Shouyong Jiang

Research output

Research output per year 2014 2017 2018 2020 2021 2022 2024

• 25 Article
• 11 Published conference contribution
• 4 Unpublished paper
• 2 Working paper
• 3 More
  • 1 Abstract
  • 1 Conference article
  • 1 Preprint

Research output per year

Research output per year

Search results

• 2024

  Coordinate-Aware Mask R-CNN with Group Normalization: A Underwater Marine Animal Instance Segmentation Framework

  Yi, D., Ahmedov, H. B., Jiang, S., Li, Y., Flinn, S. J. & Fernandes, P. G., 6 Mar 2024, (E-pub ahead of print) In: Neurocomputing. 127488.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Computer aided manufacturing 100%
  • Masks 95%
  • Animals 87%
  • Fish 66%
  • Fishes 55%

• An improved brain storm optimization with chunking-grouping method

  Guo, J., Gao, C., Jiang, S., Xie, W. & Wu, Z., 15 Feb 2024, In: Concurrency and Computation: Practice and Experience. 36, 4, 20 p., e7924.

  Research output: Contribution to journal › Article › peer-review

  • Grouping 100%
  • Brain 95%
  • Optimization 66%
  • Strategy 41%
  • Intelligence 39%

• A Mahalanobis Distance-based Approach for Dynamic Multi-objective Optimization with Stochastic Changes

  Hu, Y., Zheng, J., Jiang, S., Yang, S., Zou, J. & Wang, R., 1 Feb 2024, In: IEEE Transactions on Evolutionary Computation. 28, 1, p. 238-251 14 p.

  Research output: Contribution to journal › Abstract › peer-review

  • Dynamic Optimization Problems 100%
  • Dynamic Optimization 98%
  • Mahalanobis Distance 94%
  • Multi-objective Optimization 84%
  • Multiobjective Optimization Problems 83%
  3 Citations (Scopus)
• 2023

  Biological survival optimization algorithm with its engineering and neural network applications

  Wang, L., Zhang, Q., He, X., Yang, S., Jiang, S. & Dong, Y., 1 May 2023, In: Soft Computing. 27, p. 6437–6463 7 p.

  Research output: Contribution to journal › Article › peer-review

  • Neural Network Applications 100%
  • Optimization Algorithm 59%
  • Engineering 45%
  • Neural networks 33%
  • Adjustment 27%

• Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

  Hu, Y., Zheng, J., Jiang, S., Yang, S. & Zou, J., 1 Apr 2023, In: IEEE Transactions on Cybernetics. 53, 4, p. 2572 - 2585 14 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Multiobjective optimization 100%
  • Population distribution 54%
  • Evolutionary algorithms 32%
  11 Citations (Scopus)

  6 Downloads (Pure)
• 2022

  CaR: A Cutting and Repulsion-Based Evolutionary Framework for Mixed-Integer Programming Problems

  Liu, J., Wang, Y., Huang, P. Q. & Jiang, S., Dec 2022, In: IEEE Transactions on Cybernetics. 52, 12, p. 13129 - 13141 13 p.

  Research output: Contribution to journal › Article › peer-review

  • Integer programming 100%
  5 Citations (Scopus)

• Evolutionary Dynamic Multi-Objective Optimisation: A survey

  Jiang, S., Zou, J., Yang, S. & Yao, X., 21 Nov 2022, In: ACM Computing Surveys. 55, 4, 47 p., 76.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Dynamic Optimization 100%
  • Evolutionary Dynamics 95%
  • Multi-objective Optimization 86%
  • Multiobjective optimization 65%
  • Performance Assessment 15%
  32 Citations (Scopus)

  10 Downloads (Pure)

• Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm

  Zhang, Q., He, X., Yang, S., Dong, Y., Song, H. & Jiang, S., Sept 2022, In: Information Sciences. 610, p. 868-886 19 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Polynomials 100%
  • Prediction 84%
  • Polynomial 53%
  • Multi-objective Optimization 40%
  • Predict 38%
  5 Citations (Scopus)

  1 Downloads (Pure)

• Enhanced Affinity Propagation Clustering on Heterogeneous Information Network

  Rajan, D., Jiang, S., Yi, D., Pang, W. & Coghill, G., 15 Aug 2022, (Accepted/In press).

