This research explores the application of a Quantum-Inspired Genetic Algorithm (QIGA) to optimize complex systems, utilizing a numerical experiment with a focus on the objective function... The QIGA integrates quantum-inspired principles, including crossover, entanglement, and evolution, to strike a balance between exploration and exploitation within the solution space. A 100-generation experiment with a population size of 50 reveals the algorithm's adaptability and gradual convergence towards optimal solutions. The linear combination crossover, guided by quantum principles, enhances diversity, while entanglement and evolution operations introduce correlations between quantum states. The results underscore the algorithm's potential, prompting discussions on parameter tuning, comparisons with classical algorithms, and considerations for transitioning to real quantum hardware. The findings contribute to the understanding of quantum-inspired optimization and pave the way for further research in quantum computing applications for complex system optimization.
Egon et al. (Thu,) studied this question.