检索结果-内蒙古大学图书馆

A multiobjective immune algorithm based on a multiple-affinity model

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第1期202卷 60-72页

作者： Hu, Zhi-Hua Shanghai Maritime Univ Logist Res Ctr Shanghai 200135 Peoples R China

This paper presents a new multiobjective immune algorithm based on a multiple-affinity model inspired by immune system (MAM-MOIA). The multiple-affinity model builds the relationship model among main entities and concepts in multiobjective problems (MOPs) and multiobjective evolutionary algorithms (MOEAs), including feasible solution, variable space, objective space, Pareto-optimal set, ranking and crowding distance. In the model, immune operators including clonal proliferation, hypermutation and immune suppression are designed to proliferate superior antibodies and suppress the inferiors. MAM-MOIA is compared with NSCA-II, SPEA2 and NNIA in solving the ZDT and DTLZ standard test problems. The experimental study based on three performance metrics including coverage of two sets, convergence and spacing proves that MAM-MOIA is effective for solving MOPs. (C) 2009 Elsevier B.V. All rights reserved.

关键词： multiobjective optimization multiobjective immune algorithm multiobjective evolutionary algorithm Crowding distance Multiple-affinity model

来源：评论

学校读者我要写书评

暂无评论

multiobjective multifactorial immune algorithm for multiobjective multitask optimization problems

引用

APPLIED SOFT COMPUTING 2021年 107卷

作者： Xu, Zhiwei Zhang, Kai Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

Inspired by human brains' ability to solve multiple tasks simultaneously, evolutionary multitasking is proposed to improve the overall efficiency of optimizing multiple tasks simultaneously by reusing the learned knowledge. The immune algorithm is inspired by the biological immune system that has been proven to be effective in many practical multiobjective optimization problems, with efficient convergence and search efficiency. In this paper, a novel multiobjective multifactorial immune algorithm is proposed with a novel information transfer method to solve multiobjective multitask optimization problems. For each task, information advantageous for this task will be transferred from the others to accelerate convergence through the proposed information transfer method. Finally, the proposed algorithm is compared with the state-of-the-art multiobjective evolutionary multitasking algorithms and the classic multiobjective evolutionary algorithms. The experimental results on the classical multiobjective multitask and the multiobjective many-task test suites demonstrate that the proposed algorithm provides very promising performances. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Evolutionary multitasking multiobjective immune algorithm multiobjective optimization Evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：