Quantum-Inspired Evolutionary Algorithm (QEA) - A Rising Optimization Approach in Recent Years

Over the past two years, the Quantum-Inspired Evolutionary Algorithm (QEA) has gained substantial attention due to its exceptional performance in optimization tasks. This algorithm effectively handles various optimization problems, with the knapsack problem (a discrete binary optimization challenge) serving as a typical case study. The knapsack problem represents an NP-hard complexity class where even the fastest classical computers require considerable time to identify optimal solutions. QEA addresses this challenge through quantum-inspired representation using qubit individuals and quantum rotation gates for population evolution, enabling faster convergence to near-optimal solutions while reducing computational time and resource requirements. Consequently, QEA has become a prominent research focus in recent years, with successful applications spanning multiple domains including finance, healthcare, and engineering. The algorithm typically implements quantum measurement operations to collapse qubit states into classical binary solutions, followed by fitness evaluation and iterative quantum-gate updates to direct the search toward promising regions of the solution space.