Multi-Objective Chaos Memetic Algorithm for DTLZ Problems

被引：0
|
作者：
Ammaruekarat, Paranya [1 ]
Meesad, Phayung [2 ]
机构：
[1] King Mongkuts Univ Technol N Bangkok, Dept Informat Technol, Bangkok, Thailand
[2] King Mongkuts Univ Technol N Bangkok, Dept Teccher Training Elect Engn, Bangkok, Thailand
来源：
MEMS, NANO AND SMART SYSTEMS, PTS 1-6 | 2012年 / 403-408卷
关键词：
Multi-Objective Memetic algorithm; Multi-objective optimization; Chaos theory;
D O I：
10.4028/www.scientific.net/AMR.403-408.3676
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on Multi-Objective Memetic Algorithm (MOMA), a novel Multi-Objective Chaos Memetic Algorithm (MOCMA) is proposed. MOCMA is presented to keep population's diversity, avoid local optimum and improve performance of Multi-Objective Memetic Algorithm. By virtue of the over-spread character of chaos sequence, it is used to generate chromosome to overcome redundancies. At the same time, searching space is enlarged by using sensitivity of chaos initial value. The comparisons of MOCMA with NSGAII in DTLZ problems suggest that MOCMA clearly outperforms in converging towards the true pareto front and finding the spread of solutions.
引用
收藏
页码：3676 / +
页数：3
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