Hybrid decomposition and strength Pareto multi-objective evolutionary algorithm

doi:10.11772/j.issn.1001-9081.2014.10.2880

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (10): 2880-2885.DOI: 10.11772/j.issn.1001-9081.2014.10.2880

• Artificial intelligence • Previous Articles Next Articles

Hybrid decomposition and strength Pareto multi-objective evolutionary algorithm

QIU Xingxing¹,ZHANG Zhenzhen²,WEI Qiming¹

1. School of Information Science and Technology, Jiujiang University, Jiujiang Jiangxi 332005, China;
2. College of Science, Jiujiang University, Jiujiang Jiangxi 332005, China

Received:2014-05-09 Revised:2014-07-02 Online:2014-10-30 Published:2014-10-01
Contact: QIU Xingxing

混合分解和强度帕累托多目标进化算法

邱兴兴¹,张珍珍²,魏启明¹

1. 九江学院信息科学与技术学院, 江西九江 332005;
2. 九江学院理学院, 江西九江 332005

通讯作者: 邱兴兴
作者简介:邱兴兴(1979-),男,江西九江人,讲师,硕士,CCF会员,主要研究方向:数据挖掘、进化计算；
张珍珍(1979-),女,江西九江人,讲师,硕士,主要研究方向:小波分析、软计算；
魏启明(1979-),男,江西九江人,副教授,硕士，主要研究方向:智能计算。
基金资助:
教育部人文社会科学研究项目;江西省教育厅科学基金项目

Abstract

Abstract:

In multi-objective evolutionary optimization, Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) with decomposition strategy is of low computational complexity, while Strength Pareto Evolutionary Algorithm-2 (SPEA2) 〖BP(〗with strength Pareto strategy 〖BP)〗can generate solution sets distributed uniformly. By combining these two strategies, a novel multi-objective evolutionary algorithm was proposed for solving Multi-objective Optimization Problem (MOP) with complex and discontinuous Pareto fronts in this paper. In the proposed algorithm, decomposition strategy was firstly used to make the solution set quickly approach the Pareto front; then strength Pareto strategy was used to make the solution set uniformly distributed along the Pareto front, and the weight vector set in decomposition strategy was rearranged based on this solution set, with a consequence of the weight vector set adapted to a particular Pareto front; and finally, decomposition strategy was used again to make the solution set further approach the Pareto front. In terms of Inverted Generational Distance (IGD) metric, the novel algorithm was compared with three state-of-art algorithms, MOEA/D, SPEA2 and paλ-MOEA/D on twelve benchmark problems. The experimental results indicate that the performance of the proposed algorithm is optimal on seven benchmark problems, and is approximately optimal on five benchmark problems. Moreover, the algorithm can generate uniformly distributed solution sets whether Pareto fronts of MOP are simple or complex, continuous or discontinuous.

摘要：

在多目标进化优化中，使用分解策略的基于分解的多目标进化算法(MOEA/D)时间复杂度低，使用〖BP(〗强度帕累托策略的〖BP)〗强度帕累托进化算法-2(SPEA2)能得到分布均匀的解集。结合这两种策略，提出一种新的多目标进化算法用于求解具有复杂、不连续的帕累托前沿的多目标优化问题(MOP)。首先,利用分解策略快速逼近帕累托前沿；然后,利用强度帕累托策略使解集均匀分布在帕累托前沿，利用解集重置分解策略中的权重向量集，使其适配于特定的帕累托前沿；最后,利用分解策略进一步逼近帕累托前沿。使用的反向世代距离(IGD)作为度量标准，将新算法与MOEA/D、SPEA2和paλ-MOEA/D在12个基准问题上进行性能对比。实验结果表明该算法性能在7个基准问题上最优，在5个基准问题上接近于最优，且无论MOP的帕累托前沿是简单或复杂、连续或不连续的，该算法均能生成分布均匀的解集。

CLC Number:

TP18

QIU Xingxing ZHANG Zhenzhen WEI Qiming. Hybrid decomposition and strength Pareto multi-objective evolutionary algorithm[J]. Journal of Computer Applications, 2014, 34(10): 2880-2885.

邱兴兴张珍珍魏启明. 混合分解和强度帕累托多目标进化算法[J]. 计算机应用, 2014, 34(10): 2880-2885.

References

［1］COELLO COELLO C A, LAMONT G B, van VELDHUIZEN D A. Evolutionary algorithms for solving multi-objective problems ［M］. 2nd ed. Berlin: Springer-Verlag, 2007.
［2］ZITZLER E, THIELE L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach ［J］. IEEE Transactions on Evolutionary Computation, 1999,3(4):257-271.
［3］ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm [EB/OL] . [2014-01-12] . http://www.kddresearch.org/Courses/Spring-2007/CIS830/Handouts/P8.pdf.
［4］CORNE D W, JERRAM N R, KNOWLES J D, et al.PESA-Ⅱ: region-based selection in evolutionary multiobjective optimization ［C］// Proceedings of the Genetic and Evolutionary Computation Conference. San Francisco: Morgan Kaufmann Publishers, 2001:283-290.
［5］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.
［6］QINGFU Z, HUI L. MOEA/D: a multiobjective evolutionary algorithm based on decomposition ［J］. IEEE Transactions on Evolutionary Computation, 2007,11(6):712-731.
［7］HUI L, QINGFU Z. Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-Ⅱ ［J］. IEEE Transactions on Evolutionary Computation, 2009,13(2):284-302.
［8］MIETTINEN K M. Nonlinear multiobjective optimization ［M］. Norwell: Kluwer Academic Publishers, 1999.
［9］GU F Q, LIU H L. A novel weight design in multi-objective evolutionary algorithm ［C］// Proceedings of the 2010 International Conference on Computational Intelligence and Security. Washington, DC: IEEE Computer Society, 2010:137-141.
［10］JIANG S, CAI Z, ZHANG J, et al.Multiobjective optimization by decomposition with Pareto-adaptive weight vectors ［C］// Proceedings of the 2011 7th International Conference on Natural Computation. Piscataway: IEEE Press, 2011,3:1260-1264
［11］DURILLO J J, NEBRO A J, ALBA E. The jMetal framework for multi-objective optimization: design and architecture ［C］// Proceedings of the 2010 IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2010:1-8.
［12］DURILLO J J, NEBRO A J. jMetal: a Java framework for multi-objective optimization ［J］. Advances in Engineering Software, 2011,42(10):760-771.
［13］ZITZLER E, DEB K, THIELE L. Comparison of multiobjective evolutionary algorithms: empirical results ［J］. Evolutionary Computation, 2000,8(2):173-195.
［14］ZHENG J. Multi-objective evolutionary algorithms and their applications ［M］. Beijing: Science Press, 2007.(郑金华.多目标进化算法及其应用［M］.北京:科学出版社,2007.)
［15］DEB K, THIELE L, LAUMANNS M, et al.Scalable multi-objective optimization test problems ［C］// Proceedings of the 2002 IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2002,1:825-830.
［16］ZITZLER E, THIELE L, LAUMANNS M, et al.Performance assessment of multiobjective optimizers: an analysis and review ［J］. IEEE Transactions on Evolutionary Computation, 2003,7(2):117-132.

[1]	LYU Le, ZHANG Bohan, JING Junchang, LIU Dong. Multiple target nodes hiding method based on permanence [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	. Privacy preserving localization of surveillance images based on large vision models [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Federated class-incremental learning method with multi-head self-attention for label semantic embedding [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	Jianli DING, Hui HUANG, Weidong CAO. Dynamic monitoring method of flight chain operation status [J]. Journal of Computer Applications, 2024, 44(12): 3941-3948.
[5]	Jingxin LIU, Wenjing HUANG, Liangsheng XU, Chong HUANG, Jiansheng WU. Unsupervised feature selection model with dictionary learning and sample correlation preservation [J]. Journal of Computer Applications, 2024, 44(12): 3766-3775.
[6]	Yifei SONG, Yi LIU. Fast adversarial training method based on data augmentation and label noise [J]. Journal of Computer Applications, 2024, 44(12): 3798-3807.
[7]	Yanran SHEN, Xin WEN, Jinhao ZHANG, Shuai ZHANG, Rui CAO, Baolu GAO. fMRI brain age prediction model with lightweight multi-scale convolutional network [J]. Journal of Computer Applications, 2024, 44(12): 3949-3957.
[8]	Zucuan ZHANG, Xuebin CHEN, Rui GAO, Yuanhuai ZOU. Federated learning client selection method based on label classification [J]. Journal of Computer Applications, 2024, 44(12): 3759-3765.
[9]	. Equivariant graph neural network-based simulator for lagrangian particle flow [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Multi-scale Information Fusion Time Series Long-term Forecasting Model Based on Neural Network [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Multi-target detection algorithm for traffic intersection images based on YOLOv9 [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	. Personalized learning recommendation in collaboration with knowledge graph and large language model [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	. Review of interpretable deep knowledge tracing methods [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	. Multi-scale Decorrelation Graph Convolutional Network [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Object detection uncertainty measurement scheme based on guide to the expression of uncertainty in measurement [J]. Journal of Computer Applications, 0, (): 0-0.

Hybrid decomposition and strength Pareto multi-objective evolutionary algorithm

混合分解和强度帕累托多目标进化算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics