Adaptive Multiobjective Particle Swarm Optimization Based on Decomposition Archive

doi:10.3969/j.issn.0372-2112.2020.07.001

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Adaptive Multiobjective Particle Swarm Optimization Based on Decomposition Archive

更新时间：2025-07-08

- Adaptive Multiobjective Particle Swarm Optimization Based on Decomposition Archive
- Acta Electronica Sinica Vol. 48, Issue 7, Pages: 1245-1254(2020)
- 作者机构：
  
  1. 北京工业大学信息学部,北京,100124
  2. 计算智能与智能系统北京市重点实验室,北京,100124
  3. 北京工业大学信息学部,北京,100124
  4. 计算智能与智能系统北京市重点实验室,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.07.001
  CLC： TP18
- Published Online：25 July 2020，
  
  Published：2020
- 稿件说明：
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Adaptive Multiobjective Particle Swarm Optimization Based on Decomposition Archive[J]. Acta Electronica Sinica, 2020, 48(7): 1245-1254.
DOI：

Adaptive Multiobjective Particle Swarm Optimization Based on Decomposition Archive[J]. Acta Electronica Sinica, 2020, 48(7): 1245-1254. DOI： 10.3969/j.issn.0372-2112.2020.07.001.

摘要

为了提高多目标粒子群优化算法解的分布性，文中提出了一种自适应分解式多目标粒子群优化算法（Adaptive Multiobjective Particle Swarm Optimization based on Decomposed Archive，AMOPSO-DA）.首先，设计了一种基于优化解空间分布信息的外部档案更新策略，有效提升了AMOPSO-DA的空间搜索能力；其次，提出了一种基于粒子进化方向信息的飞行参数调整方法，有效平衡了AMOPSO-DA的探索和开发能力.最后，将提出的AMOPSO-DA应用于多目标优化问题，实验结果表明，文中提出的AMOPSO-DA能够获得分布性较好的优化解.

Abstract

To improve the distribution performance of multiobjective particle swarm optimization algorithm

an adaptive multiobjective particle swarm optimization algorithm

based on the decomposed archive

named AMOPSO-DA

is developed in this paper. First

an external archive update strategy

based on the spatial distribution information of optimal solutions

is designed to improve the searching ability of AMOPSO-DA. Second

an adaptive flying parameter adjustment strategy

based on the evolutionary direction information of each particle

is proposed to balance the exploration ability and the exploitation ability. Finally

this proposed AMOPSO-DA is applied to some multiobjective optimization problems. The experiment results demonstrate that AMOPSO-DA can obtain well-distributed optimal solutions.

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