混合分散搜索的进化多目标优化算法

doi:10.11772/j.issn.1001-9081.2014.10.2874

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (10): 2874-2879.DOI: 10.11772/j.issn.1001-9081.2014.10.2874

• Artificial intelligence • Previous Articles Next Articles

混合分散搜索的进化多目标优化算法

WU Kunan¹,²,YAN Xuanhui¹,²,CHEN Zhenxing¹,²,BAI Meng¹,²

1. Fujian Key Laboratory of Network Security and Cryptography (Fujian Normal University), Fuzhou Fujian 350007, China
2. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou Fujian 350007, China;

Received:2014-04-21 Revised:2014-06-12 Online:2014-10-30 Published:2014-10-01
Contact: YAN Xuanhui

混合分散搜索的进化多目标优化算法

吴坤安¹,²,严宣辉¹,²,陈振兴¹,²,白猛¹,²

1. 福建省网络安全与密码技术重点实验室(福建师范大学)，福州 350007
2. 福建师范大学数学与计算机科学学院，福州 350007

通讯作者: 严宣辉
作者简介:吴坤安（1987-），男，江西上饶人，硕士研究生，主要研究方向：计算智能、数据挖掘；严宣辉（1968-），男，福建福州人，副教授，博士研究生，主要研究方向：人工智能、网络安全；陈振兴（1988-），男，福建龙岩人，硕士研究生，主要研究方向：计算智能、数据挖掘；白猛（1989-）,男(回族)，河北定州人，硕士研究生，主要研究方向：计算智能、数据挖掘。
基金资助:
国家自然科学基金资助项目

Abstract

Abstract:

The diversity of population, the searching capability and the robustness are three key points to the multi-objective optimization problem, which directly affect the convergence of algorithm and the spread of solutions set. To better deal with above problems, a Scatter Search hybrid Multi-Objective Evolutionary optimization Algorithm (SSMOEA) was proposed. The SSMOEA followed the scatter search structure but designed a new selection strategy of diversity and integrated the method of co-evolution in the process of subset generation. Additionally, a novel adaptive multi-crossover operation was employed to improve the self-adaptability and robustness of the algorithm. The experimental results on twelve standard benchmark problems show that, compared with three state-of-the-art multi-objective optimizers, SPEA2, NSGA-Ⅱ and AbYSS, SSMOEA outperforms the other three algorithms as regards the coverage, uniformity and approximation. Meanwhile, its robustness is also significantly improved.

摘要：

在进化多目标优化算法中，种群的多样性、对目标空间的搜索能力及算法的鲁棒性直接影响算法的收敛能力和解集的分散性。针对这些问题，提出了一种混合分散搜索的进化多目标优化算法(SSMOEA)。SSMOEA在混合分散搜索算法架构的同时，重新设计其多样性的选取策略，并引入协同进化机制。此外，为了提高算法的自适应性和鲁棒性，采用了一种新颖的自适应多交叉算子选择方法。SSMOEA与经典的多目标进化算法SPEA2、NSGA-Ⅱ和MOEA/D在12个基准测试函数上的对比结果表明，SSMOEA不仅在求得的Pareto最优解集的宽广性、均匀性和逼近性上有明显优势，而且算法的鲁棒性也有明显的提高。

CLC Number:

TP183

WU Kunan YAN Xuanhui CHEN Zhenxing BAI Meng. 混合分散搜索的进化多目标优化算法[J]. Journal of Computer Applications, 2014, 34(10): 2874-2879.

吴坤安严宣辉陈振兴白猛. 混合分散搜索的进化多目标优化算法[J]. 计算机应用, 2014, 34(10): 2874-2879.

