MOEA/D Homepage

 

 

MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition) is a generic algorithm framework.  It decomposes a multiobjective optimization problem into a number of different single objective optimization subproblems and defines neighbourhood relations among these subproblems.  Then a population-based method is used to optimize these subproblems simultaneously.  Each subproblem is optimized by using information mainly from its neighbouring subproblems.  The source codes of MOEA/D can be found in Qingfu Zhang’s homepage,  Jmetal,  MOEA Framework  and MOS web .

Research Papers on MOEA/D

  1. Q.  Zhang and  H. Li,  MOEA/D: A Multi-objective Evolutionary Algorithm Based on Decomposition, IEEE Trans. on Evolutionary Computation, vol.11, no. 6, pp712-731  2007. C++Code:  continuous MOP and knapsack problem.  Matlab Code.  Java Code (written by Wudong Liu).

      A simple version of MOEA/D is introduced in this paper.  It won the IEEE TEVC Outstanding Paper Award.

  1.  H. Li and Q. Zhang,  Multiobjective Optimization Problems with Complicated Pareto Sets,  MOEA/D and NSGA-II, IEEE Trans on Evolutionary Computation, vol. 12,  no 2,  pp 284-302, April/2009,   paper (pdf) and C++ code

      Two different neighbourhoods are used and a new solution is allowed to replace a very small number of old solutions in this version. 

  1. Q. Zhang,  W. Liu,  and H Li, The Performance of a New Version of MOEA/D on CEC09 Unconstrained MOP Test Instances, Working Report CES-491, School of CS & EE, University of Essex, 02/2009.   paper (pdf) and source code,

      Noting that different subproblems require different amounts of computational resources. A strategy for dynamical resource allocation is introduced in this version. It won the CEC2009 Competition.

  1. Q. Zhang,   W. Liu,   E. Tsang and B. Virginas, Expensive Multiobjective Optimization by MOEA/D with Gaussian Process Model,  paper (pdf) and source code.   IEEE Trans on Evolutionary Computation, vol. 14, no.3, pp 456-474, 2010.

      It uses EGO in MOEA/D for dealing with expensive MOPs.

  1. H. Ishibuchi, Yuji Sakane, Noritake Tsukamoto, and Y. Nojima, Adaptation of scalarizing functions in MOEA/D: An adaptive scalarizing function-based multiobjective evolutionary algorithm," Lecture Notes in Computer Science 5467: Evolutionary Multi-Criterion Optimization – EMO 2009, pp. 438-452, Springer, Berlin, April 2009.
  2. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Effects of using two neighborhood structures on the performance of cellular evolutionary algorithms for many-objective optimization," Proc. of 2009 IEEE Congress on Evolutionary Computation, pp. 2508-2515, Trondheim, Norway, May 18-21, 2009.
  3. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations," Proc. of 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1820-1825, San Antonio, USA, October 10-13, 2009.
  4. H. Ishibuchi, Y. Sakane, N. Tsukamoto and Y. Nojima, "Simultaneous use of different scalarizing functions in MOEA/D," Proc. of Genetic and Evolutionary Computation Conference - GECCO 2010, pp. 519-526, Portland, USA, July 7-11, 2010    

     It proposes two approaches for using different aggregation functions simultaneously.

  1. A.J. Nebro, J.J. Durillo, A Study of the Parallelization of the Multi-Objective Metaheuristic MOEA/D,  Learning and Intelligent Optimization (LION 4), pp: 303-317. January 2010.
  2. Y. Mei, K. Tang and X. Yao, ``Decomposition-Based Memetic Algorithm for Multi-Objective Capacitated Arc Routing Problem,'' IEEE Transactions on Evolutionary Computation, Accepted.

            A combination of MOEA/D and NSGA-II is proposed for dealing with a hard multiobjective optimization problem.

  1. Pieter Palmers, Trent McConaghy, Michiel Steyaert, Georges G. E. Gielen: Massively multi-topology sizing of analog integrated circuits. DATE 2009: 706-711

           Each suproblem records more than one solution to maintain search diversity.

  1. Hui Li and Dario Landa-Silva, An Adaptive Evolutionary Multi-objective Approach Based on Simulated Annealing. To Appear in: MIT Evolutionary Computation Journal. 2010

      Simulated Annealing + MOEA/D is proposed for handling combinatorial problems.  

  1. Noura Al Moubayed, Andrei Petrovski and John McCall, A Novel Multi-Objective Particle Swarm Optimisation based on Decomposition, PPSN 2010.

     MOEA/D+PSO is proposed for continuous problem.

  1. S. Pal, S. Das, A. Basak, and P. N. Suganthan, "Synthesis of difference patterns for monopulse antennas with optimal combination of array-size and number of subarrays --- a multi-objective optimization approach," Progress In Electromagnetics Research B, Vol. 21, 257-280, 2010.
  2. S. Pal, B. Qu, S. Das, and P. N. Suganthan, "Linear antenna array synthesis with constrained multi-objective differential evolution," Progress In Electromagnetics Research B, Vol. 21, 87-111, 2010.
  3. Guerra-Gomez, I.;   Tlelo-Cuautle, E.;   McConaghy, T.;   Gielen, G.;  Decomposition-based multi-objective optimization of second-generation current conveyors, Circuits and Systems, 2009. MWSCAS '09. 52nd IEEE International Midwest Symposium on  Issue Date: 2-5 Aug. 2009  pp 220 – 223. 
  4. I. Guerra-G´omeza  et al,   Sizing mixed-mode circuits by multi-objective evolutionary algorithms,   53rd IEEE International Midwest Symposium on Circuits and Systems, 2010.
  5. I. Guerra-G´omeza  et al,  Optimizing Current Conveyors by Evolutionary Algorithms Including Differential Evolution, Electronics, Circuits, and Systems, 2009. ICECS 2009. 16th IEEE International Conference on , Issue Date: 13-16 Dec. 2009 On page(s): 259 - 262 
  6. Chen, C.-M., Chen, Y.-p., Shen, T.-C., & Zao, J. Optimizing degree distributions in LT codes by using the multiobjective evolutionary algorithm based on decomposition. In Proceedings of 2010 IEEE Congress on Evolutionary Computation (CEC 2010) (pp. 3635–3642).
  7. Chen, C.-M., Chen, Y.-p., & Zhang, Q. Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization. In Proceedings of 2009 IEEE Congress on Evolutionary Computation (CEC 2009) (pp. 209–216).
  8. Yung-Hsiang Chan, Tsung-Che Chiang, and Li-Chen Fu, A Two-phase Evolutionary Algorithm for Multiobjective Mining of Classification Rules, CEC 2010.
  9. Pei-Chann Chang, Shih-Shin Chen, Qingfu Zhang (2008), MOEA/D for Flowshop Scheduling Problems, Proceeding of Congress of Evolutionary Computation 2008 (CEC 2008), Hong Kong
  10. Hai-Lin Liu  Fang-qing Gu  Yiu-ming Cheung, T-MOEA/D: MOEA/D with Objective Transform in Multi-objective Problems  Information Science and Management Engineering (ISME), 2010 International Conference of , 2010.
  11. Tey Jing Yuen, Rahizar Ramli, Comparison of Computational Efficiency of MOEA/D and NSGA-II For Passive Vehicle Suspension Optimization, ECMS 2010.
  12.  Antony Waldocka, David Corne, Multiple Objective Optimisation applied to Route Planning, SEAS DTC Fifth Conference Proceedings, 2010.

MOEA/D is tested on a very interesting routing problem.

  1.   Bo Liu, Francisco V. Fernández, Qingfu Zhang, Murat Pak, Suha Sipahi, Georges G. E. Gielen: An enhanced MOEA/D-DE and its application to multiobjective analog cell sizing. IEEE Congress on Evolutionary Computation 2010
  2.   Qingfu Zhang, Hui Li, Dietmar Maringer, Edward Tsang: MOEA/D with NBI-style Tchebycheff approach for portfolio management. IEEE Congress on Evolutionary Computation 2010.

