References

Papers about MOEA/D and decomposition-based EMO

Strengths, weaknesses, variants, generalizations, and applications of MOEA/D and related approaches. More references may be added by request.

2021

• Zhenkun Wang, Qingfu Zhang, Yew-Soon Ong, Shunyu Yao, Haitao Liu and Jianping Luo, Choose Appropriate Subproblems for Collaborative Modeling in Expensive Multiobjective Optimization, IEEE Transaction on Cybernetics, in press, 2021. Code

• Zhenkun Wang, Hui-Ling Zhen, Jingda Deng, Qingfu Zhang, Xijun Li, Mingxuan Yuan and Jia Zeng, Multiobjective Optimization-Aided Decision-Making System for Large-Scale Manufacturing Planning , IEEE Transaction on Cybernetics, in press, 2021.

• Zhenkun Wang, Qingyan Li, Qite Yang, Hisao Ishibuchi, The dilemma between eliminating dominance-resistant solutions and preserving boundary solutions of extremely convex Pareto fronts, Complex & Intelligent Systems, in press, 2021.

• Zhenkun Wang, Jingda Deng, Qingfu Zhang, Qite Yang, On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D, The 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021, Shenzhen, China, March 28–31, 2021.

2020

• Lei Chen, Deb Kalyanmoy, Hailin Liu and Qingfu Zhang, Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition Based Evolutionary Many-objective Optimization Algorithms, Evolutionary Compuatation, in press.

• Lu Zhichao and Deb Kalyanmoy and Boddeti Vishnu Naresh, MUXConv: Information Multiplexing in Convolutional Neural Networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020).

• Miqing Li and Xin Yao, What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation, Evolutionary Compuatation, 28(2): 227-253, 2020.Code

• Hanjing Cheng, Zidong Wang, Zhihui Wei , Lifeng Ma and Xiaohui Liu, On Adaptive Learning Framework for Deep Weighted Sparse Autoencoder: A Multiobjective Evolutionary Algorithm, IEEE Transaction on Cybernetics, in press, 2020.

• Kaiwen Li, Tao Zhang, and Rui Wang, Deep Reinforcement Learning for Multi-objective Optimization, IEEE Transaction on Cybernetics, in press, 2020.

• X. Cai, W. Sun, M. Misir, K. C. Tan, X. Li, T. Xu and Z. Fan, A Bi-objective Constrained Robust Gate Assignment Problem: Formulation, Instances and Algorithm, IEEE Transaction on Cybernetics, in press, 2020. Code

• S. Yao, Z. Dong and X. Wang, A multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy, Information Sciences, 2020, 511: 18-35. Code

• Z. Fan, Z. Wang, W. Li, Y. Yuan, Y. You, Z. Yang, F. Sun and J. Ruan, Push and pull search embedded in an M2M framework for solving constrained multi-objective optimization problems, Swarm and Evolutionary Computation, in press, 2020. Code

2019

• Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang and Sam Kwong, Pareto Multi-Task Learning, Conference on Neural Information Processing Systems (NIPS 2019). Code

• X. Cai, C. Xia, Q. Zhang, Z. Mei, H. Hu and L. Wang, The Collaborative Local Search based on Dynamic Constrained Decomposition with Grids for Combinatorial Multiobjective Optimization, IEEE Transaction on Cybernetics, in press, 2019. Code

• Y. Xiang, X. Yang, Y. Zhou and H. Huang, Enhancing Decomposition-based Algorithms by Estimation of Distribution for Constrained Optimal Software Product Selection, IEEE Transactions on Evolutionary Computation, in press, 2019. Code

• Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multiobjective test problem toolkit, Evolutionary computation, in press, 2019. Code

• X. Cai, H. Sun, Q. Zhang, Z. Fan. A Grid Weighted Sum Pareto Local Search for Combinatorial Multi and Many-Objective Optimization. IEEE Transaction on Cybernetics, vol. 49, no. 9, pp. 3586-3598, 2019. Code

• Z. Wang, Y-S. Ong, J. Sun, A. Gupta and Q. Zhang, A Generator for Multiobjective Test Problems with Difficult-to-Approximate Pareto Front Boundaries, IEEE Transactions on Evolutionary Computation, 23(4): 556-571, 2019. Code

• Z. Wang, Y-S. Ong and H. Ishibichi, On Scalable Multiobjective Test Problems with Hardly-dominated Boundaries, IEEE Transactions on Evolutionary Computation, 23(2): 217-231, 2019. Code

• Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Push and pull search for solving constrained multi-objective optimization problems, Swarm and evolutionary computation, 44: 665-679, 2019. Code

• Z. Fan, Y. Fang, W. Li, X. Cai, C. Wei, and E. Goodman，MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems, Applied Soft Computing, 74: 621-633, 2019. Code

• Z. Fan, W. Li, X. Cai, H. Huang, Y. Fang, Y. You, J. Mo, C. Wei, and E. D. Goodman, An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions, Soft Computing, 23(23): 12491-12510, 2019. Code

2018

• X. Cai, Z. Mei, Z. Fan, Q. Zhang, A Constrained Decomposition Approach With Grids for Evolutionary Multiobjective Optimization. IEEE Transaction on Evolutionary Computation, 22(4), 564-577, 2018. Code

• X. Cai, Z. Mei, Z. Fan, A Decomposition-Based Many-Objective Evolutionary Algorithm With Two Types of Adjustments for Direction Vectors. IEEE Transaction on Cybernetics, 48(8), 2335-2348, 2018. Code

• Y. Jiang, Y. Liu, J. Shang, P. Yildirim, and Q. Zhang, Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives, European Journal of Operational Research, 267(2): 612-627, 2018.

