Publications
Edited Books
1. Harrison, K.R., Elsayed, S., Garanovich, I.L., Weir, T., Boswell, S.G., Sarker, R.A. Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling. Adaptation, Learning, and Optimization. eBook DOI: 978-3-030-88315-7. Vol (16). Springer
2. Leu, G., Singh, H., Elsayed, S. (Eds.). (2016). Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings (Vol. 8). Springer
Refereed Journal Articles
3. Mara, S.T.W., Sarker, R., Essam, D. and Elsayed, S., 2023. Solving electric vehicle-drone routing problem using memetic algorithm. Swarm and Evolutionary Computation, p.101295.
4. Meselhi, M.A., Elsayed, S.M., Essam, D.L. and Sarker, R.A., 2023. Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments. CMC-Computers Materials & Continua, 74(1), pp.1-17.
5. Hamza, N., Sarker, R., Essam, D. and Elsayed, S., 2023. Evolutionary approach for dynamic constrained optimization problems. Alexandria Engineering Journal, 66, pp.827-843.
6. Ahrari A; Elsayed S; Sarker R; Essam D; Coello CAC, 2022, Revisiting Implicit and Explicit Averaging for Noisy Optimization’, IEEE Transactions on Evolutionary Computation, in press
7. Mohamed, R.E., Elsayed, S., Hunjet, R., Abbass, H., 2022. Connectivity-Aware Particle Swarm Optimisation for Shepherding-based Swarm Guidance. IEEE Transactions on Emerging Topics in Computational Intelligence, in press
8. Harrison KR; Elsayed SM; Garanovich IL; Weir T; Boswell SG; Sarker RA, 2022, ‘Generating datasets for the project portfolio selection and scheduling problem’, Data in Brief, vol. 42, pp. 108208 - 108208,
9. Harrison, K.R., Elsayed, S.M., Weir, T., Garanovich, I.L., Boswell, S.G. and Sarker, R.A., 2022. Solving a novel multi-divisional project portfolio selection and scheduling problem. Engineering Applications of Artificial Intelligence, 112, p.104771.
10. Meselhi, M., Sarker, R., Essam, D. and Elsayed, S., 2022. A decomposition approach for large-scale non-separable optimization problems. Applied Soft Computing, 115, p.108168.
11. Ahrari, A., Elsayed, S., Sarker, R., Essam, D. and Coello, C.A.C., 2022. PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods. SoftwareX, 17, p.100961.
12. Ahrari, A., Elsayed, S., Sarker, R., Essam, D. and Coello, C.A.C., 2021. Static and dynamic multimodal optimization by improved covariance matrix self-adaptation evolution strategy with repelling subpopulations. IEEE Transactions on Evolutionary Computation, 26(3), pp.527-541.
13. Elfeky, E. Z., Elsayed, S., Marsh, L., Essam, D., Cochrane, M., Sims, B., & Sarker, R. (2021). A Systematic Review of Coevolution in Real-Time Strategy Games. IEEE Access, pp. 136647 - 136665.
14. Zaman, F., Elsayed, S., Sarker, R., Essam, D., & Coello Coello. A. C. (2021). Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions. IEEE Transactions on Cybernetics, In press
15. Ahrari, A., Elsayed, S., Sarker, R., Essam, D. and Coello Coello, C.: A Novel Parametric Benchmark Generator for Dynamic Multimodal Optimization. Swarm and Evolutionary Computation, vol. 65, pp. 100924 – 100924, 2021.