A new decomposition approach is proposed in this paper.

  1. Andreas Konstantinidis, Christoforos Charalambous, Aimin Zhou, Qingfu Zhang: Multi-objective mobile agent-based Sensor Network Routing using MOEA/D. IEEE Congress on Evolutionary Computation 2010.
  2. Andreas Konstantinidis, Kun Yang and Qingfu Zhang, "Problem-specific Encoding and Genetic Operation for a Multi-Objective Deployment and Power Assignment Problem in Wireless Sensor Networks", IEEE International Conference on Communications, ICC'09 AHSN, June 2009.
  3. Joao A. Duro, Qingfu Zhang, Dhish Kumar Saxena, and Ashutosh Tiwari, Framework for Many-objective Test Problems with both Simple and Complicated Pareto-set Shapes, 2010. Working report.  

     MOEA/D is tested on  many-objective problems.

  1. L.  Ke, Q. Zhang and R. Battiti,   Multiobjective Combinatorial Optimization by Using Decomposition and Ant Colony, 2010. Working Report.

           MOEA/D with Ant Colony Optimization.

  1. Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Hiroyuki Ohyanagi and Yusuke Nojima,  Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D, EMO 2011.
  2. Zapotecas Martínez, S. & Coello Coello, A Multi-objective Particle Swarm Optimizer Based on Decomposition, In Proceedings of the 13th annual conference on Genetic and Evolutionary Computation (GECCO'2011).   MOEA/D+ PSO   
  3. Karthik Sindhya, Sauli Ruuska, Tomi Haanpää and Kaisa Miettinen, A new hybrid mutation operator for multiobjective optimization with differential evolution, SOFT COMPUTING - A FUSION OF FOUNDATIONS, METHODOLOGIES AND APPLICATIONS, March/2011. MOEA/D+ Nonlinear Crosover/Mutation.  
  4. N. Al Moubayed, A. Petrovski and J. McCall, Multi-Objective Optimisation of Cancer Chemotherapy using Smart PSO with Decomposition,In 3rd IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making in conjunction with IEEE Symposium Series on Computational Intelligence (SSCI 2011), April 2011, Paris, France.
  5. Lai Yung-Pin, Multiobjective Optimization using MOEA/D with a New Mating Selection Mechanism, MSc Thesis, 2009, Taiwan Normal University, Taiwan.
  6. Zhang Jiandong et al, The Research on Multiple-impulse Correction Submunition Multi-objective Optimization Based on MOEA/D , Journal of Projectiles, Rockets, Missiles and Guidance,  2010-02.
  7. Andreas Konstantinidis and Kun Yang,  Multi-objective Energy-efficient Dense Deployment in Wireless Sensor Networks using a Hybrid Problem-specific MOEA/D, Applied Soft Computing, 2011–01.
  8. R. De. Carvalho, et al., An efficient algorithm for multiobjective optimization problems based on mathematical decomposition and evolutionary computation, XVIII SIMPÓSIO DE ENGENHARIA DE PRODUÇÃO Gestão de projetos e Engenharia de produção,  Bauru, SP, Brasil,  08 a 10 de novembro de 2010.
  9. Hisao Ishibuchi and Yusuke Nojima, Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning, SOFT COMPUTING - A FUSION OF FOUNDATIONS, METHODOLOGIES AND APPLICATIONS, 2010.  
  10. Juan J. Durillo, Qingfu Zhang, Antonio J. Nebro and Enrique Alba, Distribution of Computational Effort in Parallel MOEA/D, LION5, 2011.
  11. Qingbin Zhang, et al, Fuel-time Multiobjective Optimal Control of Flexible Structures Based on MOEA/D, Journal of National University of Defense Technology, 2009-06.
  12. F. Gu and H.L. Liu, A Novel Weight Design in Multi-objective Evolutionary Algorithm, 2010 International Conference on Computational Intelligence and Security.