References

［1］DEB K. Multi-objective optimization using evolutionary algorithm ［M］. Chichester: Wiley, 2001.
［2］SCHAFFER J D. Multiple objective optimization with vector evaluated genetic algorithms ［C］// Proceedings of the 1st International Conference on Genetic Algorithms. Pittsburgh: Lawrence Erlbaum Associates, 1985:93-100.［3］DEB K, PRATAP A, AGARWAL S, et al.A fast and elitist multi-objective genetic algorithm: NSGA-Ⅱ ［J］. IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197.
［4］ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm ［EB/OL］. ［2014-01-13］. http://wenku.baidu.com/view/6620436aa98271fe910ef928.html.
［5］NEBRO A J, DURILLO J J, GARCIA-NIETO J, et al.SMPSO: A new PSO-based metaheuristic for multi-objective optimization ［C］// Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making. Piscataway: IEEE Press, 2009:66-73.
［6］YANG D, JIAO L, GONG M, et al.Adaptive ranks and K-nearest neighbor list based multiobjective immune algorithm ［J］. Computational Intelligence, 2010,26(4):359-385.
［7］ZHANG Q, LI H. MOEA/D: a multiobjective evolutionary algo-rithm based on decomposition ［J］. IEEE Transactions on Evolutionary Computation, 2007,11(6):712-731.
［8］LAGUNA M, MARTI R. Scatter search ［M］// ALBA E, MARTI R. Metaheuristic Procedures for Training Neutral Networks. Berlin: Springer-Verlag, 2006:139-152.
［9］GLOVER F, LAGUNA M, MARTI R. Fundamentals of scatter search and path relinking ［J］. Control and Cybernetic, 2000,29(3):653-684.
［10］BEAUSOLEIL R P. "MOSS": multiobjective scatter search applied to non-linear multiple criteria optimization ［J］. European Journal of Operational Research, 2006,169(2):426-449.
［11］MOLINA J, LAGUNA M, MARTI R, et al.SSPMO: a scatter tabu search procedure for non-linear multiobjective optimization ［J］. Informs Journal on Computing, 2007,19(1):91-100.
［12］SALAZAR-AGUILAR M A, ROS-MERCADO R Z, GONZLEZ-VELARDE J L, et al.Multiobjective scatter search for a commercial territory design problem ［J］. Annals of Operations Research, 2012,199(1):343-360.
［13］MARSIL A C, CARLOS E K, VIVIANA C M, et al.Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem ［J］. Energy, 2013,53:14-21.
［14］KARTHIK S, KAISA M, DEB K. A hybrid framework for evolutionary multi-objective optimization ［J］. IEEE Transactions on Evolutionary Computation, 2013,17(4):495-511.
［15］QIAN C, YU Y, ZHOU Z H. An analysis on recombination in multi-objective evolutionary optimization ［C］// Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. New York: ACM, 2011:2051-2058.
［16］ELSAYED S M, SARKER R A, ESSAM D L. A comparative study of different variants of genetic algorithms for constrained optimization ［C］ // Proceedings of the 8th International Conference on Simulated Evolution and Learning. Berlin: Springer-Verlag, 2010:177-186.
［17］ESHELMAN L J, SCHAFFER J D. Real-coded genetic algorithms and interval-schemata ［C］// Proceedings of the 1992 2nd Workshop on Foundations of Genetic Algorithms. San Mateo: Morgan Kaufmann,1993:187-202.
［18］DEB K, AGRAWAL R B. Simulated binary crossover for continuous search space ［J］. Complex Systems, 1995,9(2):115-148.
［19］TSUTSUI S, YAMAMURA M, HIGUCHI T. Multi-parent recombination with simplex crossover in real coded genetic algorithms ［C］// Proceedings of the Genetic and Evolutionary Computation Conference. San Mateo: Morgan Kaufmann,1999:657-664.
［20］DEB K, JOSHI D, ANAND A. Real-coded evolutionary algorithms with parent-centric recombination ［C］// Proceedings of 2002 Congress on Evolutionary Computation. Piscataway: IEEE Press, 2002,1:61-66.
［21］CARLOS M F, PETER J F. Multi-objective optimization and multiple constraint handling with evolutionary algorithms—Part II: application example ［J］. IEEE Transactions on Systems, Man and Cybernetics, 1998,28(1):38-47.
［22］ZITZLER E, DEB K, THIELE L. Comparison of multi-objective evolutionary: empirical results ［J］. Evolutionary Computation, 2000,8(2):173-195.
［23］DEB K, THIELE L, LAUMANNS M, et al.Scalable test problems for evolutionary multiobjective optimization ［C］ // Evolutionary Multiobjective Optimization, Theoretical Advance and Applications. Berlin: Springer-Verlag, 2005:825-830.
［24］VELDHUIZEN D A V. Multiobjective evolutionary algorithms: classifications, analyses, and new innovation ［D］. Ohio: Air Force Institute of Technology Department of Electrical Engineering and Computer Engineering, 1998.
［25］ZHOU A M, JIN Y C, ZHANG Q F, et al.Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion ［C］// IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2006:892-899.

[1]	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.
[2]	. Equivariant graph neural network-based simulator for lagrangian particle flow [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Multi-scale Information Fusion Time Series Long-term Forecasting Model Based on Neural Network [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Multi-scale Decorrelation Graph Convolutional Network [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	. Object detection uncertainty measurement scheme based on guide to the expression of uncertainty in measurement [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	Nengbing HU, Biao CAI, Xu LI, Danhua CAO. Graph classification method based on graph pooling contrast learning [J]. Journal of Computer Applications, 2024, 44(11): 3327-3334.
[7]	Fu LIN, Jiasheng SHI, Ze GAO, Zunkang CHU, Qiongmin MA, Haiyan YU, Weixiong RAO. Physical system simulation based on deep representation learning for 3D geometric features [J]. Journal of Computer Applications, 2024, 44(11): 3548-3555.
[8]	Yiyang FAN, Yang ZHANG, Shang ZENG, Yu ZENG, Maoli FU. Multivariate long-term series forecasting model based on decomposition and frequency domain feature extraction [J]. Journal of Computer Applications, 2024, 44(11): 3442-3448.
[9]	Wenbo LIU, Lianfei YU, Dongmei XIE, Chuang CAI, Zhijian QU, Chongguang REN. Long-term prediction model of time series based on multi-scale feature fusion [J]. Journal of Computer Applications, 2024, 44(11): 3435-3441.
[10]	. Medical image segmentation network with content-guided multi-angle feature fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	WANG Xiang, CHEN Zhixiang, MAO Guojun. Multivariate time series prediction method combining local and global correlation#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	Wenjuan JIANG, Yi GUO, Jiaojiao FU. Reasoning question answering model of complex temporal knowledge graph with graph attention [J]. Journal of Computer Applications, 2024, 44(10): 3047-3057.
[13]	Hua HUANG, Ziyi YANG, Xiaolong LI, Chuang LI. Predictive business process monitoring method based on concept drift [J]. Journal of Computer Applications, 2024, 44(10): 3167-3176.
[14]	Rui ZHANG, Junming PAN, Xiaolu BAI, Jing HU, Rongguo ZHANG, Pengyun ZHANG. Agent model for hyperparameter self-optimization of deep classification model [J]. Journal of Computer Applications, 2024, 44(10): 3021-3031.
[15]	. Pedestrian trajectory prediction based on graph convolutional network and endpoint induction [J]. Journal of Computer Applications, 0, (): 0-0.

混合分散搜索的进化多目标优化算法

混合分散搜索的进化多目标优化算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics