Schemes analysis on immune multiobjective optimization in dynamic environments

被引：1
|
作者：
Qian, Shuqu [1 ]
Liu, Yanmin [2 ]
Wu, Huihong [1 ]
Hang, Jin [1 ]
Guo, Benhua [1 ]
机构：
[1] Anshun Univ, Sch Sci, Anshun 561000, Peoples R China
[2] Zunyi Normal Coll, Sch Math & Comp Sci, Zunyi 563002, Guizhou, Peoples R China
来源：
2019 11TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2019), VOL 1 | 2019年
基金：
中国国家自然科学基金;
关键词：
artificial immune system; dynamic multiobjective optimization; change detection; change reaction; ALGORITHM;
D O I：
10.1109/IHMSC.2019.00015
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a parallel immune algorithm (PIA) inspired by artificial immune system is proposed for solving dynamic multiobjective problems (DMOPs). The population is divided into nondominated subpopulation and dominated sub-population. One focuses on exploring Pareto-optimal set through immune clone and hypermutation, while another is exploited and improved via the simulated binary crossover and polynomial mutation. To improve the tracking ability, the change detection and change reaction are developed in PIA. In numerical experiments, the effect of change detection threshold and individual updating ratio on the performance of PIA is interpreted and analyzed from different perspectives and further possible research direction is discussed.
引用
收藏
页码：29 / 32
页数：4
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