  13. W. Huang and H. Li,  "On the differential evolution schemes in MOEA/D",  in Proc. ICNC, 2010, pp.2788-2792.
  14. Aniruddha Basak, Siddharth Pal, V. Ravikumar Pandi, Bijaya K. Panigrahi, Manas Kumar Mallick, Ankita Mohapatra: A Novel Multi-objective Formulation for Hydrothermal Power Scheduling Based on Reservoir End Volume Relaxation. SEMCCO 2010: 718-726  (MOEA/D-DE for optimal power generation).
  15. Md Nasir, A. K. Mondal1, S. Sengupta, Swagatam Das  and Ajith Abraham, An Improved Multiobjective Evolutionary Algorithm based on Decomposition with Fuzzy Dominance, CEC 2011.
  16. Hisao Ishibuchi, Naoya Akedo, Hiroyuki Ohyanagi, and Yusuke Nojima, Behavior of EMO Algorithms on Many-Objective Optimization Problems with Correlated Objectives, CEC 2011.
  17. Tsung-Che Chiang and Yung-Pin Lai, MOEA/D-AMS: Improving MOEA/D by an Adaptive Mating Selection Mechanism, CEC 2011.
  18. Wenping Ma, Bao Fu, Maoguo Gong and Haifeng Du, Community Detection in Complex Network By Using Multi-Objective Evolutionary Algorithm based on Decomposition, CEC2011.
  19. Esteban Tlelo-Cuautle, et al,  Evolutionary Algorithms in the Optimal Sizing of Analog Circuits, INTELLIGENT COMPUTATIONAL OPTIMIZATION IN ENGINEERING,  Studies in Computational Intelligence, 2011, Volume 366/2011, 109-138.
  20. Chen, Yikai, Yang, Shiwen and Nie, Zaiping, Improving conflicting specifications of time-modulated antenna arrays by using a multiobjective evolutionary algorithm, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2011, pp1099-1204.
  21. Andreas Konstantinidis, Haris Haralambous, Alexandros Agapitos and Harris Papadopoulos, "GP-MOEA/D Approach for Modelling Total Electron Content over Cyprus", International Journal of Engineering Intelligent Systems, 2011.
  22. Andreas Konstantinidis and Kun Yang, "Multiobjective K-Connected Deployment and Power Assignment in WSNs using a Problem-specific Constrained Evolutionary Algorithm based on Decomposition", Computer Communications, vol.34-1, pp. 83-98, January 2011.
  23. Siwei Jiang, Zhihua Cai, Jie Zhang, Yew-Soon Ong, Multiobjective Optimization by Decomposition with Pareto-adaptive Weight Vectors, 2011 7th International Conference on Natural Computation. 2011.
  24.  S-Z. Zhao, P. N. Suganthan and Q. Zhang,  Decomposition Based Multiobjective Evolutionary Algorithm with an Ensemble of Neighbourhood Sizes, IEEE Trans on Evolutionary Computation, 2011,  accepted
  25. Yung-Hsiang Chan, Multiobjective Evolutionary Algorithm for Rule Extraction in Data Mining, MSc Thesis, Taiwan University, 2010.
  26. Ahmed Kafafy, Ahmed Bounekkar, Stéphane Bonnevay: A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems. GECCO 2011: 497-504.   (GRASP in MOEA/D)
  27. Jian-Ping Li Alastair Wood,  Reliability Redundancy Optimization using MOEA/D, the 11th Annual Workshop on Computational Intelligence (UKCI2011)
  28. Carolina P. Almeida, Richard A. Gonçalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, An experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem,  Annals of Operation Research,  Oct/2011.  MOTA/D=MOEA/D+TA
  29. Jixang Cheng, Gexiang Zhang, Zhidan Li and Yuquan Li, Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems, Soft Cmputing,  Sept/2011, MOEA/D+ACO.
