Improved Bloch Quantum Genetic Algorithm Based on Elite Strategy

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.