|Table of Contents|

A multi-objective particle swarm optimization algorithmbased on Pareto correlation degree domination(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年04期
Page:
439-446
Research Field:
Publishing date:

Info

Title:
A multi-objective particle swarm optimization algorithmbased on Pareto correlation degree domination
Author(s):
Tang Kezong1Li Zuoyong2Zhan Tangsen1Li Fang1Jiang Yunhao1
1.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 2.Industrial Robot Application of Fujian University Engineering Research Center,Minjiang University,Fuzhou 350108,China
Keywords:
multi-objective optimization particle swarm optimization Pareto domination correlation degree diversity
PACS:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2019.43.04.009
Abstract:
In order to improve the convergence and diversity of multi-objective optimization algorithm,this paper proposes a multi-objective particle swarm optimization algorithm based on Pareto correlation degree domination(MOPSO-PCD). On the basis of strict compliance with the traditional Pareto domination programming,MOPSO-PCD integrates the grey relational analysis method into the evolutionary process of non-dominated solutions,and designs a new Pareto correlation association degree domination programming. In the selection process of the global optimal particle,the optimal particle with the largest correlation degree leads the particle swarm to approach the true Pareto frontier distribution. At the same time,the domination programming can also maintain the diversity of the non -dominated solutions in the external archive,and reduce or avoid the loss of the diversity of the final solution set,thus maintaining the distribution process of the non-dominated solutions of the external archive. Simulation results of ZDT and DTLZ test functions show that MOPSO-PCD has better Pareto optimal frontier distribution and faster convergence efficiency than three comparison algorithms.

References:

[1] Knowles J,Corne D. Properties of an adaptive archiving algorithm for storing nondominated vectors[J]. IEEE Transactions on Evolutionary Computation,2003,7(2):100-116.
[2]Wickramasinghe U K,Carrese R,Li X D. Designing airfoils using a reference point based evolutionary many-objective particle swarm optimization algorithm[C]//2010 IEEE World Congress on Computational Intelligence. Barceiona,Spain:IEEE,2010:1-8.
[3]Deb K,Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach,part i:solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation,2014,18(4):577-601.
[4]李笠,王万良,徐新黎,等. 基于网格排序的多目标粒子群优化算法[J]. 计算机研究与发展,2017,54(5):1012-1023.
Li L,Wang W L,Xu X L,et al. Multi-objective particle swarm optimization based on grid ranking[J]. Journal of Computer Research and Development,2017,54(5):101-1023.
[5]章恩泽,陈庆伟. 改进的r支配高维多目标粒子群优化算法[J]. 控制理论与应用,2015,32(5):623-630.
Zhan E Z,Chen Q W. Improved r-dominance-based swarm optimization for multi-objective optimization[J]. Control Theory & Applications,2015,32(5):32(5):623-630.
[6]Laumanns M,Thiele L,Deb K,et al. Combining convergence and diversity in evolutionary multiobjective optimization[J]. Evolutionary Computation,2002,10(3):263-282.
[7]Farina M,Amato P. A fuzzy definition of optimality for many-criteria optimization problems[J]. IEEE Transactions on Systems Man and Cybernetics-Part A Systems and Humans,2004,34(3):315-326.
[8]Yang S,Li M,Liu X,et al. A grid-based evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation,2013,17(5):721-736.
[9]Hu W,Yen G G. Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system[J]. IEEE Transactions on Evolutionary Computation,2015,19(1):1-18.
[10]Liang J J,Qin A K,Suganthan P N,et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE Transactions on Evolutionary Computation,2006,10(3):281-295.
[11]Zhan Z H,Zhang J,Li Y. et al. Adaptive particle swarm optimization[J]. IEEE Trans on System,Man,and Cybernetics-Part B:Cybernetics,2009,3996:1362-1381.
[12]汤可宗,丰建文,李芳,等. 多策略自适应粒子群优化算法[J]. 南京理工大学学报,2017,41(3):301-306.
Tang K Z,Feng J W,Li F,et al. Multi-strategy adaptive particle swarm optimization algorithm[J]. Journal of Nanjing University of Science and Technology,2017,41(3):301-306.
[13]Britto A,Pozo A. Using archiving methods to control convergence and diversity for many-objective problems in particle swarm optimization[C]//2012 IEEE Congress on Evolutionary Computation. Brisbane,QLD,Australia:IEEE,2012:1-8.
[14]Raquel C R,Jr P C N. An effective use of crowding distance in multiobjective particle swarm optimization[C]//Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. Washington DC,USA:ACM,2005:257-264.
[15]Deb K,Pratap A,Agarwal S,et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[16]Kukkonen S,Lampinen J. An empirical study of control parameters for the third version of generalized differential evolution(GDE3)[C]//2006 IEEE International Conference on Evolutionary Computation. Vancouver,BC,Canada:IEEE,2005:443-450.

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Last Update: 2019-09-30