Full Text:   <9278>

Summary:  <416>

Suppl. Mater.: 

CLC number: O224

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2022-04-14

Cited: 0

Clicked: 2682

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Luda ZHAO

https://orcid.org/0000-0002-7476-5896

Bin WANG

https://orcid.org/0000-0003-0593-3531

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.11 P.1714-1732

http://doi.org/10.1631/FITEE.2100508


DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers


Author(s):  Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU

Affiliation(s):  College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; more

Corresponding email(s):   zhaoluda@nudt.edu.cn, wbeeinudt@126.com

Key Words:  Multi-objective evolutionary algorithm (MOEA), Formalizing preference of decision makers, Population renewal strategy, Preference interaction


Share this article to: More <<< Previous Article|

Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU. DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1714-1732.

@article{title="DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers",
author="Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="11",
pages="1714-1732",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100508"
}

%0 Journal Article
%T DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers
%A Luda ZHAO
%A Bin WANG
%A Xiaoping JIANG
%A Yicheng LU
%A Yihua HU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 11
%P 1714-1732
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100508

TY - JOUR
T1 - DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers
A1 - Luda ZHAO
A1 - Bin WANG
A1 - Xiaoping JIANG
A1 - Yicheng LU
A1 - Yihua HU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 11
SP - 1714
EP - 1732
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100508


Abstract: 
The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid interactive preference based MOEA (DIP-MOEA), which explicitly takes the preferences of decision makers (DMs) into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preferences of DMs, the membership functions are mapped to generate a decision preference grid and a preference error grid. Then, we put forward two dominant modes of population, preference degree dominance and preference error dominance, and use this advantageous scheme to update the population in these two grids. Finally, the populations in these two grids are combined with the DMs’ preference interaction information, and the preference multi-objective optimization interaction is performed. To verify the performance of DIP-MOEA, we test it on two kinds of problems, i.e., the basic DTLZ series functions and the multi-objective knapsack problems, and compare it with several different popular preference-based MOEAs. Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs, quickly provides the test results, and has better performance in the distribution of the Pareto front solution set.

DIP-MOEA:一种形式化表达决策者偏好的双重网格交互偏好多目标进化算法

赵禄达1,2,王斌1,2,姜晓平1,2,卢义成3,胡以华1,2
1国防科技大学电子对抗学院,中国合肥市,230037
2国防科技大学第三学科交叉中心,中国合肥市,230037
3中国人民解放军78092部队,中国成都市,610000
摘要:几乎所有现有的基于偏好的多目标进化算法(MOEA)给出的最终解集都与决策者偏好信息的表示存在一定距离。因此,提出一种多目标优化算法,称为双重网格交互式基于偏好的多目标进化算法(DIP-MOEA),该算法明确考虑了决策者偏好。首先根据实际多目标优化问题(MOPs)的优化目标和决策者偏好映射隶属度函数,生成决策偏好度网格和偏好误差网格。其次,提出偏好度支配和偏好误差支配两种种群支配方式,并利用该方案更新两个网格中的种群。最后综合两个网格中的种群并结合决策者偏好交互信息可进行偏好多目标优化交互。为验证DIP-MOEA性能,我们在基本DTLZ系列函数和多目标背包问题上对DIP-MOEA进行测试,并将其与几种流行的基于偏好的多目标进化算法进行比较。实验结果表明,DIP-MOEA能较好表达决策者偏好信息,提供满足决策者偏好的解集,快速求解测试问题结果,并在最终解集的Pareto前沿分布性具有较好表现。

关键词:多目标进化算法(MOEA);决策者偏好形式化;种群更新策略;偏好交互

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Akram M, Kahraman C, Zahid K, 2021. Group decision-making based on complex spherical fuzzy VIKOR approach. Knowl-Based Syst, 216:106793.

[2]Altuzarra A, Moreno-Jiménez JM, Salvador M, 2007. A Bayesian priorization procedure for AHP-group decision making. Eur J Oper Res, 182(1):367-382.

[3]Bazgan C, Hugot H, Vanderpooten D, 2009. Solving efficiently the 0–1 multi-objective knapsack problem. Comput Oper Res, 36(1):260-279.

[4]Cai XY, Xiao YS, Li MQ, et al., 2021. A grid-based inverted generational distance for multi/many-objective optimization. IEEE Trans Evol Comput, 25(1):21-34.