• R. Wang, H. Ishibuchi, Z. Zhou T. Liao and T. Zhang, Localized weighted sum method for many-objective optimization, IEEE Transactions on Evolutionary Computation, IEEE, 18 (3), 22, 2018. Code

• X. Ma, Q. Zhang, G. Tian, J. Yang, and Z. Zhu, On Tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 22(2): 226-244, 2018.

2017

• X. Cai, Z. Yang, Z. Fan, Q. Zhang. A Decomposition-based Sorting and Angel-based Selection for Multi- and Many-objective Evolutionary Optimization. IEEE Transaction on Cybernetics, 47 (9), 2824-2837, 2017. Code

• Z. Wang, Q. Zhang, H. Li, H. Ishibuchi and L. Jiao, On The Use of Two Reference Points in Decomposition Based Multiobjective Evolutionary Algorithms, Swarm and Evolutionary Computation, 34: 89-102, 2017. Code

• S. Jiang, and S. Yan, A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, vol. 21, no. 3, pp. 329-346, 2017

• Y. Xiang, Y. Zhou, M. Li, and Z. Chen, A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, vol. 21, no. 1, pp. 131-152, 2017

• H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes, IEEE Transactions on Evolutionary Computation, vol. 21, no. 2, pp. 169-190, 2017

• H. L. Liu, L. Chen, Q. Zhang, and K. Deb, Adaptively Allocating Search Effort in Challenging Many-Objective Optimization Problems, IEEE Transactions on Evolutionary Computation, vol. PP, no. 99, pp. 1-1, 2017

This paper presents a new adaptive search effort allocation strategy for MOEA/D-M2M, which adaptively adjusts the subregions of its subproblems by detecting the importance of different objectives in an adaptive manner

• A. Trivedi, D. Srinivasan, K. Sanyal, and A. Ghosh, A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition, IEEE Transactions on Evolutionary Computation, vol. 21, no. 3, pp. 440-462, 2017

This paper presents a comprehensive survey of the decomposition-based MOEAs proposed in the last decade

• M. Wu, K. Li, S. Kwong, Y. Zhou, and Q. Zhang, Matching-based selection with incomplete lists for decomposition multiobjective optimization, IEEE Transactions on Evolutionary Computation, vol. 21, no. 4, pp. 554–568, 2017

In this paper, the concept of incomplete preference lists into the stable matching model to remedy the loss of population diversity in the selection of MOEA/D

• X. Ma, Q. Zhang, J. Yang, and Z. Zhu. On tchebycheff decomposition approaches for multi-objective evolutionary optimization, IEEE Transactions on Evolutionary Computation, vol. PP, no. 99, pp. 1-1, 2017

This paper proposes a Tchebycheff decomposition with lp-norm constraint on direction vectors in which the subproblem objective functions are endowed with clear geometric property

• F. Gu, and Y. M. Cheung, Self-organizing map-based weight design for decomposition-based many-objective evolutionary algorithm, IEEE Transactions on Evolutionary Computation, vol. PP, no. 99, pp. 1-1, 2017

​A novel weight design method based on self-organizing map is integrated with most of the decomposition-based algorithms for solving many-objective optimization problems

• R. Liu, R. Wang, X. Yu, and L. An, Shape automatic clustering-based multi-objective optimization with decomposition, Machine Vision and Applications, 2017

A new shape automatic clustering method based on multi-objective optimization with decomposition (MOEA/D-SAC) is proposed, which aims to find the final cluster number k as well as an optimal clustering result for the shape datasets

• M. Ming, R. Wang, Y. Zha, and T. Zhang, Pareto adaptive penalty-based boundary intersection method for multi-objective optimization, Information Sciences, 414:158 – 174, 2017

A simple yet effective method called Pareto adaptive PBI (PaP) is proposed by which a suitable penalty value can be adaptively identified, which therefore can maintain fast convergence speed, meanwhile, leading to a good approximation of the PF

• C. Zhang, K.C. Tan, L.H. Lee, and L. Gao, Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts, Soft Computing, 2017

A method for adjusting weight vectors for bi-objective optimization problems with discontinuous PF is proposed

• H. Xing, Z. Wang, T. Li, H. Li, and R. Qu, An improved MOEA/D algorithm for multi-objective multicast routing with network coding, Applied Soft Computing, vol. 59, pp. 88-103, 2017

MOEA/D hybridizing with a population-based incremental learning technique is proposed for multicast routing optimization

• K. Michalak, ED-LS: a heuristic local search for the multiobjective firefighter problem, Applied Soft Computing, , vol. 59, pp. 389-404, 2017

• R. Wang, Z. Zhou, H. Ishibuchi, T. Liao, and T. Zhang, Localized weighted sum method for many-objective optimization, IEEE Transactions on Evolutionary Computation, vol. 99, no. XX, pp. XX-XX, 2017

A novel decomposition based EMO algorithm called MOEA/D-LWS is proposed in which the weighted sum method is applied in a local manner. That is, for each search direction, the optimal solution is selected only amongst its neighboring solutions.

• H. Li, Q. Zhang, J. Deng, Z-B. Xu,​ A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization, IEEE Trancations on Neural Network and Learning Systems, vol. 99, pp. PP, 2017

​A variant of MOEA/D is proposed to solve a bi-objective optimization problem, of which the knee solution with sparsity structure is located in the region of interest

• H. Li, Q. Zhang, and J. Deng, Biased multiobjective optimization and decomposition algorithm, IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 52-66, 2017

​This paper proposes a MOEA/D variant with CMA-ES for dealing with the MOPs with biased Pareto front, which requires the search operators with high precision

• S. Sarkar, S. Das, S. S. Chaudhuri, Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images, Applied Soft Computing, vol. 50, pp. 142-157, 2017

• Y. Qi, Q. Zhang, X. Ma, Y. Quan, Q. Miao: Utopian point based decomposition for multi-objective optimization problems with complicated Pareto fronts, Applied Soft Computing, 61: 844-859, 2017.