16. Harrison, K.R., Elsayed, S., Garanovich, I., Weir, T., Galister, M., Boswell, S., Taylor, R. and Sarker, R. A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design. IEEE Access. In press, 83410 - 83430
17. Saad, H.M., Chakrabortty, R.K., Elsayed, S. and Ryan, M.J., 2021. Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling, IEEE Access, pp. 38488 – 38502
18. Ahrari, A., Elsayed, S., Sarker, R., Essam, D. and Coello Coello, C.: Adaptive Multilevel Prediction Method for Dynamic Multimodal Optimization (2021) IEEE Transactions on Evolutionary Computation 25, no. 3 pp.463-477
19. Ahrari, A; Elsayed, S.; Sarker, R; Essam, D., Coello Coello, C.: A Heredity-based Adaptive Variation Operator for Reinitialization in Dynamic Multi-objective Problems’, Applied Soft Computing, vol. 101, pages 107027, 2020
20. Elsayed, S., Sarker, R., Essam, D., Coello Coello, C.: Evolutionary Approach for Large-Scale Mine Scheduling. Information Sciences, vol. 523, pp. 77- 90, 2020
21. Meselhi, M., Elsayed, S., Sarker, R., Essam, D.:” Contribution Based Co-evolutionary Algorithm for Large Problems,” IEEE Access, vol 8, pp. 203369 - 203381, 2020
22. El-Fiqi, H., Campbell, B., Elsayed, S., Perry, A., Singh, H., Hunjet, R., Abbass. H.: The Limits of Reactive Shepherding Approaches for Swarm Guidance,” EEE Access, 8, pp.214658-214671
23. Ahrari, A; Elsayed, S.; Sarker, R; Essam, D., Coello Coello, C.: Weighted Pointwise Prediction Method for Dynamic Multiobjective Optimization’, Information Sciences, vol. 546, pp. 349-367, 2020
24. Sallam, K; Elsayed S.; Chakrabortty, R.; Ryan, M.: Evolutionary Framework with Reinforcement Learning-based Mutation Adaptation, IEEE Access, vol. 8, pp.194045-194071, 2020
25. Li, K., Elsayed, S., Sarker, R., Essam, D., “Landscape-Based Similarity Check Strategy for Dynamic Optimization Problems,” IEEE Access, vol. 8, pp. 178570-178586, 2020
26. Zaman, F; Elsayed, S.; Sarker, R; Essam, D., Coello Coello, C.: An Evolutionary Approach for Resource Constrained Project Scheduling with Uncertain Changes, Computers & Operations Research, 125, p.105104
27. Zaman, F; Elsayed, S.; Sarker, R; Essam, D.: Resource Constrained Project Scheduling with Dynamic Disruption Recovery, IEEE Access, vol. 146, 2020
28. Zaman, F; Elsayed, S.; Sarker, R; Essam, D.: Hybrid evolutionary algorithm for large-scale project scheduling problems, Computers and Industrial Engineering, vol. 146, 2020
29. Harrison, K.R., Elsayed, S., Garanovich, I., Weir, T., Galister, M., Boswell, S., Taylor, R. and Sarker, R.. Portfolio Optimization for Defence Applications. IEEE Access. pp. 60152 - 60178, 2020
30. Sallam, K., Elsayed, S., Sarker, R., Essam, D. (2020): Landscape-Assisted Multi-Operator Differential Evolution for Solving Constrained Optimization Problems. Expert Systems With Applications, 162, p.113033
31. Liu, C., Zhao, Yan, B., Elsayed, S., and Sarker, R.: Transfer Learning-Assisted Multi-Objective Evolutionary Clustering Framework with Decomposition for High-Dimensional Data. Information Sciences, pp. 440-456, 2019
32. Fernandez-Rojas, R., Perry, A., Singh, H., Campbell, B., Elsayed, S., Hunjet, R., and Abbass, H.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey. IEEE Access, pp. 33304 -33328, 2019
33. Zaman, M.F., Elsayed, S., Sarker, R., Essam, D., Coello Coello, C.: Multi-Method based Algorithm for Multi-objective Problems under Uncertainty. Information Sciences, 481, 81-109 , 2019
34. Liu, C., Zhao, Elsayed, S., Ray, T., and Sarker, R: Adaptive sorting-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 23(2), pp.247-25, 2018
35. Elsayed, S., Sarker, R., Coello Coello, C.: Fuzzy Rule-based Design of Evolutionary Algorithm for Optimization. IEEE Transactions on Cybernetics, 49(1), pp.301-314, 2017
36. Elsayed, S., Sarker, R., Coello Coello, C.: Sequence-based Deterministic Initialization for Evolutionary Algorithms. IEEE Transactions on Cybernetics, 47(9), 2911-2923 (2017)
37. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Finding Nash Equilibria in Electricity Markets. IEEE Transactions on Evolutionary Computation, 22(4), pp.536-549, 2017
38. Elsayed, S., Sarker, R., Ray, T., Coello Coello, C.: Consolidated Optimization Algorithm for Resource-constrained Project Scheduling Problems. Information Sciences, 418, 346-368 (2017)
39. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Landscape-Based Adaptive Operator Selection Mechanism for Differential Evolution. Information Sciences, 2017, 418, 383-404
40. Elsayed, S., Sarker, R., Coello, C. C., & Ray, T. (2018). Adaptation of operators and continuous control parameters in differential evolution for constrained optimization. Soft Computing, 22(19), 6595-6616
41. Shafie, K., Elsayed, S., Sarker, R., Ryan, M.: Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms. Applied Soft Computing, 56, 717-729, 2017.
42. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Co-evolutionary Approach for Strategic Bidding in Competitive Electricity Markets. Applied Soft Computing, 51, 1-22 (2017)
43. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Power Generation Planning with Uncertain Renewable Energy. Energy, 112, 408-419 (2016)
44. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Configuring Two-algorithm-based Evolutionary Approach for Solving Dynamic Economic Dispatch Problems. Engineering Applications of Artificial Intelligence, 53, 105-125 (2016)
45. Elsayed, S., Sarker, R.: Differential evolution framework for big data optimization. Memetic Computing, 8 (1), 17-33, (2016)
46. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Dynamic Economic Dispatch Problems. IEEE Transactions on Power Systems, 1-10 (2015)
47. ayed, E., Essam, D., Sarker, R., Elsayed, S.: Decomposition-based evolutionary algorithm for large scale constrained problems. Information Sciences, 316, 457-486 (2015)
48. Mabrok, M.A., Elsayed, S., Ryan, M.J.: Mathematical framework for recursive model-based system design. Nonlinear Dynamics, 84, 223–236 (2016)
49. Elsayed, S., Sarker, R., Essam, D.: Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimization. Applied Soft Computing, 26, 515-522 (2015)
50. Elsayed, S., Sarker, R., Essam, D.: Survey of Uses of Evolutionary Computation Algorithms and Swarm Intelligence for Network Intrusion Detection. International Journal of Computational Intelligence and Applications, 14(4) (2015).
51. Sarker, R., Elsayed, S., Ray, T.: Differential evolution with dynamic parameters selection for optimization problems. IEEE Transactions on Evolutionary Computation 18(5), 689-707 (2014)
52. Elsayed, S., Sarker, R., Mezura-Montes, E.: Self-adaptive mix of particle swarm methodologies for constrained optimization. Information Sciences, 27, 216-233 (2014)
53. Hamza, N., Sarker, R., Essam, D., Deb, K., Elsayed, S.: A constraint consensus memetic algorithm for solving constrained optimization problems. Engineering Optimization, 46(11), 1447-1464 (2014)
54. Sarker, R., Elsayed, S.: Evolutionary algorithm for analyzing higher degree research student recruitment and completion. Cogent Engineering, 2(1), 1063760 (2015).
