Publications

2021

  • Raphaël Cosson, Bilel Derbel, Arnaud Liefooghe, Hernán E. Aguirre, Kiyoshi Tanaka, Qingfu Zhang. Decomposition-Based Multi-objective Landscape Features and Automated Algorithm Selection. European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2021), LNCS 12692: 34-50, Virtual Event, 2021 ★ best paper nomination ★

  • Bilel Derbel, Geoffrey Pruvost, Byung-Woo Hong. Enhancing MOEA/D with Escape Mechanisms. IEEE Congress on Evolutionary Computation (CEC 2021): 1163-1170, Kraków, Poland, 2021

  • Bilel Derbel, Lorenzo Canonne. A graph coloring based parallel hill climber for large-scale NK-landscapes. Genetic and Evolutionary Computation Conference (GECCO 2021): 216-224, Lille France, 2021. ★ best paper nomination ★

  • Xinye Cai, Chao Xia, Qingfu Zhang, Zhiwei Mei, Han Hu, Lisong Wang, Jun Hu. The Collaborative Local Search Based on Dynamic-Constrained Decomposition With Grids for Combinatorial Multiobjective Optimization. IEEE Transactions on Cybernetics 51(5): 2639-2650, 2021

2020

  • Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, Qingfu Zhang. On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D. European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2020), LNCS 12102: 131-147, Seville, Spain, 2020

  • Nicolas Berveglieri, Bilel Derbel, Arnaud Liefooghe, Hernán E. Aguirre, Qingfu Zhang, Kiyoshi Tanaka. Designing parallelism in surrogate-assisted multiobjective optimization based on decomposition. Genetic and Evolutionary Computation Conference (GECCO 2020): 462-470, Cancún, Mexico, 2020

  • Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Sébastien Verel, Qingfu Zhang. Surrogate-assisted multi-objective combinatorial optimization based on decomposition and Walsh basis. Genetic and Evolutionary Computation Conference (GECCO 2020): 542-550, Cancún, Mexico, 2020

  • Arnaud Liefooghe, Fabio Daolio, Sébastien Verel, Bilel Derbel, Hernán E. Aguirre, Kiyoshi Tanaka. Landscape-Aware Performance Prediction for Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 24(6): 1063-1077, 2020

  • Mengyuan Wu, Ke Li, Sam Kwong, Qingfu Zhang. Evolutionary Many-Objective Optimization Based on Adversarial Decomposition. IEEE Transactions on Cybernetics 50(2): 753-764, 2020

  • Qingling Zhu, Qingfu Zhang, Qiuzhen Lin. A Constrained Multiobjective Evolutionary Algorithm With Detect-and-Escape Strategy. IEEE Transactions on Evolutionary Computation 24(5): 938-947, 2020

  • Jialong Shi, Qingfu Zhang, Jianyong Sun. PPLS/D: Parallel Pareto Local Search Based on Decomposition. IEEE Transactions on Cybernetics 50(3): 1060-1071, 2020

  • Omar Abdelkafi, Bilel Derbel, Arnaud Liefooghe, Darrell Whitley. On the Design of a Partition Crossover for the Quadratic Assignment Problem. International Conference on Parallel Problem Solving from Nature (PPSN 2020), LNCS 12269,: 303-316, Leiden, The Netherlands, 2020

  • Arnaud Liefooghe, Sébastien Verel, Bilel Derbel, Hernán E. Aguirre, Kiyoshi Tanaka. Dominance, Indicator and Decomposition Based Search for Multi-objective QAP: Landscape Analysis and Automated Algorithm Selection. International Conference on Parallel Problem Solving from Nature (PPSN 2020), LNCS 12269: 33-47, Leiden, The Netherlands, 2020

2019

  • Nicolas Berveglieri, Bilel Derbel, Arnaud Liefooghe, Hernán E. Aguirre, Qingfu Zhang, Kiyoshi Tanaka. Surrogate-assisted multiobjective optimization based on decomposition: a comprehensive comparative analysis. Genetic and Evolutionary Computation Conference (GECCO 2019): 507-515, Prague, Czech Republic, 2019

  • Bilel Derbel, Arnaud Liefooghe, Sébastien Verel, Hernán E. Aguirre, Kiyoshi Tanaka. New features for continuous exploratory landscape analysis based on the SOO tree. Conference on Foundations of Genetic Algorithms (FOGA 2019): 72-86, Potsdam, Germany, 2019

