A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

Ming-gang DONG; Bao LIU; Chao JING

doi:10.1631/FITEE.1900321

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A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

Regular Papers | Updated：2022-05-19

- A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization
- 不规则优化问题中基于动态资源分配的高维多目标优化算法
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 8, Pages: 1171-1190(2020)
- Affiliations：
  
  College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
  Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin 541004, China
  Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic & Technology, Guilin 541004, China
- Author bio：
  
  Chao JING, E-mail: jingchao@glut.edu.cn
- Funds：
- DOI：10.1631/FITEE.1900321
  CLC： TP391
- Received：28 June 2019，
  
  Revised：2020-;7-21，
  
  Published：2020-08
- Accepted：
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Ming-gang DONG, Bao LIU, Chao JING. A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1171-1190.
DOI：

Ming-gang DONG, Bao LIU, Chao JING. A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1171-1190. DOI： 10.1631/FITEE.1900321.

摘要

多目标优化问题广泛存在于高速列车头形设计、重叠社区检测、电力调度等领域。为解决这类问题，目前方法主要集中于求解具有规则性帕累托前沿的问题，而非具有不规则帕累托前沿的问题。针对这种情况，提出一种基于动态资源分配分解的高维多目标进化算法（MaOEA/D-DRA）进行不规则优化。该算法能够根据问题的帕累托前沿形状，将计算资源动态分配到不同搜索区域。在搜索过程中使用进化种群和外部存档，从外部存档中提取的信息用于引导进化种群到不同搜索区域。进化种群采用切比雪夫方法将问题分解为若干子问题，并以协作方式优化所有子问题。采用转化的密度估计方法更新外部档案。将所提算法与5种最先进的多目标进化算法对比。实验结果表明，所提算法在收敛速度和种群成员多样性方面优于5种对比算法。与加权和方法和基于惩罚的边界相交方法比较，将切比切夫方法集成到算法中，对性能有一定提高。

Abstract

The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design

overlapping community detection

power dispatch

and unmanned aerial vehicle formation. To address such issues

current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto front. Considering this situation

we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation (MaOEA/D-DRA) for irregular optimization. The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem's Pareto front. An evolutionary population and an external archive are used in the search process

and information extracted from the external archive is used to guide the evolutionary population to different search regions. The evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems

and all the subproblems are optimized in a collaborative manner. The external archive is updated with the method of shift-based density estimation. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms using a variety of test problems with irregular Pareto front. Experimental results show that the proposed algorithm outperforms these five algorithms with respect to convergence speed and diversity of population members. By comparison with the weighted-sum approach and penalty-based boundary intersection approach

there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.

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Keywords

references

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