基于群学习的混合人工免疫网络

Jian Fu, Zhonghua Li, Hongzhou Tan
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引用次数: 16

摘要

人工免疫系统是在生物免疫系统丰富机制的启发下发展起来的一种新途径。它包括克隆、变异、选择甚至交叉等基本操作。广泛应用于函数优化、异常检测、模式识别、计算机安全、机器学习、控制工程等领域。然而,目前人工免疫系统的进化过程只取决于两个因素。一是抗体与抗原的适合度,二是抗体群体的浓度。作为一种全局搜索方法,粒子群优化包含了一个重要的社会学习机制,使其能够快速逼近全局最优解。提出了一种具有群体学习和精英保持的混合人工免疫网络优化算法。仿真结果表明,该方法具有时间复杂度低、收敛速度快的特点,是一种有效的优化工具。
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A Hybrid Artificial Immune Network with Swarm Learning
The artificial immune system is a new approach inspired from abundant mechanisms of biological immune system. It includes such basic operations as clone, mutation, and selection, even crossover. It is widely applied to function optimization, abnormal detection, pattern recognition, computer security, machine learning, control engineering, etc. However, the evolutionary process of the current artificial immune system depends on only two factors. One is the fitness between antibody and antigen, and the other is the concentration of antibody population. As a global searching method, particle swarm optimization includes an important social learning mechanism that enables it to fast approximate the global optimum. This paper proposed a hybrid artificial immune network for optimization with swarm learning and elite-keeping. Simulation results indicated this hybrid method has lower time complexity and fast convergence, and is an effective optimization tool.
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