  • Hui Li, Kalyanmoy Deb, Qingfu Zhang, Ponnuthurai N. Suganthan, Lei Chen. Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties. Swarm and Evolutionary Computation 46: 104-117, 2019

  • Zhenkun Wang, Yew-Soon Ong, Jianyong Sun, Abhishek Gupta, Qingfu Zhang. A Generator for Multiobjective Test Problems With Difficult-to-Approximate Pareto Front Boundaries. IEEE Trans. on Evolutionary Computation 23(4): 556-571, 2019

  • Mengyuan Wu, Ke Li, Sam Kwong, Qingfu Zhang, Jun Zhang. Learning to Decompose: A Paradigm for Decomposition-Based Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 23(3): 376-390, 2019

  • Hu Zhang, Jianyong Sun, Tonglin Liu, Ke Zhang, Qingfu Zhang. Balancing exploration and exploitation in multiobjective evolutionary optimization. Information Sciences, Volume 497: 129-148, 2019

  • Wenjun Wang, Shaoqiang Yang, Qiuzhen Lin, Qingfu Zhang, Ka-Chun Wong, Carlos A. Coello Coello, Jianyong Chen. An Effective Ensemble Framework for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 23(4): 645-659, 2019

  • Xiaoliang Ma, Xiaodong Li, Qingfu Zhang, Ke Tang, Zhengping Liang, Weixin Xie, Zexuan Zhu. A Survey on Cooperative Co-evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 23(3): 421-441, 2019

  • Xiaoyu He, Yuren Zhou, Zefeng Chen, Qingfu Zhang. Evolutionary Many-objective Optimization based on Dynamical Decomposition. IEEE Transactions on Evolutionary Computation 23(3): 361-375, 2019

  • Xinye Cai, Haoran Sun, Qingfu Zhang, Yuhua Huang. A Grid Weighted Sum Pareto Local Search for Combinatorial Multi and Many-Objective Optimization. IEEE Transactions on Cybernetics 49(9): 3586-3598, 2019

  • Jianyong Sun, Hu Zhang, Aimin Zhou, Qingfu Zhang. Learning from a Stream of Non-Stationary and Dependent Data in Multiobjective Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 23(4): 541-555, 2019

  • Qingling Zhu, Qingfu Zhang, Qiuzhen Lin, Jianyong Sun. MOEA/D with Two Types of Weight Vectors for Handling Constraints. IEEE Congress on Evolutionary Computation (CEC 2019): 1359-1365, Wellington, New Zealand, 2019

2018

  • Jialong Shi, Qingfu Zhang. A new cooperative framework for parallel trajectory-based metaheuristics. Applied Soft Computing 65: 374-386, 2018

  • Xiaoliang Ma, Xiaodong Li, Qingfu Zhang, Ke Tang, Zhengping Liang, Weixin Xie, Zexuan Zhu. A survey on cooperative co-evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2018

  • Mengyuan Wu, Ke Li ; Sam Kwong, Qingfu Zhang, Jun Zhang. Learning to decompose: A paradigm for decomposition-based multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2018

  • Jianyong Sun, Hu Zhang, Aimin Zhou, Qingfu Zhang. Learning from a stream of non-stationary and dependent data in multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 2018

  • Xiaoyu He, Yuren Zhou, Zefeng Chen, Qingfu Zhang. Evolutionary many-objective optimization based on dynamical decomposition. IEEE Transactions on Evolutionary Computation, 2018

  • Xinye Cai, Haoran Sun, Qingfu Zhang, Yuhua Huang. A grid weighted sum Pareto local search for combinatorial multi- and many-objective optimization. IEEE Transactions on Cybernetics, 2018

  • Xinye Cai, Zhiwei Mei, Zhun Fan, Qingfu Zhang. A constrained decomposition approach with grids for evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, vol 22(4), pp 564-577, 2018

  • Hai-Lin Liu, Lei Chen, Qingfu Zhang, Kalyanmoy Deb. Adaptively allocating search effort in challenging many-objective optimization problems. IEEE Transactions on Evolutionary Computation, vol 22(3), pp 433-448, 2018

