Rigorous Running Time Analysis of a Simple Immune-Based Multi-Objective Optimizer for Bi-Objective Pseudo-Boolean Functions

被引：1

作者：

Zhou S. ^{[1
,3
]}

Peng X. ^{[2
]}

Wang Y. ^{[1
]}

Xia X. ^{[4
]}

机构：

[1] School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai

[2] School of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou

[3] Office of Budget and Finance, Jiaxing University, Jiaxing, 314001, Zhejiang

[4] College of Mathematics, Physics and Information Engineering, Jiaxing University, Jiaxing, 314001, Zhejiang

来源：

Journal of Shanghai Jiaotong University (Science) | 2018年 / 23卷 / 6期

关键词：

A; evolutionary algorithm; running time analysis; somatic contiguous hypermutation; TP; 18;

D O I：

10.1007/s12204-018-2004-z

中图分类号：

学科分类号：

摘要：

A simple immune-based multi-objective optimizer (IBMO) is proposed, and a rigorous running time analysis of IBMO on three proposed bi-objective pseudo-Boolean functions (Bi-Trap, Bi-Plateau and Bi-Jump) is presented. The running time of a global simple evolutionary multi-objective optimizer (GSEMO) using standard bit mutation operator with IBMO using somatic contiguous hypermutation (CHM) operator is compared with these three functions. The results show that the immune-based hypermutation can significantly beat standard bit mutation on some well-known multi-objective pseudo-Boolean functions. The proofs allow us to understand the relationship between the characteristics of the problems and the features of the algorithms more deeply. These analysis results also give us a good inspiration to analyze and design a bio-inspired search heuristics. © 2018, Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature.

引用

页码：827 / 833

页数：6

共 37 条

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