• Xi Lin, Q. Zhang, and S Kwong. An efficient batch expensive multiobjective evolutionary algorithm based on decomposition. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017), pp. 1343–1349, 2017

This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based on Decomposition (MOBO/D)

• Z. Fan, Y. Fang, W. Li, J. Lu, X. Cai, and C. Wei. A comparative study of constrained multi objective evolutionary algorithms on constrained multi-objective optimization problems. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017), pp. 209–216, 2017

This paper studies the performances of several MOEA/D variants on constrained multiobjective optimization problems.

• X. Chen, C. Shi, A. Zhou, B. Wu, and Z. Cai. A decomposition based multiobjective evolutionary algorithm with semisupervised classification. In 2017 IEEE Congress on EvolutionaryComputation (CEC 2017), pp. 797–804, 2017

Based on the typical decomposition based multiobjective evolutionary algorithm MOEA/D, the algorithm framework through integrating the novel offspring selection process based on semi-supervised classification is designed

• O. R. Castro, R. Santana, J. A. Lozano, and A. Pozo. Combining CMA-ES and MOEA/D for many-objective optimization. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017), pp. 1451–1458, 2017

The algorithm proposed in this paper modifies MOEA/D by including a new Pareto dominance update mechanism that brings more diversity into the search

• H. Zhang, A. Zhou, G. Zhang, and H. K. Singh. Accelerating MOEA/D by Nelder-Mead method. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017), pp. 976–983, 2017

One of the derivative-free optimization methods, Nelder-Mead simplex (NMS) method, is used in MOEA/D for accelerating the algorithm convergence

• I. R. Meneghini and F. G. Guimares. Evolutionary method for weight vector generation in multi-objective evolutionary algorithms based on decomposition and aggregation. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017), pp. 1900–1907, 2017

The proposed evolutionary algorithm is able to prevent the creation of weight vectors along the border of the orthant, which is a region that contains solutions of little interest to the decision maker

• K. Izumiya and M. Munetomo. Multi-objective evolutionary optimization based on decomposition with linkage identification considering monotonicity. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017), pages 905–912, 2017

This paper studies a version of MOEA/D that incorporates a linkage identification technique to enhance the ability to solve difficult multi-objective optimization problems that have complex interactions among genes

• K. Michalak. The MOEA/D algorithm with gaussian neighbourhoods for the multiobjective travelling salesman problem. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2017), pp 155–156, 2017

In this paper the MOEA/D-G algorithm, a modification of the MOEA/D algorithm using Gaussian distributions, is proposed

• M. Wu, S. Kwong, Y. Jia, K. Li, and Q. Zhang. Adaptive weights generation for decomposition-based multi-objective optimization using gaussian process regression. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017), pp. 641–648 2017

This paper proposes an adaptive method to periodically regenerate the weight vectors for decomposition-based multi-objective algorithms according to the geometry of the estimated Pareto front

• H. Sato, M. Miyakawa, and K. Takadama. An improved MOEA/D utilizing variation angles for multi-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2017), pp. 163–164 2017

This work proposes a decomposition-based multi-objective evolutionary algorithm utilizing variation angles among objective and weight vectors

• M. Elarbi, S. Bechikh, and L. Ben Said. On the importance of isolated solutions in constrained decomposition-based many-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, (GECCO 2017), pp. 561–568, 2017

The IS-update procedure (Isolated Solution) is subsequently embedded within the Multi-Objective Evolutionary Algorithm-based on Decomposition (MOEA/D)

• J. C. Castillo, C. Segura, A. H. Aguirre, G. Miranda, and C. Leon. A multi-objective decomposition-based evolutionary algorithm with enhanced variable space diversity control. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2017), pp. 1565–1571, 2017

MOEA/D with Enhanced Variable-Space Diversity (MOEA/D-EVSD) is proposed. This variant induces a gradual loss of diversity by altering the mating selection process

• M. Kaidan, T. Harada, and R. Thawonmas. Integrating surrogate evaluation model and asynchronous evolution in multiobjective evolutionary algorithm for expensive and different evaluation time. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2017), pp. 1833–1840, 2017

This paper proposes Extreme Learning Surrogate assisted Asynchronous MOEA/D (AELMOEA/D) that solves multi-objective optimization problems with expensive and different evaluation time by integrating a surrogate evaluation model and an asynchronous evolution method.

• H. B. Nguyen, B. Xue, H. Ishibuchi, P. Andreae, and M. Zhang. Multiple reference points MOEA/D for feature selection. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2017), pages 157–158, 2017

This paper proposes a new decomposition strategy for feature selection called MOEA/D-MRPs which uses multiple reference points instead of multiple weight vectors

• M. Miyakawa, H. Sato, and Y. Sato. Utilization of infeasible solutions in MOEA/D for solving constrained many-objective optimization problems. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2017), pp. 35–36, 2017

For solving constrained many-objective optimization problems, this work proposes CMOEA/D-DM introducing the Directed Mating utilizing useful infeasible solutions having better scalarizing function values than feasible ones in the MOEA/D algorithm framework.

• X. Guo. A new decomposition many-objective evolutionary algorithm based on efficiency order dominance. In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2017

This paper proposes a new dominance relation based on - efficiency order dominance (MOEA/D-eEOD) eEOD in each sub-problem to realize the selection and update of the individuals

• B. Khan, S. Hanoun, M. Johnstone, C. P. Lim, D. Creighton, and S. Nahavandi. A new decomposition-based evolutionary framework for many-objective optimization. In 2017 Annual IEEE International Systems Conference (SysCon), pp. 1–7, 2017

In the class of decomposition-based MOEAs using reference points, a novel framework with a restricted mating selection scheme is proposed to further improve the quality of the solutions close to the target reference vectors

• R. Denysiuk, A. Gaspar-Cunha, Weighted stress function method for multiobjective evolutionary algorithm based on decomposition, Evolutionary Multi-Criterion Optimization (EMO 2017), pp. 176-190, 2017

This study suggests a weighted stress function method (WSFM) for fitness assignment in MOEA/D. WSFM establishes analogy between the stress-strain behavior of thermoplastic vulcanizates and scalarization of a multiobjective optimization problem.