[5]Champasak P, Panagant N, Pholdee N, et al., 2020. Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle. Aerosp Sci Technol, 100:105783.

[6]Chen XQ, Lai CS, Ng WWY, et al., 2021. A stochastic sensitivity-based multi-objective optimization method for short-term wind speed interval prediction. Int J Mach Learn Cybern, 12(9):2579-2590.

[7]Cheng R, Jin YC, Olhofer M, et al., 2016. A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput, 20(5):773-791.

[8]Chiu WY, Manoharan SH, Huang TY, 2020. Weight induced norm approach to group decision making for multi-objective optimization problems in systems engineering. IEEE Syst J, 14(2):1580-1591.

[9]Cui ZH, Zhang JJ, Wu D, et al., 2020. Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Inform Sci, 518:256-271.

[10]Deb K, 2001. Nonlinear goal programming using multi-objective genetic algorithms. J Oper Res Soc, 52(3):291-302.

[11]Deb K, Pratap A, Agarwal S, et al., 2002a. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput, 6(2):182-197.

[12]Deb K, Thiele L, Laumanns M, et al., 2002b. Scalable multi-objective optimization test problems. Proc Congress on Evolutionary Computation, p.825-830.

[13]Dong MG, Liu B, Jing C, 2020. A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization. Front Inform Technol Electron Eng, 21(8):1171-1190.

[14]George T, Amudha T, 2020. Genetic algorithm based multi-objective optimization framework to solve traveling salesman problem. In: Sharma H, Govindan K, Poonia R (Eds.), Advances in Computing and Intelligent Systems: Algorithms for Intelligent Systems. Springer, Singapore.

[15]Huang CZ, Yuan HW, Geng YM, 2021. Multi-objective preference optimization method of permanent magnet assisted reluctance motor. Proc 17th Int Conf on Computational Intelligence and Security, p.413-419.

[16]Ishibuchi H, Matsumoto T, Masuyama N, et al., 2020. Effects of dominance resistant solutions on the performance of evolutionary multi-objective and many-objective algorithms. Proc Genetic and Evolutionary Computation Conf, p.507-515.

[17]Jakubovski Filho HL, Ferreira TN, Vergilio SR, 2019. Preference based multi-objective algorithms applied to the variability testing of software product lines. J Syst Softw, 151:194-209.

[18]Jiang SW, Zhang J, Ong YS, et al., 2015. A simple and fast hypervolume indicator-based multiobjective evolutionary algorithm. IEEE Trans Cybern, 45(10):2202-2213.

[19]Lai G, Liao MH, Li K, 2021. Empirical studies on the role of the decision maker in interactive evolutionary multi-objective optimization. Proc IEEE Congress on Evolutionary Computation, p.185-192.

[20]Li H, Deng JD, Zhang QF, et al., 2019. Adaptive Epsilon dominance in decomposition-based multiobjective evolutionary algorithm. Swarm Evol Comput, 45:52-67.

[21]Li HR, He FZ, Yan XH, 2019. IBEA-SVM: an indicator-based evolutionary algorithm based on pre-selection with classification guided by SVM. Appl Math A J Chin Univ, 34(1):1-26.

[22]Li LM, Wang YL, Trautmann H, et al., 2018. Multiobjective evolutionary algorithms based on target region preferences. Swarm Evol Comput, 40:196-215.

[23]Lin BR, Chen HZ, Liu YC, et al., 2021. A preference-based multi-objective building performance optimization method for early design stage. Build Simul, 14(3):477-494.

[24]Liu QQ, Jin YC, Heiderich M, et al., 2020. An adaptive reference vector-guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems. IEEE Trans Cybern, 52(5):2698-2711.

[25]Liu RC, Song XL, Fang LF, et al., 2017. An r-dominance-based preference multi-objective optimization for many-objective optimization. Soft Comput, 21(17):5003-5024.

[26]López-Jaimes A, Coello CAC, 2014. Including preferences into a multiobjective evolutionary algorithm to deal with many-objective engineering optimization problems. Inform Sci, 277:1-20.

[27]Louis SJ, McDonnell J, 2004. Learning with case-injected genetic algorithms. IEEE Trans Evol Comput, 8(4):316-328.