55. Elsayed, S., Sarker, R., Essam, D.L.: A self-adaptive combined strategies algorithm for constrained optimization using differential evolution. Applied Mathematics and Computation, 241, 267-282 (2014)
56. Elsayed, S., Sarker, R., Essam, D.L.: A new genetic algorithm for solving optimization problems. Engineering Applications of Artificial Intelligence, 27(0), 57-69 (2014)
57. Elsayed, S., Sarker, R., Essam, D.L.: Adaptive Configuration of evolutionary algorithms for constrained optimization. Applied Mathematics and Computation, 222, 680-711 (2013)
58. Elsayed, S., Sarker, R., Essam, D.L.: Self-adaptive differential evolution incorporating a heuristic mixing of operators. Computational Optimization and Applications, 54(3), 771-790 (2013)
59. Elsayed, S., Sarker, R., Essam, D.L.: An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems. IEEE Transactions on Industrial Informatics, 9(1), 89-99 (2013)
60. Elsayed, S., Sarker, R., Essam, D.: On an Evolutionary Approach for Constrained Optimization Problem Solving. Applied Soft Computing, 12(10), 3208-3227 (2012)
61. Elsayed, S., Sarker, R., Essam, D.L.: Multi-operator based evolutionary algorithms for solving constrained optimization problems. Computers and Operations Research, 38(12), 1877-1896 (2011)
Scholarly book chapters
62. Harrison, K. R., Elsayed, S. M., Garanovich, I. L., Weir, T., Boswell, S. G., & Sarker, R. A. (2022). A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options. In Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling (pp. 89-123). Springer, Cham.
63. Harrison KR; Elsayed SM; Garanovich IL; Weir T; Boswell SG; Sarker RA, 2022, ‘A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options’, in Adaptation, Learning, and Optimization, pp. 89 - 123, Springer.
64. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Differential Evolution with Landscape-based Operator Selection for Solving numerical optimization problems. In: Leu, G., Singh, H., Elsayed, S. (eds.) Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium. Proceedings in Adaptation, Learning and Optimization, pp. 371-387. Springer International Publishing.
65. Zaman, F., Elsayed, S., Ray, T., Sarker, R.: An Evolutionary Framework for the Bi-Objectives Dynamic Economic and Environmental Dispatch Problems. In: Leu, G., Singh, H., Elsayed, S. (eds.) Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium. Proceedings in Adaptation, Learning and Optimization, pp. 495-508. Springer International Publishing.
66. Elsayed, S., Sarker, R.: Dynamic Configuration of Differential Evolution Control Parameters and Operators. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings. Lecture Notes in Computer Science, pp. 78-88. Springer International Publishing, Cham (2016)
67. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: A Double Action Genetic Algorithm for Scheduling the Wind-Thermal Generators. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings. Lecture Notes in Computer Science, pp. 258-269. Springer International Publishing, Cham (2016)
68. Elsayed, S., Zaman, M.F., Sarker, R.: Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, H.J. (eds.) Intelligent and Evolutionary Systems: The 19th Asia Pacific Symposium, IES 2015. Proceedings in Adaptation, Learning and Optimization, vol. 5, pp. 357-369. Springer International Publishing, Cham (2016)
69. Debie, E., Elsayed, S., Essam, D.L., Sarker, R.: Investigating Multi-Operator Differential Evolution for Feature Selection. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016. Lecture Notes in Computer Science, pp. 273-284. Springer International Publishing, Cham (2016)
70. Ali, I.M., Elsayed, S., Ray, T., Sarker, R.: A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings. Lecture Notes in Computer Science, pp. 209-220. Springer International Publishing, Cham (2016)
71. Elsayed, S., Sarker, R.: Evolving the Parameters of Differential Evolution Using Evolutionary Algorithms. In: Handa, H., Ishibuchi, H., Ong, Y.-S., Tan, K.C. (eds.) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, vol. 1. Proceedings in Adaptation, Learning and Optimization, pp. 523-534. Springer International Publishing, (2015)