• M. Pescador-Rojas, R. H. Gómez, E. Montero, N. Rojas-Morales, M-C. Riff, C. A. Coello Coello, An overview of weighted and unconstrained scalarizing functions, Evolutionary Multi-Criterion Optimization (EMO 2017), pp. 499-513, 2017

This paper presents a general review of weighted scalarizing functions without constraints

• X. Hao, J. Liu and Z. Wang, An Improved Global Replacement Strategy for MOEA/D on Many-objective Kanpsack Problems, 2017 IEEE Congress on Automation Science and Engineering, p. 624-629, (CASE) 2017. Code

2016

• M. Li, S. Yang, and X. Liu. Pareto or non-Pareto: Bi-criterion evolution in multi-objective optimization. IEEE Transactions on Evolutionary Computation, 20(5): 645-665, 2016. Code

• H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes, IEEE Transactions on Evolutionary Computation, vol. PP, no. 99, pp. 1–1, 2016

• R. Wang, Q. Zhang, and T. Zhang, Decomposition-based algorithms using Pareto adaptive scalarizing methods, IEEE Transactions on Evolutionary Computation, vol. 20, no. 6, pp. 821-837, 2016. Code

• R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, vol. 20, no. 5, pp. 773-791, 2016

• L. Wang, Q. Zhang, A. Zhou, M. Gong, and L. Jiao, Constrained subproblems in a decomposition-based multiobjective evolutionary algorithm, IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 475–480, 2016. Code

• Y. Yuan, H. Xu, B. Wang, B. Zhang, and X. Yao, Balancing convergence and diversity in decomposition-based many-objective optimizers, IEEE Transactions on Evolutionary Computation, vol. 20, no. 2, pp. 180–198, 2016.

• X. Ma, F. Liu, Y. Qi. etal, A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables, IEEE Transactions on Evolutionary Computation, 20(2): 275-298, 2016.

• A. Zhou and Q. Zhang, Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol. 20, no. 1, pp. 52–64, 2016. Code

• X. Cai, Z. Yang, Z. Fan, and Q. Zhang, Decomposition-based-sorting and angle-based-selection for evolutionary multiobjective and manyobjective optimization, IEEE Transactions on Cybernetics, vol. PP, no. 99, pp. 1–14, 2016

• Z. Wang, Q. Zhang, A. Zhou, M. Gong, and L. Jiao, Adaptive replacement strategies for MOEA/D, IEEE Transactions on Cybernetics, vol. 46, no. 2, pp. 474–486, 2016. Code

• S. Jiang and S. Yang, An improved multiobjective optimization evolutionary algorithm based on decomposition for complex Pareto fronts, IEEE Transactions on Cybernetics, vol. 46, no. 2, pp. 421– 437, 2016

• R. Saborido, A. B. Ruiz, and M. Luque, Global WASF-GA: An evolutionary algorithm in multiobjective optimization to approximate the whole Pareto optimal front, Evolutionary Computation, 2016

• S. Yang, S. Jiang, and Y. Jiang, Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes, Soft Computing, pp. 1–15, 2016

• H. Zhang, X. Zhang, X.-Z. Gao, and S. Song, Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble, Neurocomputing, vol. 173, pp. 1868-1884, 2016.

• Y. Qi, L. Bao, X. Ma, Q. Miao, X. Li. Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: A case study on reservoir flood control operation, Information Sciences, 367-368(1): 529-549, 2016.

• H.-L. Liu, L. Chen, Q. Zhang, and K. Deb, An evolutionary manyobjective optimisation algorithm with adaptive region decomposition, in Congress on Evolutionary Computation (CEC 2016), pp. 4763-4769, 2016

• X. Lin, Q. Zhang, and S. Kwong, A decomposition based multiobjective evolutionary algorithm with classification, in Congress on Evolutionary Computation (CEC 2016), pp. 3292-3299, 2016

• H. Sato, S. Nakagawa, M. Miyakawa, and K. Takadama, Enhanced decomposition-based many-objective optimization using supplemental weight vectors, in Congress on Evolutionary Computation (CEC 2016), pp. 1626-1633, 2016

• S. Jiang, L. Feng, D. Yang, C. K. Heng, Y.-S. Ong, A. N. Zhang, P. S. Tan, and Z. Cai, Towards adaptive weight vectors for multiobjective evolutionary algorithm based on decomposition, in Congress on Evolutionary Computation (CEC 2016), pp. 500-507, 2016

• B. Derbel, A. Liefooghe, Q. Zhang, H. Aguirre, K. Tanaka, Multi-objective local search based on decomposition, in Parallel Problem Solving from Nature (PPSN 2016), pp. 431-441, 2016.

• X. Ma, Z. Zhu, Z. Ji, J. Yang and N. Wu, A comparative study on decomposition-based multi-objective evolutionary algorithms for many-objective optimization, 2016 IEEE Congress on Evolutionary Computation (CEC2016), 2016: 1-6.

• H. Tam, M-F. Leung, Z. Wang, S-C Ng, C-C Cheung, A-K Lui. Improved Adaptive Global Replacement Scheme for MOEA/D-AGR, 2016 IEEE Congress on Evolutionary Computation, p. 2153-2160, (CEC) 2016.