  • Xiaoliang Ma, Qingfu Zhang, Guangdong Tian, Junshan Yang, Zexuan Zhu. On Tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, vol 22(2), pp 226-244, 2018

  • Miyako Sagawa, Hernan Aguirre, Fabio Daolio, Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Kiyoshi Tanaka. A machine-learning approach to select important variables for recombination on many-objective evolutionary optimization. International Journal of Smart Computing and Artificial Intelligence, vol 2(1), pp 59-78, 2018

  • Sébastien Verel, Bilel Derbel, Arnaud Liefooghe, Hernán Aguirre and Kiyoshi Tanaka. A surrogate model based on Walsh decomposition for pseudo-boolean functions. 15th International Conference on Parallel Problem Solving from Nature (PPSN 2018), Lecture Notes in Computer Science (LNCS), vol 11102, pp 181-193, Coimbra, Portugal, 2018

  • Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Manuel López-Ibánez, Hernán Aguirre and Kiyoshi Tanaka. On Pareto local optimal solutions networks. 15th International Conference on Parallel Problem Solving from Nature (PPSN 2018), Lecture Notes in Computer Science (LNCS), vol 11102, pp 232-244, Coimbra, Portugal, 2018 ★ best paper nomination ★

  • Bilel Derbel, Arnaud Liefooghe, Qingfu Zhang, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka. A set-oriented MOEA/D. Genetic and Evolutionary Computation Conference (GECCO 2018), pp 617-624, Kyoto, Japan, 2018

  • Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe, Jianyong Sun. Parallel Pareto local search revisited — First experimental results on bi-objective UBQP. Genetic and Evolutionary Computation Conference (GECCO 2018), pp 753-760, Kyoto, Japan, 2018

  • Yuri Marca, Hernán Aguirre, Saúl Zapotecas, Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Kiyoshi Tanaka. Pareto dominance-based MOEAs on Problems with Difficult Pareto Set Topologies. Genetic and Evolutionary Computation Conference (GECCO 2018, Companion), pp 189-190, Kyoto, Japan, 2018

  • Hugo Monzón, Hernán Aguirre, Sébastien Verel, Arnaud Liefooghe, Bilel Derbel, Kiyoshi Tanaka. Studying MOEAs dynamics and their performance using a three compartmental model. Genetic and Evolutionary Computation Conference (GECCO 2018, Companion), pp 191-192, Kyoto, Japan, 2018

  • Ke Li, Qingfu Zhang. Tutorial on Decomposition multi-objective optimisation: current developments and future opportunities. Genetic and Evolutionary Computation Conference (GECCO 2018, Companion), pp 907-936, Kyoto, Japan, 2018

2017

  • Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe, Sébastien Verel. Using parallel strategies to speed up Pareto local search. 11th International Conference on Simulated Evolution and Learning (SEAL 2017), Lecture Notes in Computer Science (LNCS), vol 10593, pp 62–74, Shenzhen, China, 2017 ★ best student paper award ★

  • Hugo Monzon, Hernán Aguirre Sébastien Verel, Arnaud Liefooghe, Bilel Derbel, Kiyoshi Tanaka. Closed state model for understanding the dynamics of MOEAs. Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, 2017

  • Mengyuan Wu, Sam Kwong, Yuheng Jia, Ke Li, Qingfu Zhang. Adaptive weights generation for decomposition-based multi-objective optimization using Gaussian process regression. Genetic and Evolutionary Computation Conference (GECCO 2017), pp 641-648, Berlin, Germany, 2017

  • Xi Lin, Qingfu Zhang, Sam Kwong. An efficient batch expensive multi-objective evolutionary algorithm based on decomposition. IEEE Congress on Evolutionary Computation (CEC 2017), pp 1343-1349, San Sebastián, Spain, 2017

  • Jialong Shi, Qingfu Zhang, Bilel Derbel and Arnaud Liefooghe. A parallel tabu search for the unconstrained binary quadratic programming problem. IEEE Congress on Evolutionary Computation (CEC 2017), pp 557–564, San Sebastián, Spain, 2017

  • Oliver Cuate, Bilel Derbel, Arnaud Liefooghe, El-Ghazali Talbi, Oliver Schütze. An approach for the local exploration of discrete many objective optimization problems. 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Lecture Notes in Computer Science (LNCS), vol 10173, pp 135-150, Münster, Germany, 2017