A Quantum-Inspired Artificial Immune System for Multiobjective 0-1 Knapsack Problems

In this study, a novel quantum-inspired artificial immune system (MOQAIS) is presented for solving the multiobjective 0-1 knapsack problem (MKP) The proposed algorithm is composed of a quantum-inspired artificial immune algorithm (QAIS) and an artificial immune system based binary encoding (BAIS). On one hand, QAIS, based on Q-bit representation, is responsible for exploration of the search space by using clone, mutation with it chaos-based rotation gate, update operator of Q-gate. On the other hand. BAIS is applied for exploitation of the search space with clone, a reverse mutation. Most, importantly, two diversity schemes, suppression algorithm and truncation algorithm with similar individuals (TASI), are employed to preserve the diversity of the population, and a new selection scheme based on TASI is proposed to create the new population. Simulation results show that MOQAIS is better than two quantum-inspired evolutionary algorithms and a weight-based multiobjective artificial immune system.