2015

• Y. L. Li, Y. Zhou, Z. H. Zhan, and J. Zhang, A primary theoretical study on decomposition based multiobjective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol. 20, no. 4, pp. 563-576, 2015

• K. Li, K. Deb, Q. Zhang, and S. Kwong, An evolutionary manyobjective optimization algorithm based on dominance and decomposition, IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. 694–716, 2015

• S. B. Gee, K. C. Tan, V. A. Shim, and N. Pal, Online diversity assessment in evolutionary multiobjective optimization: A geometrical perspective, IEEE Transactions on Evolutionary Computation, vol. 19, no. 4, pp. 542–559, 2015

• X. Cai, Y. Li, Z. Fan, and Q. Zhang, An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization, IEEE Transactions on Evolutionary Computation, vol. 19, no. 4, pp. 508–523, 2015.Code

• M. Asafuddoula, T. Ray, and R. Sarker, A decomposition-based evolutionary algorithm for many objective optimization, IEEE Transactions on Evolutionary Computation, vol. 19, no. 3, pp. 445–460, 2015

• K. Li, S. Kwong, Q. Zhang, and K. Deb, Interrelationship-based selection for decomposition multiobjective optimization, IEEE Transactions on Cybernetics, vol. 45, no. 10, pp. 2076–2088, 2015

• H. Sato, Analysis of inverted PBI and comparison with other scalarizing functions in decomposition based MOEAs, Journal of Heuristics, vol. 21, no. 6, pp. 819-849, 2015

• A. B. Ruiz, R. Saborido, and M. Luque, A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm, Journal of Global Optimization, vol. 62, no. 1, pp. 101–129, 2015

• S. Zapotecas-Martinez, B. Derbel, A. Liefooghe, H. Aguirre, and K. Tanaka, Geometric differential evolution in MOEA/D: a preliminary study, in Conference on Artificial Intelligence (MICAI 2015), pp. 364-376, 2015

• Z. Wang, Q. Zhang, and H. Li, Balancing convergence and diversity by using two different reproduction operators in MOEA/D: Some preliminary work, in Systems, Man, and Cybernetics (SMC 2015), pp. 2849-2854, 2015

• A. Mohammadi, M. Omidvar, X. Li, and K. Deb, Sensitivity analysis of penalty-based boundary intersection on aggregation-based EMO algorithms, in Congress on Evolutionary Computation (CEC 2015), pp. 2891–2898, 2015

• B. Derbel, A. Liefooghe, G. Marquet, E. Talbi, A fine-grained message passing MOEA/D, in Congress on Evolutionary Computation (CEC 2015), pp. 1837-1844, 2015

• H. Li, M. Ding, J. Deng, and Q. Zhang, On the use of random weights in MOEA/D, in Congress on Evolutionary Computation (CEC 2015), pp. 978–985, 2015

• S. Zapotecas-Martinez, B. Derbel, A. Liefooghe, D. Brockhoff, H. E. Aguirre, and K. Tanaka, Injecting CMA-ES into MOEA/D, in Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 783–790, 2015

• M. Pilat and R. Neruda, Incorporating user preferences in MOEA/D through the coevolution of weights, in Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 727–734, 2015

• R. Gonalves, C. Almeida, and A. Pozo, Upper confidence bound (UCB) algorithms for adaptive operator selection in MOEA/D, in Evolutionary Multi-Criterion Optimization (EMO 2015), pp. 411–425, 2015

• L. Bezerra, M. López-Ibáñez, and T. Stützle, Comparing decomposition-based and automatically component-wise designed multi-objective evolutionary algorithms, in Evolutionary Multi-criterion Optimization (EMO 2015), pp. 396–410, 2015

• R. Wang, Q. Zhang, and T. Zhang, Pareto adaptive scalarising functions for decomposition based algorithms, in Evolutionary Multi-Criterion Optimization (EMO 2015), pp. 248-262, 2015

• R. Goncalves, J. Kuk, C. Almeida, and S. Venske, MOEA/D-HH: A hyper-heuristic for multi-objective problems, in Evolutionary Multi-Criterion Optimization (EMO 2015), pp. 94-108, 2015

2014

• K. Li, Q. Zhang, S. Kwong, M. Li, and R. Wang, Stable matching based selection in evolutionary multiobjective optimization, IEEE Transactions on Evolutionary Computation, vol. 18, no. 6, pp. 909–923, 2014

• H. Jain and K. Deb, An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach, IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 602–622, 2014

• K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints, IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, 2014

• H. Liu, F. Gu, and Q. Zhang, Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems, IEEE Transactions on Evolutionary Computation, vol. 18, no. 3, pp. 450–455, 2014. Code

• K. Li, A. Fialho, S. Kwong, and Q. Zhang, Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, vol. 18, no. 1, pp. 114–130, 2014

• Y. Qi, X. Ma, F. Liu, L. Jiao, J. Sun, and J. Wu, MOEA/D with adaptive weight adjustment, Evolutionary Computation, vol. 22, no. 2, pp. 231–264, 2014

• I. Giagkiozis, R. Purshouse, and P. Fleming, Generalized decomposition and cross entropy methods for many-objective optimization, Information Sciences, vol. 282, pp. 363-87, 2014

• X. Ma, Y. Qi, L. Li, F. Liu, L. Jiao, and J. Wu, MOEA/D with uniform decomposition measurement for many-objective problems, Soft Computing, vol. 18, no. 12, pp. 2541-64, 2014

• X. Ma, F. Liu, Y. Qi, M. Gong, M. Yin, L. Li, L. Jiao, J. Wu, MOEA/D with opposition-based learning for multiobjective optimization problem, Neurocomputing, (146):48-64, 2014.

• X. Ma, Y. Qi, L. Li, etal, MOEA/D with uniform decomposition measurements for many-objective problems, Soft Computing, 18(12): 2541–2564, 2014.