  Research output: Contribution to conference › Unpublished paper › peer-review

  • Semantics 100%
  • Scalability 59%
  • Damping 55%

• OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

  Jiang, S., Otero Muras, I., Banga, J. R., Wang, Y., Kaiser, M. & Krasnogor, N., 15 Apr 2022, In: ACS Synthetic Biology. 11, 4, p. 1531–1541 11 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Gene Knockout Techniques 100%
  • Gene Amplification 95%
  • Escherichia coli 57%
  • Strain 57%
  • Genome 55%
  5 Citations (Scopus)

  4 Downloads (Pure)
• 2021

  Optimizing the flight route of UAV using biology migration algorithm

  Zhang, Q. Y., He, X. Y., Dong, Y. Q., Jiang, S. Y. & Song, H., 28 Sept 2021, In: Journal of Physics: Conference Series. 2025, 1, 8 p., 012052.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  File

  • pilotless aircraft 100%
  • biology 66%
  • flight 58%
  • routes 51%
  • intelligence 44%
  1 Citation (Scopus)

  5 Downloads (Pure)

• Dynamic multi-objective optimization algorithm based decomposition and preference

  Hu, Y., Zheng, J., Zou, J., Jiang, S. & Yang, S., 1 Sept 2021, In: Information Sciences. 571, p. 175-190 16 p.

  Research output: Contribution to journal › Article › peer-review

  • Dynamic Optimization 100%
  • Multi-objective Optimization 86%
  • Decomposition 77%
  • Optimization Algorithm 74%
  • Region of Interest 70%
  17 Citations (Scopus)

• Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

  Zhang, Q., Jiang, S., Yang, S. & Song, H., 3 Aug 2021, In: PloS ONE. 16, 8 August, 39 p., e0254839.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Benchmarking 100%
  • Population 72%
  • Linear Models 72%
  3 Citations (Scopus)

  8 Downloads (Pure)

• Deep learning based prediction on greenhouse crop yield combined TCN and RNN

  Gong, L., Yu, M., Jiang, S., Cutsuridis, V. & Pearson, S., 1 Jul 2021, In: Sensors. 21, 13, 16 p., 4537.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • crops 100%
  • Lycopersicon esculentum 91%
  • greenhouses 91%
  • Deep Learning 91%
  • Greenhouses 80%
  52 Citations (Scopus)

  5 Downloads (Pure)

• An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth

  Alhnaity, B., Kollias, S., Leontidis, G., Jiang, S., Schamp, B. & Pearson, S., 1 Jun 2021, In: Information Sciences. 560, p. 35-50 16 p., INS16166.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Memory Term 100%
  • Neural Networks 91%
  • Wavelets 88%
  • Forecasting 86%
  • Deep neural networks 73%
  34 Citations (Scopus)

  9 Downloads (Pure)

• Neural modeling of antisaccade performance of healthy controls and early Huntington's disease patients

  Cutsuridis, V., Jiang, S., Dunn, M. J., Rosser, A., Brawn, J. & Erichsen, J. T., 11 Jan 2021, In: Chaos. 31, 1, 013121.

  Research output: Contribution to journal › Article › peer-review

  • Modeling 100%
  • Performance 91%
  • Latency 73%
  • performance 67%
  • Error Rate 47%
  3 Citations (Scopus)

• On Analysis of Irregular Pareto Front Shapes

  Jiang, S., Guo, J., Alhnaity, B. & Zhang, Q., 2021, Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings. Ishibuchi, H., Zhang, Q., Cheng, R., Li, K., Li, H., Wang, H. & Zhou, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 15-25 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12654 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Pareto Front 100%
  • Irregular 64%
  • Irregularity 40%
  • Multiobjective Optimization Problems 13%
  • Evolutionary Algorithms 13%
  2 Citations (Scopus)
• 2020

  An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme

  Guo, J., Shao, M., Jiang, S. & Yang, S., 8 Jul 2020, GECCO 2020 companion: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, p. 165-166 2 p.

