基于量子计算和威布尔分布的混合CHIO算法求解JSP问题

Solving the JSP problem using a hybrid CHIO algorithm based on quantum computing and Weibull distribution

针对冠状病毒群免疫优化算法(coronavirus herd immunity optimizer,CHIO)在解决优化问题时存在易陷入局部最优解、收敛速度慢和收敛精度差等问题,文章提出一种量子混合CHIO算法(quantum hybrid coronavirus herd immunity optimizer,QCHIO)。首先,引入量子计算的思想,通过量子相关性实现全局搜索和快速收敛的目标,能够有效避免算法陷入局部最优解的问题。其次,采用威布尔分布算子的大步长和小步长来增加算法的多样性,使算法能够更好地探索搜索空间,增强了算法的全局开发能力。此外,还引入β-登山算子通过搜索当前最优解的邻域,尝试找到更优的解,从而增加了算法的搜索宽度,改善了解的质量。多邻域搜索则通过搜索全局最优解的多个邻域来增加了算法的收敛精度。为验证其性能,将QCHIO应用到10种标准算例中与其他几种改进算法进行了对比分析,并通过显著性检验证明了QCHIO的优越性。最后将QCHIO应用到某发动机生产调度实例上,进一步证明了QCHIO的可行性和优越性。

A quantum hybrid coronavirus herd immunity optimizer(QCHIO)algorithm is proposed to address the issues of local optima trapping,slow convergence speed,and poor convergence accuracy in the Coronavirus herd immunity optimizer(CHIO)algorithm.Firstly,the concept of quantum computing is introduced to achieve the goals of global search and fast convergence through quantum correlations,effectively avoiding the problem of the algorithm getting trapped in local optima.Secondly,the algorithm enhances its global exploration capability by utilizing both large and small step sizes of the Weibull distribution operator to increase algorithm diversity and better explore the search space.Additionally,the hill-climbing operator is introduced to search the neighborhood of the current best solution,attempting to find better solutions and thereby increasing the algorithm’s search breadth and improving the quality of solutions.Multi-neighborhood search further enhances the convergence accuracy of the algorithm by searching multiple neighborhoods of the global optimum.To validate its performance,QCHIO is applied to 10 standard test cases and compared with other improved algorithms,demonstrating its superiority through significant testing.Finally,the feasibility and superiority of QCHIO are further demonstrated by applying it to a case of engine production scheduling.