• S. Venske, R. Goncalves, and M. Delgado, ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm, Neurocomputing, vol. 127, pp. 65-77, 2014

• Z. Wang, Q. Zhang, M. Gong, and A. Zhou, A replacement strategy for balancing convergence and diversity in MOEA/D, in Congress on Evolutionary Computation (CEC 2014), pp. 2132– 2139, 2014. Code

• Y. Qi, X. Ma, M. Yin, etal, MOEA/D with a delaunay triangulation based weight Adjustment, In Genetic and Evolutionary Computation Conference (GECCO 2014), 93-94, 2014.

• H. Sato, Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization, in Genetic and Evolutionary Computation Conference (GECCO 2014), pp. 645–652, 2014

• G. Marquet, B. Derbel, A. Liefooghe, E. Talbi, Shake them all! Rethinking selection and replacement in MOEA/D, in Parallel Problem Solving from Nature (PPSN 2014), pp. 641-651, 2014

• A. Zhou, Q. Zhang, and G. Zhang, A multiobjective evolutionary algorithm based on mixture Gaussian models, Journal of Software, 5:913-928, 2014. Code

• B. Derbel, D. Brockhoff, A. Liefooghe, S. Verel, On the impact of multiobjective scalarizing functions, in Parallel Problem Solving from Nature (PPSN 2014), pp. 548-558, 2014

2013

• K. Sindhya, K. Miettinen, and K. Deb, A hybrid framework for evolutionary multi-objective optimization, IEEE Transactions on Evolutionary Computation, vol. 17, no. 4, pp. 495-511, 2013

• L. Ke, Q. Zhang, and R. Battiti, MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and ant colony, IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1845–1859, 2013

MOEA/D + ant colony optimization

• Y. Tan, Y. Jiao, H. Li, and X. Wang, MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives, Computers & Operations Research, vol. 40, no. 6, pp. 1648-1660, 2013

• S. Zapotecas Martinez and C. A. Coello Coello, MOEA/D assisted by RBF networks for expensive multi-objective optimization problems, in Genetic and Evolutionary Computation Conference (GECCO 2013), pp. 1405–1412, 2013

• H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, Adaptation of scalarizing functions in MOEA/D: an adaptive scalarizing function based multiobjective evolutionary algorithm, in Evolutionary Multi-Criterion Optimization (EMO 2013), pp. 438-452, 2013

• I. Giagkiozis, R. Purshouse, and P. Fleming, Generalized decomposition, in Evolutionary Multi-Criterion Optimization (EMO 2013), pp. 428-442, 2013

• B. Derbel, D. Brockhoff, A. Liefooghe, Force-based cooperative search directions in evolutionary multi-objective optimization, in Evolutionary Multi-Criterion Optimization (EMO 2013), pp 383-397, 2013

• I. Giagkiozis, R. C. Purshouse, and P. J. Fleming, Towards understanding the cost of adaptation in decomposition-based optimization algorithms, in Systems, Man, and Cybernetics (SMC 2013), pp. 615-620, 2013

2012

• S.-Z. Zhao, P. Suganthan, and Q. Zhang, Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, IEEE Transactions on Evolutionary Computation, vol. 16, no. 3, pp. 442–446, 2012

• V. Shim, K. Tan, and C. Cheong, A hybrid estimation of distribution algorithm with decomposition for solving the multiobjective multiple traveling salesman problem, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 5, pp. 682–691, 2012

• R. Carvalho, R.Saldanha, B. Gomes, A. Lisboa, and A. Martins, A multi-objective evolutionary algorithm based on decomposition for optimal design of Yagi-Uda antennas, IEEE Transactions on Magnetics, vol. 48, no. 2, pp. 803-806, 2012

• C. Almeida, R. Gonçalves, E. Goldbarg, M. Goldbarg, and M. Delgado, An experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem, Annals of Operations Research, vol. 199, no. 1, pp 305–341, 2012

MOTA/D = MOEA/D + TA

• J. Cheng, G. Zhang, Z. Li, and Y. Li, Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems, Soft Computing, vol. 16, no. 4, pp. 597–614, 2012

MOEA/D + ant colony optimization

• A. Konstantinidis, and K. Yang, Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D, Applied Soft Computing, vol. 12, no. 7, pp. 1847-1864, 2012

• Y-Y. Tan , Y-C. Jiao , H. Li, and X-K. Wang, MOEA/D-SQA: a multi-objective memetic algorithm based on decomposition, Engineering Optimization, vol. 44, no. 9, pp. 1095-1115, 2012

• M. Gong, L. Ma, Q. Zhang, and L. Jiao, Community detection in networks by using multiobjective evolutionary algorithm with decomposition, Physica A: Statistical Mechanics and its Applications, vol. 391, no. 15, pp. 4050–4060, 2012

• N. Al Moubayed, B. Awwad Shiekh Hasan, J. Q. Gan, A. Petrovski, and J. McCall, Continuous presentation for multi-objective channel selection in brain-computer interfaces, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• M. Asafuddoula, T. Ray, R. Sarker, and K. Alam, An adaptive constraint handling approach embedded MOEA/D, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• K. Deb, and H. Jain, Handling many-objective problems using an improved NSGA-II procedure, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• A. Kafafy, A. Bounekkar, and S. Bonnevay, Hybrid metaheuristics based on MOEA/D for 0/1 multiobjective knapsack problems: A comparative study, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• A. Mohammadi, M. Omidvar, and X. Li, Reference point based multi-objective optimization through decomposition, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• M. Nasir, S. Sengupta, S. Das, and P. Suganthan, An improved multi-objective optimization algorithm based on fuzzy dominance for risk minimization in biometric sensor network, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• S. Roy, S. Zapotecas Martínez, C. A. Coello Coello, and S. Sengupta, A multi-objective evolutionary approach for linear antenna array design and synthesis, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• S. Sengupta, S. Das, M. Nasir, A.. Vasilakos, and W. Pedrycz, Energy-efficient differentiated coverage of dynamic objects using an improved evolutionary multi-objective optimization algorithm with fuzzy-dominance, in Congress on Evolutionary Computation (CEC 2012), pp. 500-507, 2016