72. Elsayed, S., Sarker, R., Essam, D.: The Influence of the Number of Initial Feasible Solutions on the Performance of an Evolutionary Optimization Algorithm. In: Bui, L., Ong, Y., Hoai, N., Ishibuchi, H., Suganthan, P. (eds.) Simulated Evolution and Learning, vol. 7673. Lecture Notes in Computer Science, pp. 1-11. Springer Berlin Heidelberg, (2012)
73. Elsayed, S., Sarker, R., Essam, D.: A Three-Strategy Based Differential Evolution Algorithm for Constrained Optimization. In: Wong, K., Mendis, B.S., Bouzerdoum, A. (eds.) Neural Information Processing. Theory and Algorithms, vol. 6443. Lecture Notes in Computer Science, pp. 585-592. Springer Berlin Heidelberg, (2010)
74. Elsayed, S., Sarker, R., Essam, D.: A Comparative Study of Different Variants of Genetic Algorithms for Constrained Optimization. In: Deb, K., Bhattacharya, A., Chakraborti, N., Chakroborty, P., Das, S., Dutta, J., Gupta, S., Jain, A., Aggarwal, V., Branke, J., Louis, S., Tan, K. (eds.) Simulated Evolution and Learning, vol. 6457. Lecture Notes in Computer Science, pp. 177-186. Springer Berlin / Heidelberg, (2010)
Refereed conference papers
75. Elsayed, S. and Hassanin, M., 2023, February. Improved Shepherding Model for Large-scale Swarm Control. In 2023 International Conference on Smart Computing and Application (ICSCA) (pp. 1-6). IEEE.Liu, J., Singh, H., Elsayed, S., Hunjet, R., and Abbass, H. "Effective robotic swarm shepherding in the presence of obstacles", IEEE 2023 Congress on Evolutionary Computation, 2023 Chicago USA
76. Nguyen, D., Singh, H., Elsayed, S., Hunjet, R., and Abbass, H. "Multi-Agent Knowledge Transfer in a Society of Interpretable Neural Network Minds for Dynamic Context Formation in Swarm Shepherding", International Joint Conference on Neural Networks (IJCNN), 2023
77. Hamza, N., Elsayed, S., Sarker, R., Essam, E., 2022. Evolutionary Constrained Optimization with Dynamic Changes and Uncertainty in the Objective Function. 14th International Conference on Software, Knowledge, Information Management and Applications, Cambodia
78. Mohamed, R.E., Hunjet, R., Elsayed, S. and Abbass, H., 2022. Communication Constraint On Swarm Guidance Tasks. 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Singapore, in press
79. Elfeky, E., Cochrane, M., Crase, S., Elsayed, S., Sims, B., Essam, D. and Sarker, R., 2022, December. Coevolution with Danger Zone Levels Strategy for the Weapon Target Assignment Problem. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 596-603). IEEE.
80. Harrison, K.R., Elsayed, S.M., Weir, T., Garanovich, I.L., Boswell, S.G. and Sarker, R.A., 2022, December. A Novel Multi-Objective Project Portfolio Selection and Scheduling Problem. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 480-487). IEEE.
81. Elfeky, E., Cochrane, M., Marsh, L., Elsayed, S., Sims, B., Crase, S., Essam, D. and Sarker, R., 2022, April. Coevolutionary Algorithm for Evolving Competitive Strategies in the Weapon Target Assignment Problem. In 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (pp. 9-18).
82. Hamza, N., Elsayed, S., Sarker, R. and Essam, D., 2022, July. Solving constrained problems with dynamic objective functions. In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
83. Mohamed, R.E., Hunjet, R., Elsayed, S. and Abbass, H., 2021. Deep Learning For Noisy Communication System. In 2021 31st International Telecommunication Networks and Applications Conference (ITNAC) (pp. 40-47). IEEE. [best student paper award]
84. Debie, E., Singh, H., Elsayed, S., Perry, A., Hunjet, R. & Abbass, H. 2021, ‘A Neuro-Evolution Approach to Shepherding Swarm Guidance in the Face of Uncertainty’, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 2634-2641
85. Harrison, K., Elsayed, S., Sarker, R., Garanovich, I., Weir, T., Boswel, S., Project Portfolio Selection with Defense Capability Options. Genetic and Evolutionary Computation Conference (GECCO), pp. 1825-1826, 2021.
86. Islam, T., Elsayed, S., Essam, D., Sarker, R., A Comparative Study of Different Forecasting Models for Energy Demand Forecasting. International Conference on Computational Intelligence in Data Mining, in press, 2021.
