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中南大学学报(自然科学版)

Journal of Central South University

第46卷    第6期    总第250期    2015年6月

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文章编号:1672-7207(2015)06-2109-09
基于改进人工蜂群算法的高维多目标优化
王艳娇1, 2,肖婧2, 3

(1. 东北电力大学 信息工程学院,吉林 吉林,132002
2. 哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨,150001
3. 辽宁省交通高等专科学校 信息工程系,辽宁 沈阳,110122
)

摘 要: 为了提高高维多目标优化算法的收敛性和分布性,提出基于改进人工蜂群算法的高维多目标优化算法。首先,利用一种改进的适应值评价方式定量比较高维多目标中个体的优劣;其次,改进人工蜂群算法,使种群迅速收敛于最优的非支配前沿;最后,建立新的分布性维护机制使所获得的非支配解分布均匀、覆盖整个最优前沿。研究结果表明:对于3~8个目标的DTLZ系列测试函数,与PISA算法等几种较流行的高维多目标算法相比,本文方法收敛性好,解集覆盖范围广且分布均匀.

 

关键字: 高维多目标优化;人工蜂群算法;适应值评价方式;分布性维护方法

Optimization of multi-objective problems based on artificial bee colony algorithm
WANG Yanjiao1, 2, XIAO Jing2, 3

1. College of Information Engineering, Northeast Dianli University, Jilin 132002, China;
2. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
3. College of Information Engineering, Liaoning Provincial College of Communications, Shenyang 110122, China

Abstract:In order to improve the convergence and diversity of large-dimensional multi-objective optimization algorithms, a novel large-dimensional multi-objective optimization algorithm based on an improved artificial bee colony algorithm was proposed. Firstly, an improved fitness evaluation method was employed to measure the superiority of every individual quantitatively. Secondly, artificial bee colony algorithm was improved to make the population converge reach the true Pareto front quickly. Finally, a novel diversity-maintaining scheme was established to make the solution set distribute uniformly and cover the whole Pareto front. The results show that the diversities and convergence of the proposed algorithm are better than other state-of-the-art large-dimensional multi-objective optimization algorithms such as PISA.

 

Key words: large-dimensional multi-objective optimization; artificial bee colony algorithm; fitness evaluation method; diversity-maintaining scheme

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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