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Multi-objective Optimization 100%
  • Evolutionary Algorithms 97%
  • Optimization Algorithm 86%
  • Penalty 76%
  • Decompose 52%
  2 Citations (Scopus)

• NIHBA: A network interdiction approach for metabolic engineering design

  Jiang, S., Wang, Y., Kaiser, M. & Krasnogor, N., 1 Jun 2020, In: Bioinformatics. 36, 11, p. 3482-3492 11 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Bender Reaction 100%
  • Metabolic engineering 88%
  • Engineering Design 79%
  • Metabolic Engineering 78%
  • Bilevel Optimization 63%
  6 Citations (Scopus)

  5 Downloads (Pure)

• Triangular Gaussian mutation to differential evolution

  Guo, J., Wu, Y., Xie, W. & Jiang, S., 1 Jun 2020, In: Soft Computing. 24, 12, p. 9307-9320 14 p.

  Research output: Contribution to journal › Article › peer-review

  • Differential Evolution 100%
  • Mutation 75%
  • Triangular 67%
  • Strategy 53%
  • Exploitation 18%
  6 Citations (Scopus)

• A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

  Jiang, S., Kaiser, M., Yang, S., Kollias, S. & Krasnogor, N., Jun 2020, In: IEEE Transactions on Cybernetics. 50, 6, p. 2814-2826 13 p.

  Research output: Contribution to journal › Article › peer-review

  • Multiobjective optimization 100%
  • Scalability 12%
  • Set theory 9%
  29 Citations (Scopus)

• AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation

  Jiang, S., Li, H., Guo, J., Zhong, M., Yang, S., Kaiser, M. & Krasnogor, N., Apr 2020, In: Information Sciences. 515, p. 365-387 23 p.

  Research output: Contribution to journal › Article › peer-review

  • Evolutionary Algorithms 100%
  • Multi-objective Optimization 99%
  • Multiobjective optimization 76%
  • Evolutionary algorithms 74%
  • Set theory 52%
  17 Citations (Scopus)

• Novel Prediction Strategies for Dynamic Multiobjective Optimization

  Zhang, Q., Yang, S., Jiang, S., Wang, R. & Li, X., Apr 2020, In: IEEE Transactions on Evolutionary Computation. 24, 2, p. 260-274 15 p.

  Research output: Contribution to journal › Article › peer-review

  • Dynamic Optimization 100%
  • Multi-objective Optimization 86%
  • Multiobjective optimization 65%
  • Prediction 56%
  • Strategy 49%
  79 Citations (Scopus)

• An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth

  Alhnaity, B., Kollias, S., Leontidis, G., Jiang, S., Schamp, B. & Pearson, S., 2020, ArXiv.

  Research output: Working paper

  • Deep neural networks 100%
  • Long short-term memory 47%
  • Time series analysis 25%
  • Wavelet decomposition 25%
  • Agriculture 22%
• 2019

  Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

  Wang, Y., Yu, J., Yang, S., Jiang, S. & Zhao, S., Nov 2019, In: Swarm and Evolutionary Computation. 50, 23 p., 100559.

  Research output: Contribution to journal › Article › peer-review

  • Dynamic Optimization Problems 100%
  • Dynamic Optimization 98%
  • Evolutionary Dynamics 93%
  • Constrained Optimization 77%
  • Constrained Optimization Problem 76%
  17 Citations (Scopus)

• NIHBA: A Network Interdiction Approach with Hybrid Benders Algorithm for Strain Design

  Jiang, S., Wang, Y., Kaiser, M. & Krasnogor, N., 4 Sept 2019, bioRxiv.

