基于综合学习策略的多目标分解粒子群算法

陈跃刚; 许奕

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基于综合学习策略的多目标分解粒子群算法

更新时间：2026-09-04

- 基于综合学习策略的多目标分解粒子群算法
- Multi-objective Decomposition Particle Swarm Optimization Algorithm Based on Comprehensive Learning Strategy
- 微电子学与计算机 2018年35卷第10期页码：75-79
- 作者机构：
  
  上海大学悉尼工商学院,上海,201899
- 作者简介：
- 基金信息：
  
  教育部人文社科规划项目 12YJA790013
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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陈跃刚, 许奕. 基于综合学习策略的多目标分解粒子群算法[J]. 微电子学与计算机, 2018, 35(10): 75-79.

CHEN Yue-gang, XU Yi. Multi-objective Decomposition Particle Swarm Optimization Algorithm Based on Comprehensive Learning Strategy[J]. 2018, 35(10): 75-79.
陈跃刚, 许奕. 基于综合学习策略的多目标分解粒子群算法[J]. 微电子学与计算机, 2018, 35(10): 75-79. DOI：

CHEN Yue-gang, XU Yi. Multi-objective Decomposition Particle Swarm Optimization Algorithm Based on Comprehensive Learning Strategy[J]. 2018, 35(10): 75-79. DOI：

摘要

本文提出了一种基于综合学习策略的多目标分解粒子群算法(D-CLMOPSO)

该算法采用综合学习策略对多目标问题进行求解

从而避免早熟收敛; 通过分解方法更新主导粒子以增强解的分布; 采用存档机制以存储优化过程中的非支配解

并采用多项式变异来避免陷入局部最优.最后将所提出的方法与三种多目标进化算法进行比较

结果表明所提算法在大多数测试问题上具有良好的性能.

Abstract

In this paper

a multi-objective decomposition particle swarm optimization algorithm (D-CLMOPSO) based on comprehensive learning strategy is proposed

which uses a comprehensive learning strategy to solve multi-objective problems

so as to avoid premature convergence. The dominant particles are updated by decomposition to enhance the solution Distribution; Archiving mechanism to store the non-dominated solution in the optimization process

and using polynomial variation to avoid falling into the local optimum. Finally

the proposed method is compared with the three multi-objective evolutionary algorithms. The results show that the proposed algorithm has good performance on most of the test problems.

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references

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