87. Saad, H., Chakrabortty, R., Elsayed, S., Quantum-Inspired Differential Evolution for Resource-Constrained Project-Scheduling: Preliminary study. IEEE Congress on Evolutionary Computation, pp. 1833-1839, 2021.
88. Meselhi, M., Elsayed, S., Sarker, R., Essam, D., Parallel Evolutionary Algorithm for EEG Optimization Problems. IEEE Congress on Evolutionary Computation, pp. 2577-2584, 2021.
89. Mohamed, R., Elsayed, S., Hunjet, R., Abbass, H., A Graph-based Approach for Shepherding Swarms with Limited Sensing Range. IEEE Congress on Evolutionary Computation, pp. 2315-2322, 2021.
90. Ahrari, A., Elsayed, S., Sarker, R., Essam, D., Coello Coello, C., Modular Analysis and Development of a Genetic Algorithm with Standardized Representation for Resource-Constrained Project Scheduling. IEEE Congress on Evolutionary Computation, pp. 612-619, 2021.
91. Elsayed, S., Singh, H., Debie, E., Perry, A., Campbell, B., Hunjet, R., Abbass. H.: Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2194-2201), 2020.
92. Harrison, K.R., Elsayed, S., Weir, T., Garanovich, I.L., Galister, M., Boswell, S., Taylor, R. & Sarker, R. 2020, ‘Multi-Period Project Selection and Scheduling for Defence Capability-Based Planning’, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 2020, pp. 4044-4050
93. Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D., Coello Coello, C.: Towards a More Practically Sound Formulation of Dynamic Problems and Performance Evaluation of Dynamic Search Methods. In2020 IEEE Symposium Series on Computational Intelligence (SSCI) 2020 Dec 1 (pp. 1387-1394). IEEE.
94. Harrison, K., Elsayed, S., Weir, T., Garanovich, I., Taylor, R. and Sarker, R.: An Exploration of Meta-Heuristic Approaches for the Project Portfolio Selection and Scheduling Problem in a Defence Context. In2020 IEEE Symposium Series on Computational Intelligence (SSCI) 2020 Dec 1 (pp. 1395-1402). IEEE.
95. El-Fiqi, H.; Campbell, B.; Elsayed, S.; Perry, A.; Singh, H.; Hunjet, R.; Abbass, H: A preliminary study towards an improved shepherding model. Genetic and Evolutionary Computation Conference Companion (GECCO ’20). Mexico, 2020
96. Elsayed, S.; Sarker, R., Hamza, N., Coello Coello, C., Mezura-Montes, E.: Enhancing Evolutionary Algorithms by Efficient Population Initialization for Constrained Problems’, IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK, 19 July 2020 - 24 July 2020
97. Sallam, K; Elsayed S.; Chakrabortty, R.; Ryan, M.: Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems, IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK, 19 July 2020 - 24 July 2020
98. Sallam, K.; Elsayed, S.; Chakrabortty, R.; Ryan, M., 2020: Multi-Operator Differential Evolution Algorithm for Solving Real-World Constrained Optimization Problems, IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK, 19 July 2020 - 24 July 2020
99. Ahrari A; Elsayed S; Sarker R; Essam D, 2019, ‘A New Prediction Approach for Dynamic Multiobjective Optimization’, in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 2268 - 2275
100. Zaman F; Elsayed S; Sarker R; Essam D; Coello Coello CA, 2019, ‘Evolutionary Algorithm for Project Scheduling under Irregular Resource Changes’, in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 403 - 410
101. Singh H; Campbell B; Elsayed S; Perry A; Hunjet R; Abbass H, 2019, ‘Modulation of Force Vectors for Effective Shepherding of a Swarm: A Bi-Objective Approach’, in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 2941 - 2948
102. Li K; Elsayed S; Sarker R; Essam D, 2019, ‘Quantum Differential Evolution: An Investigation’, in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 3022 - 3029
103. Liu, C., Zhao, Q., Yan, B., Elsayed, S. and Sarker, R., 2018, December. An Improved Multi-Objective Evolutionary Approach for Clustering High-Dimensional Data. In: 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), Zurich, Switzerland , 2018 pp. 184-190.
