基于收敛速度和多样性的多目标粒子群种群规模优化设计
Design of Population Size for Multi-objective Particle Swarm Optimization Algorithm Based on the Convergence Speed and Diversity
查看参考文献24篇
|
文摘
|
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. |
|
其他语种文摘
|
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. |
|
来源
|
电子学报
,2018,46(9):2263-2269 【核心库】
|
|
DOI
|
10.3969/j.issn.0372-2112.2018.09.031
|
|
关键词
|
多目标粒子群优化算法
;
种群规模
;
自适应调整方法
;
动态调整
;
适应度函数
;
收敛速度
;
多样性
;
基准测试函数
;
城市管网优化
|
|
地址
|
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, 计算智能与智能系统北京市重点实验室, 北京, 100124
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0372-2112 |
|
学科
|
自动化技术、计算机技术 |
|
基金
|
国家自然科学基金
;
北京市自然科学基金
;
科技部水专项
|
|
文献收藏号
|
CSCD:6344794
|
参考文献 共
24
共2页
|
|
1.
Xue B. Particle swarm optimization for feature selection in classification:A multi-objective approach.
IEEE Transactions on Cybernetics,2013,43(6):1656-1671
|
CSCD被引
29
次
|
|
|
|
|
2.
Von Lucken C. A survey on multi-objective evolutionary algorithms for many-objective problems.
Computational Optimization and Applications,2014,58(3):707-756
|
CSCD被引
5
次
|
|
|
|
|
3.
谢承旺. 一种多策略融合的多目标粒子群优化算法.
电子学报,2015,43(8):1538-1544
|
CSCD被引
15
次
|
|
|
|
|
4.
Sheikholeslami F. Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance.
Swarm and Evolutionary Computation,2017,35(4):53-64
|
CSCD被引
2
次
|
|
|
|
|
5.
Daneshyari W. Cultural MOPSO:A cultural framework to adapt parameters of multiobjective particle swarm optimization.
IEEE Congress on Evolutionary Computation,2008:1325-1332
|
CSCD被引
1
次
|
|
|
|
|
6.
Leong W F. Dynamic population size in PSO-based multiobjective optimization.
IEEE Congress on Evolutionary Computation,2006:1718-1725
|
CSCD被引
1
次
|
|
|
|
|
7.
Koumousis V K. A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance.
IEEE Transactions on Evolutionary Computation,2006,10(1):19-28
|
CSCD被引
14
次
|
|
|
|
|
8.
Brest J. High-dimensional real-parameter optimization using self-adaptive differential evolution algorithm with population size reduction.
IEEE Congress on Evolutionary Computation,2008:2032-2039
|
CSCD被引
1
次
|
|
|
|
|
9.
Liu Y. A Self-Adaptive control strategy of population size for ant colony optimization algorithms.
International Conference in Swarm Intelligence,2016:443-450
|
CSCD被引
1
次
|
|
|
|
|
10.
Chen D B. Particle swarm optimization with adaptive population size and its application.
Applied Soft Computing,2009,9(1):39-48
|
CSCD被引
14
次
|
|
|
|
|
11.
Leong W F. PSO-based multiobjective optimization with dynamic population size and adaptive local archives.
IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2008,38(5):1270-1293
|
CSCD被引
17
次
|
|
|
|
|
12.
Marler R T. Survey of multi-objective optimization methods for engineering.
Structural and Multidisciplinary Optimization,2004,26(6):369-395
|
CSCD被引
109
次
|
|
|
|
|
13.
Durillo J J. jMetal:A Java framework for multi-objective optimization.
Advances in Engineering Software,2011,42(10):760-771
|
CSCD被引
19
次
|
|
|
|
|
14.
Lalwani S. A comprehensive survey:Applications of multi-objective particle swarm optimization(MOPSO) algorithm.
Transactions on Combinatorics,2013,2(1):39-101
|
CSCD被引
7
次
|
|
|
|
|
15.
Taherkhani M. A novel stability-based adaptive inertia weight for particle swarm optimization.
Applied Soft Computing,2016,38:281-295
|
CSCD被引
20
次
|
|
|
|
|
16.
Lim D K. A novel surrogate-assisted multi-objective optimization algorithm for an electromagnetic machine design.
IEEE Transactions on Magnetics,2015,51(3):1-4
|
CSCD被引
5
次
|
|
|
|
|
17.
Li L. Multi-objective particle swarm optimization based on global margin ranking.
Information Sciences,2017,375:30-47
|
CSCD被引
9
次
|
|
|
|
|
18.
Deb K. A fast and elitist multiobjective genetic algorithm:NSGA-II.
IEEE Transactions on Evolutionary Computation,2002,6(2):182-197
|
CSCD被引
3126
次
|
|
|
|
|
19.
Tripathi P K. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients.
Information Sciences,2007,177(22):5033-5049
|
CSCD被引
50
次
|
|
|
|
|
20.
Bleuler S. Multiobjective genetic programming:Reducing bloat using SPEA2.
IEEE Congress on Evolutionary Computation,2001:536-543
|
CSCD被引
1
次
|
|
|
|
|
了解气象卫星更多信息，请访问