristic techniques, the genetic algorithm (GA). The study assesses the performance of both algorithms on highly non-linear, non-convex, and non-separable function optimization problems, viz., Ackley, Rosenbrock, and Rastrigin, that are representative of the complex real-world optimization problems. The performance of these algorithms is checked by varying the population sizes by keeping all other parameters constant and comparing the fitness value it reached along with the number of function evaluations they required for convergence. The results demonstrate that QIEO performs better for these functions than GA, by achieving the target fitness with fewer function evaluations and significantly reducing the total optimization time approximately three times for the Ackley function and four times for the Rosenbrock and Rastrigin functions. Furthermore, QIEO exhibits greater consistency across trials, with a steady convergence rate that leads to a more uniform number of function evaluations, highlighting its reliability in solving challenging optimization problems. The findings indicate that QIEO is a promising alternative to GA for these kind of functions.

翻译：本文对量子启发进化优化（QIEO）方法与一种著名的经典元启发式技术——遗传算法（GA）的GPU并行化实现进行了比较分析。本研究评估了两种算法在高度非线性、非凸且不可分离的函数优化问题（即Ackley、Rosenbrock和Rastrigin函数）上的性能，这些问题代表了复杂的现实世界优化问题。通过保持所有其他参数不变、改变种群规模，并比较算法达到的适应度值及其收敛所需的函数评估次数，来检验这些算法的性能。结果表明，对于这些函数，QIEO的表现优于GA，它能够以更少的函数评估次数达到目标适应度，并且显著减少了总优化时间：对于Ackley函数大约减少了三倍，对于Rosenbrock和Rastrigin函数大约减少了四倍。此外，QIEO在多次试验中表现出更高的一致性，其稳定的收敛速率导致函数评估次数更为均匀，突显了其在解决具有挑战性的优化问题时的可靠性。研究结果表明，对于此类函数，QIEO是GA的一种有前景的替代方案。