Immune evolutionary algorithm is a new optimization algorithm inspired by biological immune mechanism based on the deep understanding of the existing evolutionary algorithm.
免疫进化算法是在深入理解现有进化算法的基础上，受生物免疫机制的启发而形成的一种新的优化算法。
On this basis, I analyze Immune Algorithm based on Clone, Immune Algorithm based on High and Low Position and Immune Algorithm based on Particle Swarm Optimization.
在对基本的算法的分析比较基础上，分析了一种基于克隆的免疫遗传算法、基于高低位变异的免疫算法、基于粒子群优化的免疫算法。
Immune algorithm is an optimization algorithm based on immune system of organism in nature, now it is a new development direction in multi-object of mechanical optimization design.
免疫算法是一种基于自然界生物体免疫系统的优化算法，是目前机械多目标优化设计中的一个新的研究方向。
Based on biologic immune system, this paper summarizes the bionics mechanism of immune optimization, while clonal selection and immune network theories are discussed detailedly.
本文以生物免疫系统为基础，概述免疫优化的仿生机理，重点介绍了克隆选择和免疫网络理论。
Based on the immune algorithm and geometric implication of the reliability index, a global optimization method was put forward to calculate the reliability index.
基于免疫算法原理和可靠度指标的几何涵义，提出了计算岩土工程可靠度指标和设计验算点的全局优化算法。
A parallel optimization algorithm is proposed based on the idea of affinity maturation of immune cells in the germinal centers in order to achieve as many as possible local optimal solutions.
为此 ，研究了产品与过程设计活动之间的并行度优化问题 ，并建立了数学模型 ，给出了求解该问题的算法 ，其目的是使因延迟产品开发完成时间和不必要的设计修改而带来的成本尽可能最低。
An improved immune particle swarm optimization was presented in this study in order to increase the effectiveness of feature selection for emotion recognition based on Galvanic Skin Response (GSR).
为了增强情感识别过程中皮肤电反应(GSR)信号特征选择的有效性，提出了一种改进的模拟退火免疫粒子群算法。
An improved immune particle swarm optimization was presented in this study in order to increase the effectiveness of feature selection for emotion recognition based on Galvanic Skin Response (GSR).
为了增强情感识别过程中皮肤电反应(GSR)信号特征选择的有效性，提出了一种改进的模拟退火免疫粒子群算法。
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