Abstract
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 on 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 a 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.
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Gao, J., Fang, L., He, G. (2010). A Quantum-Inspired Artificial Immune System for Multiobjective 0-1 Knapsack Problems. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_21
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DOI: https://doi.org/10.1007/978-3-642-13278-0_21
Publisher Name: Springer, Berlin, Heidelberg
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