  Research output: Working paper › Preprint

  Open Access

  • Metabolic Networks and Pathways 100%
  • Fluxes 29%
  • Scalability 29%

• A scalable test suite for continuous dynamic multiobjective optimisation

  Jiang, S., Kaiser, M., Yang, S., Kollias, S. & Krasnogor, N., 2019, ArXiv.

  Research output: Working paper

  • Multiobjective optimization 100%
  • Scalability 12%
  • Set theory 9%
• 2018

  An Empirical Study of Dynamic Triobjective Optimisation Problems

  Jiang, S., Kaiser, M., Wan, S., Guo, J., Yang, S. & Krasnogor, N., 28 Sept 2018, 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 8477667. (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Dynamic Optimization Problems 100%
  • Empirical Study 78%
  • Evolutionary Algorithms 54%
  • Time-varying 44%
  • Dynamic Optimization 32%
  5 Citations (Scopus)

• Less detectable environmental changes in dynamic multiobjective optimisation

  Jiang, S., Kaiser, M., Guo, J., Yang, S. & Krasnogor, N., 2 Jul 2018, GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, p. 673-680 8 p. (GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Dynamic Optimization 100%
  • Multi-objective Optimization 86%
  • Evolutionary Algorithms 83%
  • Multiobjective optimization 65%
  • Dynamic Environment 54%
  11 Citations (Scopus)

• Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

  Jiang, S., Yang, S., Wang, Y. & Liu, X., Apr 2018, In: IEEE Transactions on Evolutionary Computation. 22, 2, p. 296-313 18 p.

  Research output: Contribution to journal › Article › peer-review

  • Multi-objective Evolutionary Algorithm 100%
  • Evolutionary algorithms 58%
  • Decomposition 46%
  • Decompose 41%
  • High Performance 20%
  69 Citations (Scopus)

• Benchmarks for CEC18 competition on dynamic multiobjective optimization

  Jiang, S., Yang, S., Yao, X., Tan, K. C., Kaiser, M. & Krasnogor, N., 10 Jan 2018.

  Research output: Contribution to conference › Unpublished paper › peer-review

• Strain design as multiobjective network interdiction problem: A preliminary approach

  Torres, M., Jiang, S., Pelta, D., Kaiser, M. & Krasnogor, N., 2018, Advances in Artificial Intelligence - 18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018, Proceedings. Gonzalez, A., Troncoso, A., Herrera, F., Damas, S., Montes, R., Alonso, S. & Cordon, O. (eds.). Springer Verlag, p. 273-282 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11160 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • NSGA-II 100%
  • Design 92%
  • Metabolic engineering 46%
  • Metabolic Network 45%
  • Bilevel Optimization 44%
  1 Citation (Scopus)
• 2017

  Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

  Yang, S., Jiang, S. & Jiang, Y., 1 Aug 2017, In: Soft Computing. 21, 16, p. 4677-4691 15 p.

  Research output: Contribution to journal › Article › peer-review

  • Multi-objective Evolutionary Algorithm 100%
  • Multiobjective optimization 60%
  • Penalty 60%
  • Evolutionary algorithms 58%
  • Decomposition 46%
  63 Citations (Scopus)

• A strength pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization

  Jiang, S. & Yang, S., Jun 2017, In: IEEE Transactions on Evolutionary Computation. 21, 3, p. 329-346 18 p., 7886269.

  Research output: Contribution to journal › Article › peer-review

  • Evolutionary Algorithms 100%
  • Pareto 92%
  • Evolutionary algorithms 76%
  • Optimization 63%
  • Nondominated Solutions 31%
  282 Citations (Scopus)

• On the use of hypervolume for diversity measurement of Pareto front approximations

  Jiang, S., Yang, S. & Li, M., 9 Feb 2017, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc., 7850225. (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Pareto Front 100%
  • Pareto 64%
  • Multiobjective optimization 63%
  • Approximation 55%
  • Evolutionary algorithms 30%
  11 Citations (Scopus)

• A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization

  Jiang, S. & Yang, S., Feb 2017, In: IEEE Transactions on Evolutionary Computation. 21, 1, p. 65-82 18 p., 7527677.

