A quantum-inspired artificial immune system for the multiobjective 0–1 knapsack problem

For solving the multiobjective 0–1 knapsack problem (MKP), a novel quantum-inspired artificial immune system (MOQAIS) is presented. The proposed algorithm is composed of a quantum-inspired artificial immune algorithm (QAIS) and an artificial immune system (BAIS). On one hand, QAIS, based on Q-bit representation, is responsible for exploration of the search space by using clone, mutation with a chaos-based rotation gate, update operation of Q-gate. On the other hand, BAIS, based on binary representation, 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 on MKP with 12 different test data show that MOQAIS is able to find a much better spread of solutions and has better convergence compared to a quantum-inspired multiobjective evolutionary algorithm (QMEA), a hybrid quantum genetic algorithm (HQGA), a weight-based multiobjective artificial immune system (WBMOAIS), an elitist non-dominated sorting genetic algorithm (NSGA-II) and an immune clonal algorithm only for MKP (ICMOA).