A Two-Space-Density Based Multi-objective Evolutionary Algorithm for Multi-objective Optimization

WANG Peng; ZHANG Chang-sheng; ZHANG Bin; WU Jia-xuan; LIU Ting-ting

doi:10.3969/j.issn.0372-2112.2017.10.005

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A Two-Space-Density Based Multi-objective Evolutionary Algorithm for Multi-objective Optimization

更新时间：2025-07-16

- A Two-Space-Density Based Multi-objective Evolutionary Algorithm for Multi-objective Optimization
- Acta Electronica Sinica Vol. 45, Issue 10, Pages: 2343-2347(2017)
- 作者机构：
  
  东北大学计算机科学与工程学院,辽宁,沈阳,110819
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61572116, No.61572117, No.61502089);Fund of National Key Science and Technology Research and Development Program of China (No.2015BAH09F02);Fundamental Research Funds for the Central Universities for Northeastern University (No.N150408001, No.N150404009)
- DOI：10.3969/j.issn.0372-2112.2017.10.005
  CLC： TP311
- Published Online：25 October 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
WANG Peng, ZHANG Chang-sheng, ZHANG Bin, et al. A Two-Space-Density Based Multi-objective Evolutionary Algorithm for Multi-objective Optimization[J]. Acta Electronica Sinica, 2017, 45(10): 2343-2347.
DOI：

WANG Peng, ZHANG Chang-sheng, ZHANG Bin, et al. A Two-Space-Density Based Multi-objective Evolutionary Algorithm for Multi-objective Optimization[J]. Acta Electronica Sinica, 2017, 45(10): 2343-2347. DOI： 10.3969/j.issn.0372-2112.2017.10.005.

摘要

目前，大多数多目标进化算法的多样性保持机制都只强调目标空间的多样性而忽视决策空间的多样性.这种设置可能导致种群在目标空间拥有良好多样性的同时却在决策空间拥有较差的多样性.为了解决上述问题，本文提出了一种基于双空间密度的多目标进化算法.为了反映个体在决策空间和目标空间的多样性，本文定义了双空间密度指标.基于双空间密度的配对选择操作可以平衡算法的收敛性与多样性；基于双空间密度的个体选择操作可以同时使决策空间和目标空间得到充分的搜索.实验结果表明，本文算法的求解质量明显优于对比算法.

Abstract

Most of the evolutionary algorithm researches related to diversity maintenance scheme are dedicated to the diversity of objective space and ignore the diversity of decision space.This arrangement could lead to excessive diversity in the objective space but poor diversity in the decision space.To address this issue

this paper proposes a two-space-density based multi-objective evolutionary algorithm.Two-space-density is defined to reflect the diversity in both the objective space and the decision space.Based on two-space-density

TSD-mating selection is presented to balance the convergence and the diversity of population;TSD-selection is designed to fully explore the objective space and the decision space.The experimental results show that our algorithm performs competitively against the chosen state-of-the-art designs.

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