104. Meselhi M; Sarker R; Essam D; Elsayed S, 2018, ‘Decomposition of overlapping optimization functions’, in Proceedings of International Conference on Computers and Industrial Engineering, CIE, Auckland, New Zealand, presented at CIE 48, Computers & Industrial Engineering, Auckland, New Zealand, 02 December 2018 - 05 December 2018
105. 45. Meselhi, M., Essam D., Sarker, R., Elsayed, S.: Enhanced Differential Grouping for Large Scale Optimization. In: 10th International Joint Conference on Computational Intelligence (IJCCI2018), 06 - 08 December, 2018, Seville, Spain, (pp. 217-224).
106. Zaman, F., Elsayed, S., Sarker, R., Essam, D.: Scenario based Solution Approach for Uncertain Resource Constrained Scheduling Problems. In: IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1-8
107. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Landscape-based Differential Evolution for Constrained Optimization Problems. In: IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1-8
108. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Improved United Multi-Operator algorithm for Solving Optimization Problems. In: IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1-8
109. Meselhi, M., Elsayed, S., Essam D., Sarker, R.: Fast Differential Evolution for Big Optimization. In: The 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2017), 2017, Colombo, Sri Lanka, pp. 1-6
110. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Multi-method based Orthogonal Experimental Design Algorithm for Solving CEC2017 Competition Problems. In: IEEE Congress on Evolutionary Computation, Donostia - San Sebastián, Spain, 2017, pp. 1350-1357
111. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: A Two-phase Differential Evolution Framework for Solving Real-World Application Problems. The 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athena, Greece, pp. 1-8.
112. Elsayed, S., Sarker, R., Coello, C. C.: Enhanced Multi-operator Differential Evolution for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 4191-4198.
113. Elsayed, S., Hamza, N., Sarker, R.: Testing United Multi-operator Evolutionary Algorithms-II on Single Objective Optimization Problems. In: IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 2966-2973. Received Best Algorithm Award.
114. Zaman, F., Elsayed, S., Sarker, R.: A Co-evolutionary approach for optimal bidding strategy of multiple electricity suppliers. In: IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 3407-3715.
115. Aguilar-Justo, A., Mezura-Montes, E., Elsayed, S., Sarker, R.: Genetic-based search for decomposition based on Variable Interaction Identification for Constrained Problems. 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2016), Ixtapa, Mexico, 2016, pp. 1-6.
116. Elsayed, S., Sarker, R., Slay, J.: Evaluating the performance of a differential evolution algorithm in anomaly detection. In: IEEE Congress on Evolutionary Computation, 2015, pp. 2490-2497.
117. Elsayed, S., Sarker, R.: An Adaptive Configuration of Differential Evolution Algorithms for Big Data. In: IEEE Congress on Evolutionary Computation 2015, pp. 695-702.
118. Ali, I., Elsayed, S., Ray, T., Sarker, R.: Memetic algorithm for solving resource constrained project scheduling problems. In: IEEE Congress on Evolutionary Computation, 2015, pp. 2761-2767.
119. Sallam, K.M., Sarker, R., Essam, D.L., Elsayed, S.: Neurodynamic differential evolution algorithm and solving CEC2015 competition problems. In: IEEE Congress on Evolutionary Computation, 2015, pp. 1033-1040.
120. Zaman, M. D., Elsayed, S., Ray, T., Sarker, R.: An Evolutionary Approach for Scheduling Solar-thermal Power Generation System. In: International Conference on Computers & Industrial Engineering, 2015, 45, pp.1-8.
121. Sayed, E., Essam, D., Sarker, R., Elsayed, S.: A decomposition-based algorithm for dynamic economic dispatch problems. In: IEEE Congress on Evolutionary Computation 2014, pp. 1898-1905.
