计算机科学 ›› 2010, Vol. 37 ›› Issue (1): 217-221.

• 人工智能 • 上一篇    下一篇

一种基于进化与免疫的动态多目标人工免疫系统模型

陶媛,吴耿锋,胡珉   

  1. (上海大学计算机工程与科学学院 上海200072);(上海大学悉尼工商学院 上海201800)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(50778109),上海市科委重点科技攻关项目(08511501702),上海市重点学科建设项目(J50103),上海大学博上创新基金((SHUCX091011)资助。

Dynamic Multi-object Artificial Immune System Model Based on Mechanisms of Evolution and Immunity

TAO Yuan,WU Geng-feng,HU Min   

  • Online:2018-12-01 Published:2018-12-01

摘要: 提出一种基于生物免疫系统工作原理的动态多目标人工免疫系统模型,模型由五元组—环境集、抗体集、抗原集、规则集和一个新的动态进化免疫算法DMEIA构成。DMEIA作为模型的核心元素,将进化算法保留上一代性相结合,用于控制和协调模型中其他元素的运作。仿真实验表明,DMEIA算法与已有算法相比,具有更稳定的环境追踪能力,以及良好的收敛性、多样性和解的分布性,从而验证了新模型的性能。

关键词: 动态多目标优化,进化免疫,环境追踪,克隆选择

Abstract: This paper proposed a new dynamic multi-object artificial immune system model based on simulating the principle of the biological immune system. The model consists of five elements, i. e. environment set, antibody set, antigen set, rule set and a Dynamic Multi-object Evolutionary Immune Algorithm(DMEIA). As a key clement of the model, DMEIA combines the feature of evolutionary algorithm which selects optimal non-dominated antibodies and makes them to join in evolution of next generation,and the characteristic of immune algorithm which has strong population diversity and adaptive searching ability to control and assorts with the operation of the model. Compared with the existed algorithms, DMEIA has better convergence, diversity, distribution of solution and stability of environment tracking, therefore the performance of the new model is proven to be available.

Key words: Dynamic mufti objective optimization, Evolutionary immune, Environment tracking, Clone selection

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