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

A quantum-inspired artificial immune system for the multiobjective 0-1 knapsack problem
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用于解决多目标0-1背包问题的量子启发人工免疫系统

DOI:
10.1016/j.amc.2013.12.088
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发表时间:
2014
影响因子:
4
通讯作者:
Feng Zhilin
Feng Zhilin
中科院分区:
数学2区
文献类型:
--
作者:
Gao Jiaquan;He Guixia;Liang Ronghua;Feng Zhilin

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为了解决多目标0-1背包问题(MKP),提出了一种新型的量子启发人工免疫系统(MOQAIS)。该算法由量子启发人工免疫算法(QAIS)和人工免疫系统(BAIS)组成。一方面,基于Q位表示的QAIS负责通过使用克隆、基于混沌的旋转门的变异、Q门的更新操作来探索搜索空间。另一方面,基于二进制表示的 BAIS 应用于利用克隆(一种反向突变)来开发搜索空间。最重要的是,采用抑制算法和相似个体截断算法(TASI)两种多样性方案来保留种群的多样性,并提出一种基于TASI的新选择方案来创建新种群。 12个不同测试数据对MKP的仿真结果表明,与量子启发多目标进化算法(QMEA)、混合量子遗传算法(HQGA)、基于权重的多目标人工免疫系统(WBMOAIS)、精英非支配排序遗传算法(NSGA-II)和仅针对MKP的免疫克隆算法(ICMOA)相比,MOQAIS能够找到更好的解扩散性,并且具有更好的收敛性。
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).
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发表时间: 2002-12-01
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期刊: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
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