Design of Population Size for Multi-objective Particle Swarm Optimization Algorithm Based on the Convergence Speed and Diversity

HAN Hong-gui; WU Shu-jun

doi:10.3969/j.issn.0372-2112.2018.09.031

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Design of Population Size for Multi-objective Particle Swarm Optimization Algorithm Based on the Convergence Speed and Diversity

更新时间：2025-07-16

- Design of Population Size for Multi-objective Particle Swarm Optimization Algorithm Based on the Convergence Speed and Diversity
- Acta Electronica Sinica Vol. 46, Issue 9, Pages: 2263-2269(2018)
- 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.0372-2112.2018.09.031
  CLC： TP18
- Published Online：25 September 2018，
  
  Published：2018
- 稿件说明：
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HAN Hong-gui, WU Shu-jun. Design of Population Size for Multi-objective Particle Swarm Optimization Algorithm Based on the Convergence Speed and Diversity[J]. Acta Electronica Sinica, 2018, 46(9): 2263-2269.
DOI：

HAN Hong-gui, WU Shu-jun. Design of Population Size for Multi-objective Particle Swarm Optimization Algorithm Based on the Convergence Speed and Diversity[J]. Acta Electronica Sinica, 2018, 46(9): 2263-2269. DOI： 10.3969/j.issn.0372-2112.2018.09.031.

摘要

针对多目标粒子群优化算法种群规模难以确定的问题，文中提出了一种基于收敛速度和多样性的多目标粒子群优化（Convergence speed and Diversity-based Multi-Objective Particle Swarm Optimization，CD-MOPSO）算法.首先，利用优化过程的收敛速度和多样性指标构造种群规模适应度函数，完成了种群规模与优化性能关系的描述；其次，基于适应度函数设计了一种种群规模自适应调整方法，实现了种群规模的动态调整；最后，将提出的CD-MOPSO在基准优化问题ZDT上测试并应用于城市管网优化，实验结果显示CD-MOPSO能够根据求解问题自动调整种群规模，与NSGA-Ⅱ、MOPSO、SPEA2和EMDS-MOPSO相比具有更快的收敛速度和更好的优化结果.

Abstract

To determine the population size of multi-objective particle swarm optimization algorithm (MOPSO)

an improved MOPSO

based on the convergence speed and diversity

named CD-MOPSO

is proposed. Firstly

the fitness function of population size

which is developed by the convergence speed and diversity during the evolutionary process

is used to describe the relationship between the population size and the performance of MOPSO. Secondly

according to the fitness function

an adaptive adjustment method is designed to update the population size of MOPSO dynamically. Finally

the proposed CD-MOPSO is tested on the ZDT benchmark optimization problems and applied to a real optimization problem of urban pipe networks. The experimental results show that the proposed CD-MOPSO can adjust the population size automatically according to the problem

compared with the performance of NSGA

MOPSO

SPEA2 and EMDS-MOPSO

CD-MOPSO has faster convergence speed with better optimization results.

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