Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem

The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO) to enhance diversity convergence performance improve in high-dimensional cases for 0-1 problems. proposed adopts quantum computing principles such as superposition states gates. It also uses adaptive mutation operations evolution, crossover observation generate new solutions trial individuals. Selection are used determine better between stored individuals created by operations. In event that worse than current individuals, rotation gate preserve population well speed up search global optimal solution. experimental results confirm advantages QDGWO effectiveness capability problems, especially situations.