A Many-Objective Evolutionary Algorithm Based on Fuzzy Dominance：MFEA

BI Xiao-jun; ZHANG Yong-jian; CHEN Chun-yu

doi:10.3969/j.issn.0372-2112.2014.08.031

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A Many-Objective Evolutionary Algorithm Based on Fuzzy Dominance：MFEA

更新时间：2025-07-16

- A Many-Objective Evolutionary Algorithm Based on Fuzzy Dominance：MFEA
- Acta Electronica Sinica Vol. 42, Issue 8, Pages: 1653-1659(2014)
- 作者机构：
  
  哈尔滨工程大学信息与通信工程学院,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61175126);Fundamental Research Funds for the Central Universities (No.HEUCFZ1209);Research Fund for the Doctoral Program of Higher Education of China (No.20112304110009)
- DOI：10.3969/j.issn.0372-2112.2014.08.031
  CLC： TP183
- Published：2014
- 稿件说明：
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BI Xiao-jun, ZHANG Yong-jian, CHEN Chun-yu. A Many-Objective Evolutionary Algorithm Based on Fuzzy Dominance：MFEA[J]. Acta Electronica Sinica, 2014, 42(8): 1653-1659.
DOI：

BI Xiao-jun, ZHANG Yong-jian, CHEN Chun-yu. A Many-Objective Evolutionary Algorithm Based on Fuzzy Dominance：MFEA[J]. Acta Electronica Sinica, 2014, 42(8): 1653-1659. DOI： 10.3969/j.issn.0372-2112.2014.08.031.

摘要

为提高高维复杂多目标优化算法的收敛性和解集分布性，提出一种基于模糊支配的高维多目标进化算法MFEA.在第二代Pareto支配类高维多目标进化算法模型基础上，利用模糊理论对模型中的环境选择进行改进，提出基于模糊隶属度的支配关系，并结合Harmonic、k邻域法和小生境技术对其中的拥挤密度估计方法进行改进，最后根据高维多目标的特点并结合模糊理论-截集的思想提出了新的环境选择策略.将该算法与目前性能最好的5种多目标进化算法在标准测试函数集上进行对比试验，结果表明本文算法与其他算法相比具有明显的优势，不仅提高了算法的收敛性能，而且保证了Pareto最优解的均匀分布性.

Abstract

In order to improve the convergence and distribution of Many-Objective Evolutionary Algorithms (MOEAs)

this paper proposes a Many-Objective Fuzzy Evolutionary Algorithm (MFEA) which is based on fuzzy dominance.On the model of algorithms based on Pareto-dominance

we improve the environmental selection using fuzzy logic.We present a new dominance strategy based on fuzzy membership.Then

we propose a new estimation method of crowding distance which incorporates Harmonic-distance

k-neighborhood method and niche technique.Finally

according to the characteristics of MOPs and the idea of -cut set

we design a new environmental selection strategy which is made up of two truncations.The proposed algorithm is compared to 5 state-of-the-art MOEAs on benchmark test problems.Simulation results show that -MFEA has obvious advantages than other algorithms because MFEA could ensure good convergence while has uniform distribution

especially

applied to solving high-dimensional MOPs.

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