Function optimization is an important problem in the fields of mathematics and computer science, with wide applications in various real-world domains. Quantum genetic algorithm is a global, parallel, and efficient search method used to solve function optimization and search problems. The Bloch Quantum Evolution Algorithm (BQEA) based on quantum bit Bloch sphere coordinates greatly expands the proportion of optimal solutions, but also expands the search space, making the algorithm prone to falling into local optima. This article proposes a new improved Bloch Elite Quantum Genetic Algorithm (BEQGA) by increasing the number of elite individuals and improving the optimization method of fixed rotation angles. Through multiple simulation experiments, it has been proven that BEQGA has a certain improvement in search ability compared to BQEA, and this has been validated in the research of key technologies for high-precision flexible bag feeding packaging equipment.
This article presents a numerical method based on quantum computing to solve two unresolved key issues for alkali–surfactant–polymer (ASP) flooding in oil exploitation: uncertainties affecting development planning and a switch control regarding injection oil wells. First, a fuzzy multi-objective mixed-integer optimal control model describing the mechanism of ASP flooding is established. Then, an improved possibilistic programming algorithm is presented to remove fuzziness and transform the model into a deterministic single-objective mixed-integer nonlinear programming (MINLP) model. On this basis, a bi-level quantum computing algorithm including quantum annealing and quantum ant colony algorithm is proposed to solve the MINLP and acquire the optimal mixed-integer control variables. The superiority of the proposed algorithm is verified by testing a number of benchmark examples. Finally, the numerical method based on quantum computing is adopted to solve successfully a mixed-integer optimal control problem for ASP flooding under uncertainty.
Search-based software engineering (SBSE) addresses critical optimization challenges in software engineering, including the next release problem (NRP) and feature selection problem (FSP). While traditional heuristic approaches and integer linear programming (ILP) methods have demonstrated efficacy for small to medium-scale problems, their scalability to large-scale instances remains unknown. Here, we introduce quantum annealing (QA) as a subroutine to tackling multi-objective SBSE problems, leveraging the computational potential of quantum systems. We propose two QA-based algorithms tailored to different problem scales. For small-scale problems, we reformulate multi-objective optimization (MOO) as single-objective optimization (SOO) using penalty-based mappings for quantum processing. For large-scale problems, we employ a decomposition strategy guided by maximum energy impact (MEI), integrating QA with a steepest descent method to enhance local search efficiency. Applied to NRP and FSP, our approaches are benchmarked against the heuristic NSGA-II and the ILP-based
ε-constraint method. Experimental results reveal that while our methods produce fewer non-dominated solutions than
ε-constraint, they achieve significant reductions in execution time. Moreover, compared to NSGA-II, our methods deliver more non-dominated solutions with superior computational efficiency. These findings underscore the potential of QA in advancing scalable and efficient solutions for SBSE challenges.