122. Greenwood, G.W., Elsayed, S., Sarker, R., Abbass, H.A.: Online generation of trajectories for autonomous vehicles using a multi-agent system. In: IEEE Congress on Evolutionary Computation, Beijing, 2014, pp. 1218-1224.
123. Elsayed, S., Sarker, R., Essam, D.L., Hamza, N.M.: Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization. In: IEEE Congress on Evolutionary Computation, Beijing, 2014, pp. 1650-1657. Received Best Algorithm Award.
124. Elsayed, S., Sarker, R., Essam, D.L.: United multi-operator evolutionary algorithms. In: IEEE Congress on Evolutionary Computation 2014, pp. 1006-1013.
125. Elsayed, S., Ray, T., Sarker, R.: A surrogate-assisted differential evolution algorithm with dynamic parameters selection for solving expensive optimization problems. In: IEEE Congress on Evolutionary Computation, 2014, pp. 1062-1068.
126. Elsayed, S., Sarker, R.: Differential Evolution with automatic population injection scheme for constrained problems. In: IEEE Symposium Series on Computational Intelligence, Singapore, 2013, pp. 112-118.
127. Elsayed, S., Sarker, R., Ray, T.: Differential Evolution with Automatic Parameter Configuration for Solving the CEC2013 Competition on Real-Parameter Optimization. In: IEEE Congress on Evolutionary Computation, Cancún, México, 2013, pp. 1932-1937.
128. Elsayed, S., Sarker, R., Mezura-montes, E.: Particle Swarm Optimizer for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, Cancún, México, 2013, pp. 1703-1711.
129. Elsayed, S., Sarker, R., Essam, D.: A Genetic Algorithm for Solving the CEC’2013 Competition Problems on Real-Parameter Optimization. In: IEEE Congress on Evolutionary Computation, Cancún, México, 2013, pp. 356-360.
130. Elsayed, S., Sarker, R., Ray, T.: Parameters Adaptation in Differential Evolution. In: IEEE Congress on Evolutionary Computation, Brisbane 2012, pp. 1- 8.
131. Elsayed, S., Sarker, R., Essam, D.: Memetic Multi-Topology Particle Swarm Optimizer for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, World Congress on Computational Intelligence (WCCI2012), Brisbane 2012, pp. 1-8.
132. Elsayed, S., Sarker, R., Essam, D.: Improved genetic algorithm for constrained optimization. In: International Conference on Computer Engineering and Systems, ICCES’2011, Cairo, Egypt, 2011, pp. 111-115.
133. Hamza, N., Elsayed, S., Essam, D., Sarker, R.: Differential evolution combined with constraint consensus for constrained optimization. In: IEEE Congress of Evolutionary Computation, New Orleans, LA, 2011 2011, pp. 865-872.
134. Elsayed, S., Sarker, R., Essam, D.L.: GA with a new multi-parent crossover for constrained optimization. In: IEEE Congress on Evolutionary Computation 2011, pp. 857-864.
135. Elsayed, S., Sarker, R., Essam, D.L.: Integrated strategies differential evolution algorithm with a local search for constrained optimization. In: IEEE Congress on Evolutionary Computation2011, pp. 2618-2625.
136. Elsayed, S., Sarker, R., Essam, D.L.: GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In: IEEE Congress on Evolutionary Computation 2011, pp. 1034-1040. Received Best Algorithm Award.
137. Elsayed, S., Sarker, R., Essam, D.: Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems. In: IEEE Congress of Evolutionary Computation, 2011, pp. 1041-1048.
138. Elsayed, S., Abd EL-Wahed, W., Ismail, N.: A Hybrid Genetic Scatter Search Algorithm for Solving Optimization Problems. In: the 6th International Conference on Informatics and Systems, Cairo, Egypt, pp. DS-12: DS1-7, 2008.
139. Elsayed, S., Abd EL-Wahed, W., Ismail, N.: Parallel Scatter Search Algorithm for Solving Optimization Problems. In: the 17th International Conference on Computer Theory and Applications (ICCTA’2007), Alexandria, Egypt, September 2007.