An Adaptive Niche for Keeping the Diversity of Solutions in Multi-Objective Evolutionary Algorithm

ZHENG Jin-hua; LIU Lei; LIU Wen-pan; ZOU Juan

doi:10.3969/j.issn.0372-2112.2012.11.030

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An Adaptive Niche for Keeping the Diversity of Solutions in Multi-Objective Evolutionary Algorithm

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- An Adaptive Niche for Keeping the Diversity of Solutions in Multi-Objective Evolutionary Algorithm
- Acta Electronica Sinica Vol. 40, Issue 11, Pages: 2330-2335(2012)
- 作者机构：
  
  湘潭大学信息工程学院,湖南,湘潭,411105
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61070088);Key Science Research Program of Education Department of Hunan Province (No.06A074)
- DOI：10.3969/j.issn.0372-2112.2012.11.030
  CLC： TP18
- Published：2012
- 稿件说明：
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ZHENG Jin-hua, LIU Lei, LIU Wen-pan, et al. An Adaptive Niche for Keeping the Diversity of Solutions in Multi-Objective Evolutionary Algorithm[J]. Acta Electronica Sinica, 2012, 40(11): 2330-2335.
DOI：

ZHENG Jin-hua, LIU Lei, LIU Wen-pan, et al. An Adaptive Niche for Keeping the Diversity of Solutions in Multi-Objective Evolutionary Algorithm[J]. Acta Electronica Sinica, 2012, 40(11): 2330-2335. DOI： 10.3969/j.issn.0372-2112.2012.11.030.

摘要

小生境技术被广泛应用在多目标进化的分布性保持方面.但是

小生境半径不易控制等限制了其在分布性保持等方面的发展.本文提出了一种自适应小生境分布性保持策略(Adaptive Niche

AN).AN通过对Pareto解集生成最小生成树来自适应调整小生境半径

同时扩大搜索小生境半径并改变计算方法使之能够识别小生境边沿的个体

便于对其修剪与评价.通过与NSGA-II

SPEA2在不同形状测试函数上进行对比实验

结果表明

AN能够对Pareto最优面进行高效地分布性保持.

Abstract

Niche is an effective and widely used diversity preservation technique in multi-objective evolutionary algorithms (MOEAs).However

it suffers from two feedbacks:the determination of niche radius is far from trivial and the fitness value evaluated by niche technique is too coarse to be reliable in some scenarios.This paper proposes an Adaptive Niche (AN) technique

in which the parameter value of niche radius can be automatically tuned according to the current population.Furthermore

the individuals locate on the niche boundary and inside are tackled differently

when evaluating the fitness value.Comprehensive experiments demonstrate the superiority of proposed AN

compared to several state-of-the-art MOEAs.

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