• V. Shim, K. Tan, and K. Tan, A hybrid adaptive evolutionary algorithm in the domination-based and decomposition-based frameworks of multi-objective optimization, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• J. Stringer, G. Lamont, and G. Akers, Radar phase-coded waveform design using MOEAs, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• W. Xin, and S. Fujimura, Parallel quantum evolutionary algorithms with Client-Server model for multi-objective optimization on discrete problems, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• S. Zapotecas Martínez, and C. A. Coello Coello, A direct local search mechanism for decomposition-based multi-objective evolutionary algorithms, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• A. Zhou, Q. Zhang, and Guixu Zhang, A multiobjective evolutionary algorithm based on decomposition and probability model, in Congress on Evolutionary Computation (CEC 2012), pp. 1–8, 2012

• H. Li, X. Su, Z. Xu, and Q. Zhang, MOEA/D with iterative thresholding algorithm for sparse optimization problems, in Parallel Problem Solving from Nature (PPSN 2012), pp. 93-101, 2012

• W. Peng, and Q. Zhang, Network topology planning using MOEA/D with objective-guided operators, in Parallel Problem Solving from Nature (PPSN 2012), pp. 62-71, 2012

• H. Ishibuchi, N. Akedo, and Y. Nojima, EMO algorithms on correlated many-objective problems with different correlation strength, World Automation Congress (WAC 2012), pp. 1-8, 2012

2011

• T. McConaghy, P. Palmers, M. Steyaert, and G. Gielen, Trustworthy genetic programming-based synthesis of analog circuit topologies using hierarchical domain-specific building blocks, IEEE Transactions on Evolutionary Computation, vol. 15, no. 4, pp. 557-570, 2011

MOEA/D with multiple solutions for each sub-problem

• H. Li and D. Landa-Silva, An adaptive evolutionary multi-objective approach based on simulated annealing. Evolutionary Computation, vol. 19, no. 4, pp. 561-595, 2010

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

• H. Ishibuchi, Y. Nakashima, and Y. Nojima, Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning, Soft Computing, vol. 15, no. 12, pp 2415–2434, 2011

• K. Sindhya, S. Ruuska, T. Haanpää, and K. Miettinen, A new hybrid mutation operator for multiobjective optimization with differential evolution, Soft Computing, vol. 15, no. 10, pp 2041–2055, 2011

MOEA/D + nonlinear crosover/mutation

• D. K. Saxena, Q. Zhang, J. A. Duro, and A. Tiwari, Framework for many-objective test problems with both simple and complicated Pareto-set shapes, in Evolutionary Multi-Criterion Optimization (EMO 2011), pp. 197-211, 2011

• H. Ishibuchi, Y. Hitotsuyanagi, H. Ohyanagi, and Y. Nojima, Effects of the existence of highly correlated objectives on the behavior of MOEA/D, in Evolutionary Multi-Criterion Optimization (EMO 2011), pp. 166-181, 2011

• T.-C. Chiang and Y.-P. Lai, MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism, in Congress on Evolutionary Computation (CEC 2011), pp. 1473–1480, 2011

• M. Gong, F. Liu, W. Zhang, L. Jiao, and Q. Zhang, Interactive MOEA/D for multi-objective decision making, in Genetic and Evolutionary Computation Conference (GECCO 2011), pp. 721–728, 2011

• A. Kafafy, A. Bounekkar, and S. Bonnevay, A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems, in Genetic and Evolutionary Computation Conference (GECCO 2011), pp. 497-504, 2011

GRASP in MOEA/D

• S. Zapotecas Martínez, and C. A. Coello Coello, A multi-objective particle swarm optimizer based on decomposition, in Genetic and Evolutionary Computation Conference (GECCO 2011), pp. 69-76, 2011

MOEA/D + PSO

• J. Durillo, Q. Zhang, A. Nebro, and E. Alba, Distribution of computational effort in parallel MOEA/D, in Learning and Intelligent Optimization (LION 2011), pp. 488-502, 2011

• S. Jiang, Z. Cai, J. Zhang, and Y.-S. Ong, Multiobjective optimization by decomposition with Pareto-adaptive weight vectors, in Natural Computation (ICNC 2011), pp. 1260–1264, 2011

2010

• Q. Zhang, W. Liu, E. Tsang, and B. Virginas, Expensive multiobjective optimization by MOEA/D with Gaussian process model, IEEE Transactions on Evolutionary Computation, vol. 14, no. 3, pp. 456– 474, 2010

This version uses EGO in MOEA/D for dealing with expensive MOPs

• Y. Mei, K. Tang and X. Yao, Decomposition-based memetic algorithm for multi-objective capacitated arc routing problem, IEEE Transactions on Evolutionary Computation, vol. 15, no. 2, pp. 151-165, 2010

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

• J. R. Figueira, A. Liefooghe, E-G. Talbi, and A. P. Wierzbicki, A parallel multiple reference point approach for multi-objective optimization, European Journal of Operational Research, vol. 205, no. 2, pp. 390-400, 2010

• 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, pp. 257-280, 2010

• 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, pp. 87-111, 2010

• A. Waldock, and D. Corne, Multiple objective optimisation applied to route planning, in Genetic and Evolutionary Computation Conference (GECCO 2010), pp. 1827-1834, 2010