  Research output: Contribution to journal › Article › peer-review

  • Dynamic Optimization 100%
  • Multi-objective Optimization 86%
  • Evolutionary Algorithms 83%
  • Multiobjective optimization 65%
  • Evolutionary algorithms 64%
  236 Citations (Scopus)

• Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons

  Jiang, S. & Yang, S., Jan 2017, In: IEEE Transactions on Cybernetics. 47, 1, p. 198-211 14 p.

  Research output: Contribution to journal › Article › peer-review

  • Multiobjective optimization 100%
  • Evolutionary algorithms 38%
  • Set theory 13%
  152 Citations (Scopus)
• 2016

  An adaptive penalty-based boundary intersection approach for multiobjective evolutionary algorithm based on decomposition

  Guo, J., Yang, S. & Jiang, S., 14 Nov 2016, 2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., p. 2145-2152 8 p. 7744053. (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Multi-objective Evolutionary Algorithm 100%
  • Penalty 60%
  • Evolutionary algorithms 58%
  • Intersection 48%
  • Decomposition 46%
  5 Citations (Scopus)

• An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

  Jiang, S. & Yang, S., Feb 2016, In: IEEE Transactions on Cybernetics. 46, 2, p. 421-437 17 p., 7060668.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Multiobjective optimization 100%
  • Evolutionary algorithms 97%
  • Decomposition 76%
  200 Citations (Scopus)

• Convergence versus diversity in multiobjective optimization

  Jiang, S. & Yang, S., 2016, Parallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings. Hart, E., Paechter, B., Handl, J., López-Ibáñez, M., Lewis, P. R. & Ochoa, G. (eds.). Springer Verlag, p. 984-993 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9921 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Multi-objective Optimization 100%
  • Sorting 79%
  • Multiobjective optimization 76%
  • Evolutionary Algorithms 38%
  • Evolutionary algorithms 37%
  14 Citations (Scopus)
• 2015

  A fast strength Pareto evolutionary algorithm incorporating predefined preference information

  Jiang, S. & Yang, S., 1 Sept 2015.

  Research output: Contribution to conference › Unpublished paper › peer-review

• Approximating multiobjective optimization problems with complex Pareto fronts

  Jiang, S. & Yang, S., 1 Sept 2015.

  Research output: Contribution to conference › Unpublished paper › peer-review
• 2014

  A framework of scalable dynamic test problems for dynamic multi-objective optimization

  Jiang, S. & Yang, S., 12 Dec 2014, IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 32-39 8 p. 7007864. (IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Dynamic Optimization 100%
  • Multi-objective Optimization 86%
  • Dynamic Problem 78%
  • Dynamic Optimization Problems 76%
  • Test Problems 68%
  22 Citations (Scopus)

• A benchmark generator for dynamic multi-objective optimization problems

  Jiang, S. & Yang, S., 17 Oct 2014, 2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings. Neagu, D., Kiran, M. & Trundle, P. (eds.). Institute of Electrical and Electronics Engineers Inc., 6930171. (2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Dynamic Optimization Problems 100%
  • Dynamic Optimization 98%
  • Multi-objective Optimization 84%
  • Multiobjective Optimization Problems 83%
  • Benchmark 67%
  12 Citations (Scopus)

• An improved quantum-behaved particle swarm optimization algorithm based on linear interpolation

  Jiang, S. & Yang, S., 16 Sept 2014, Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., p. 769-775 7 p. 6900354. (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014).

  Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

  • Particle Swarm Optimization Algorithm 100%
  • Linear Interpolation 79%
  • Particle Swarm Optimization 54%
  • Interpolation 53%
  • Particle swarm optimization (PSO) 52%
  9 Citations (Scopus)