  30. T McConaghy et al, Trustworthy Genetic Programming-Based Synthesis of Analog Circuit Topologies Using Hierarchical Domain-Specific Building Blocks, IEEE Trans on Evolutionary Computation,  2011, No4. Vol. 15.  MOEA/D with multiple solutions for each subproblem.  
  31. Saldanha, R.R. ;  Gomes, B.N. ;  Lisboa, A.C. ;  Martins, A.X.  A Multi-Objective Evolutionary Algorithm Based on Decomposition for Optimal Design of Yagi-Uda Antennas, Magnetics, IEEE Transactions on, 2012, No.2 Vol. 48.
  32. Yan-Yan Tan, Yong-Chang Jiao, Hong Li & Xin-Kuan Wang, MOEA/D-SQA: a multi-objective memetic algorithm based on decomposition, Engineering Optimization, 2012.
  33. Gong, Maoguo, et al, Community detection in networks by using multiobjective evolutionary algorithm with decomposition,  Physica A: Statistical Mechanics and its Applications, 2012.
  34. Noura Al Moubayed, Andrei Petrovski and John McCall, D2MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance, EVOLUTIONARY COMPUTATION IN COMBINATORIAL OPTIMIZATION, Lecture Notes in Computer Science, 2012, Volume 7245/2012.
  35. H Lu and X. Liu,  Compass Augmented Regional Constellation Optimization by a Multi-objective Algorithm Based on Decomposition and PSO, Chinese Journal of Electronics, 2012.
  36. X. Li and M. Yin, Design of multiobjective reconfigurable antenna array with discrete phase shifters using multiobjective differential evolution based on decomposition, International Journal of RF and Microwave Computer-Aided Engineering , 27/03/2012.
  37.  V. A. Shim, K. C. Tan, and C. Y. Cheong, A Hybrid Estimation of Distribution Algorithm with Decomposition for Solving the Multiobjective Multiple Traveling Salesman Problem, IEEE Trans SMC-C, 2012.
  38. Andreas Konstantinidis and  Kun Yang, Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D, Applied Soft Computing, 2012.
  39. Yan-yan Tan,  et al, MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives, Computer and Operations Research, 2012.
  40. D Ding, H Wang,  Evolutionary Computation of Multi-Band Antenna Using Multi-Objective Evolutionary Algorithm Based on Decomposition,  Information Computing and Applications, 2011 
  41. Konstantinidis, A.; Zeinalipour-Yazti, D.; Andreou, P.; Samaras, G.; , "Multi-objective Query Optimization in Smartphone Social Networks," Mobile Data Management (MDM), 2011 12th IEEE International Conference on , vol.1, no., pp.27-32, 6-9 June 2011.
  42. Siwei Jiang, Jie Zhang and Yew Soon Ong, Asymmetric Pareto-adaptive Scheme for Multiobjective Optimization, AI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE, Lecture Notes in Computer Science, 2011. (λ-MOEA/D)
  43. ZHAO Zhi-Chao, ZHANG Shen, ZHANG Hui Optimization of PDN Impedance for the Multiobjective Evolutionary Algorithm Based on Decomposition. Electronic Science and Technology 2012, 25(1)  
  44. CHEN Guoqiang and WANG Yuping, Community Detection of Complex Networks Based on Multiobjective Evolutionary Algorithms, 2012. Info Science and System Science.
  45. Chen Qin et al, Multi-objective optimization of supersonic-supersonic ejector, High Power Laser and Particle Beams, 2012,V24(05): 1043-1046 2012.
  46. D. Zhang et al, MOEA/D-GEP, Journal of Uni of Sci and Tech. of Central China, No.4. 2012.  

 

 

 

 

 

 

 



 

 

 

 

 

 

This list is not complete. If you know any other papers that should go into this page, please let me know.

 

Maintained by Qingfu Zhang.    Last updated 04/04/2011