MOEA/D is experimented on routing

• H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, Simultaneous use of different scalarizing functions in MOEA/D, in Genetic and Evolutionary Computation Conference (GECCO 2010), pp. 519–526, 2010

This version proposes two approaches for using different aggregation functions simultaneously

• Q. Zhang, H. Li, D. Maringer, and E. Tsang, MOEA/D with NBI-style Tchebycheff approach for portfolio management, in Congress on Evolutionary Computation (CEC 2010), pp. 1-8, 2010

A new decomposition approach is proposed in this paper

• A. Konstantinidis, C. Charalambous, A. Zhou, and Q. Zhang, Multi-objective mobile agent-based Sensor Network Routing using MOEA/D, in Congress on Evolutionary Computation (CEC 2010), pp. 1-8, 2010

• C-M. Chen, Y-P. Chen, T-C. Shen, and J. K. Zao, Optimizing degree distributions in LT codes by using the multiobjective evolutionary algorithm based on decomposition, in Congress on Evolutionary Computation (CEC 2010), pp. 1-8, 2010

• Y-H. Chan, T-C. Chiang, and L-C. Fu, A two-phase evolutionary algorithm for multiobjective mining of classification rules, in Congress on Evolutionary Computation (CEC 2010), pp. 1-7, 2010

• B. Liu, F. V. Fernández, Q. Zhang, M. Pak, S. Sipahi, and G. Gielen, An enhanced MOEA/D-DE and its application to multiobjective analog cell sizing, in Congress on Evolutionary Computation (CEC 2010), pp. 1-7, 2010

• N. Al Moubayed, A. Petrovski, and J. McCall, A novel smart multi-objective particle swarm optimisation using decomposition, in Parallel Problem Solving from Nature (PPSN 2010), pp. 1-10, 2010

MOEA/D+PSO is proposed for continuous optimization

• W. Huang and H. Li, On the differential evolution schemes in MOEA/D, in Natural Computation (ICNC 2010), pp. 2788–2792, 2010

• A.J. Nebro, and J.J. Durillo, A Study of the parallelization of the multi-objective metaheuristic MOEA/D, in Learning and Intelligent Optimization (LION 4), pp: 303-317, 2010

• H-L. Liu, F-Q. Gu, and Y-M. Cheung, T-MOEA/D: MOEA/D with objective transform in multi-objective problems, in Information Science and Management Engineering (ISME 2010), pp. 282-285, 2010

2009

• H. Li and Q. Zhang, Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284–302, 2009

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

• Q. Zhang, W. Liu, and H. Li, The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances, in Congress on Evolutionary Computation (CEC 2009), pp. 203– 208, 2009

Noting that different subproblems require different amounts of computational resources, a strategy for dynamical resource allocation is introduced in this version, it won the CEC 2009 Competition

• 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, in Congress on Evolutionary Computation (CEC 2009), pp. 2508– 2515, 2009

• C-M. Chen, Y-P. Chen, and Q. Zhang, Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization, in Congress on Evolutionary Computation (CEC 2009), pp. 209-216, 2009

• H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations, in Systems, Man, and Cybernetics (SMC 2009), pp. 1820-1825, 2009

• P. Palmers, T. McConnaghy, M. Steyaert, G. Gielen, Massively multi-topology sizing of analog integrated circuits, in Design, Automation & Test in Europe Conference & Exhibition (DATE 2009), pp. 706-711, 2009

• I. Guerra-Gómez, E. Tlelo-Cuautle, T. McConaghy, L. G. de la Fraga, G. Gielen, G. Reyes-Salgado, and J. M. Muñoz-Pacheco, Sizing mixed-mode circuits by multi-objective evolutionary algorithms, in Midwest Symposium on Circuits and Systems (MWSCAS 2009), pp. 813-816, 2009

• I. Guerra-Gomez, E. Tlelo-Cuautle, T. McConaghy, and G. Gielen, Decomposition-based multi-objective optimization of second-generation current conveyors, in Midwest Symposium on Circuits and Systems (MWSCAS 2009), pp. 220-223, 2009

• A. Konstantinidis, K. Yang, and Q. Zhang, Problem-specific encoding and genetic operation for a multi-objective deployment and power assignment problem in wireless sensor networks, in International Conference on Communications (ICC 2009), pp. 1-6, 2009

2008

• P-C. Chang, S-S. Chen, and Q. Zhang, MOEA/D for flowshop scheduling problems, in Congress on Evolutionary Computation (CEC 2008), pp. 1433-1438, 2008

2007

• Q. Zhang and H. Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712–731, 2007

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

2003

• H. Ishibuchi, T. Yoshida, and T. Murata, Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling, IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 204-223, 2003

• E. Hughes, Multiple single objective Pareto sampling, in Congress on Evolutionary Computation (CEC 2003), pp. 2678–2684, 2003

• L. Paquete and T.Stûtzle, A two-phase local search for the biobjective traveling salesman problem, in Evolutionary Multi-Criterion Optimization (EMO 2003), pp. 479–493, 2003

2002

• A. Jaszkiewicz, On the performance of multiple-objective genetic local search on the 0/1 knapsack problem a comparative experiment, IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 402–412, 2002

2001

• T. Murata, H. Ishibuchi, and M. Gen, Specification of genetic search directions in cellular multi-objective genetic algorithms, in Evolutionary Multi-Criterion Optimization (EMO 2001), pp. 82-95, 2001

2000

• T. Murata, H. Ishibuchi, and M. Gen, Cellular genetic local search for multi-objective optimization, in Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 307-314, 2000

• T. Murata and M. Gen, Cellular genetic algorithm for multi-objective optimization, in Asian Fuzzy System Symposium, pp. 538-542, 2000

1998

• H. Ishibuchi, and T. Murata, A multi-objective genetic local search algorithm and its application to flowshop scheduling, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 28, no. 3, pp. 392–403, 1998