[28]Luo WJ, Shi LM, Lin X, et al., 2019. The ĝ-dominance relation for preference-based evolutionary multi-objective optimization. Proc IEEE Congress on Evolutionary Computation, p.2418-2425.

[29]Marler RT, Arora JS, 2010. The weighted sum method for multi-objective optimization: new insights. Struct Multidisc Optim, 41(6):853-862.

[30]Menchaca-Méndez A, Montero E, Antonio LM, et al., 2019. A co-evolutionary scheme for multi-objective evolutionary algorithms based on ϵ-dominance. IEEE Access, 7:18267-18283.

[31]Miguel F, Frutos M, Tohmé F, et al., 2019. A decision support tool for urban freight transport planning based on a multi-objective evolutionary algorithm. IEEE Access, 7:156707-156721.

[32]Mohammadi A, Omidvar MN, Li XD, 2013. A new performance metric for user-preference based multi-objective evolutionary algorithms. Proc IEEE Congress on Evolutionary Computation, p.2825-2832.

[33]Paknejad P, Khorsand R, Ramezanpour M, 2021. Chaotic improved PICEA-g-based multi-objective optimization for workflow scheduling in cloud environment. Fut Gener Comput Syst, 117:12-28.

[34]Pirouz B, Khorram E, 2016. A computational approach based on the ϵ-constraint method in multi-objective optimization problems. Adv Appl Stat, 49(6):453-483.

[35]Premkumar M, Jangir P, Kumar BS, et al., 2021. A new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: diversity analysis and validations. IEEE Access, 9:84263-84295.

[36]Sudeng S, Wattanapongsakorn N, 2013. Adaptive geometric angle-based algorithm with independent objective biasing for pruning Pareto-optimal solutions. Proc Science and Information Conf, p.514-523.

[37]Sudeng S, Wattanapongsakorn N, 2015. Post Pareto-optimal pruning algorithm for multiple objective optimization using specific extended angle dominance. Eng Appl Artif Intell, 38:221-236.

[38]Sun YN, Yen GG, Yi Z, 2019. IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Trans Evol Comput, 23(2):173-187.

[39]Wahid A, Gao XY, Andreae P, 2015. Multi-objective clustering ensemble for high-dimensional data based on strength Pareto evolutionary algorithm (SPEA-II). Proc IEEE Int Conf on Data Science and Advanced Analytics, p.1-9.

[40]Wang F, Li YX, Zhang H, et al., 2019. An adaptive weight vector guided evolutionary algorithm for preference-based multi-objective optimization. Swarm Evol Comput, 49:220-233.

[41]Wang R, Purshouse RC, Fleming PJ, 2013. Preference-inspired co-evolutionary algorithm using adaptively generated goal vectors. Proc IEEE Congress on Evolutionary Computation, p.916-923.

[42]Wang R, Purshouse RC, Giagkiozis I, et al., 2015a. The iPICEA-g: a new hybrid evolutionary multi-criteria decision making approach using the brushing technique. Eur J Oper Res, 243(2):442-453.

[43]Wang R, Purshouse RC, Fleming PJ, 2015b. Preference-inspired co-evolutionary algorithms using weight vectors. Eur J Oper Res, 243(2):423-441.

[44]Yu G, Zheng J, Shen R, et al, 2016. Decomposing the user-preference in multiobjective optimization. Soft Comput, 20(10):4005-4021.

[45]Zhang QF, Li H, 2007. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput, 11(6):712-731.

[46]Zhang ZX, Chen WN, Jin H, et al., 2021. A preference biobjective evolutionary algorithm for the payment scheduling negotiation problem. IEEE Trans Cybern, 51(12):6105-6118.

[47]Zhao LD, Wang B, Shen CY, 2021. A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation. PLoS ONE, 16(6):e0252293.

[48]Zheng JH, Xie ZZ, 2014. A study on how to use angle information to include decision maker's preferences. Acta Electron Sin, 42(11):2239-2246 (in Chinese).

[49]Zheng JH, Lai N, Guo GQ, 2014. ϵ-Pareto dominance strategy based on angle preference in MOEA. PR AI, 27(6):569-576 (in Chinese).

[50]Zitzler E, Künzli S, 2004. Indicator-based selection in multiobjective search. Proc 8th Int Conf on Parallel Problem Solving from Nature, p.832-842.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE