Publications

Edited Books

1.  Harrison, K.R., Elsayed, S., Garanovich, I.L., Weir, T., Boswell, S.G. and Sarker, R.A., 2022. Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling. Springer International Publishing AG.

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.  Li, K., Elsayed, S., Sarker, R. and Essam, D. (2024) Multiple landscape measure-based approach for dynamic optimization problems. Swarm and Evolutionary Computation, 88, p.101578.

4.  Liu, J., Sarker, R., Elsayed, S., Essam, D., and Siswanto, N. (2024) Large-scale evolutionary optimization: a review and comparative study, Swarm and Evolutionary Computation, p.101466

5.  Mara, S. T. W., Sarker, R., Essam, D., & Elsayed, S. (2023). Solving electric vehicle–drone routing problem using memetic algorithm. Swarm and Evolutionary Computation, 79. doi:10.1016/j.swevo.2023.101295

6.  Hamza, N., Sarker, R., Essam, D., & Elsayed, S. (2023). Evolutionary approach for dynamic constrained optimisation problems. Alexandria Engineering Journal, 66, 827-843. doi:10.1016/j.aej.2022.10.072

7.  Meselhi, M. A., Elsayed, S. M., Essam, D. L., & Sarker, R. A. (2023). Modified Differential Evolution Algorithm for Solving Dynamic Optimisation with Existence of Infeasible Environments. Computers, Materials and Continua, 74(1). doi:10.32604/cmc.2023.027448

8.  Mohamed, R. E., Hunjet, R., Elsayed, S., & Abbass, H. (2023). Connectivity-Aware Particle Swarm Optimisation for Swarm Shepherding. IEEE Transactions on Emerging Topics in Computational Intelligence, 1-23. doi:10.1109/tetci.2022.3195178

9.  Zaman, F., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2022). Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions. IEEE Transactions on Cybernetics, 52(11), 11299-11312. doi:10.1109/TCYB.2021.3097312

10.   Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2022). Revisiting Implicit and Explicit Averaging for Noisy Optimisation. IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2022.3201090

11.   Harrison, K. R., Elsayed, S. M., Weir, T., Garanovich, I. L., Boswell, S. G., & Sarker, R. A. (2022). Solving a novel multi-divisional project portfolio selection and scheduling problem. Engineering Applications of Artificial Intelligence, 112. doi:10.1016/j.engappai.2022.104771

12.   Harrison, K. R., Elsayed, S. M., Garanovich, I. L., Weir, T., Boswell, S. G., & Sarker, R. A. (2022). Generating datasets for the project portfolio selection and scheduling problem. Data in Brief, 42, 10 pages. doi:10.1016/j.dib.2022.108208

13.   Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2022). PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimisation methods. SoftwareX, 17. doi:10.1016/j.softx.2021.100961

14.   Meselhi, M., Sarker, R., Essam, D., & Elsayed, S. (2022). A decomposition approach for large-scale non-separable optimisation problems. Applied Soft Computing, 115. doi:10.1016/j.asoc.2021.108168

15.   Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2022). Static and Dynamic Multimodal Optimisation by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations. IEEE Transactions on Evolutionary Computation, 26(3), 527-541. doi:10.1109/TEVC.2021.3117116

16.   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, 9, 136647-136665. doi:10.1109/ACCESS.2021.3115768

17.   Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2021). A Novel Parametric benchmark generator for dynamic multimodal optimisation. Swarm and Evolutionary Computation, 65. doi:10.1016/j.swevo.2021.100924

18.   Harrison, K. R., Elsayed, S., Garanovich, I. L., Weir, T., Galister, M., Boswell, S., . . . Sarker, R. (2021). A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design. IEEE Access, 9, 83410-83430. doi:10.1109/ACCESS.2021.3086070

19.   Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2021). A heredity-based adaptive variation operator for reinitialisation in dynamic multiobjective problems. Applied Soft Computing, 101. doi:10.1016/j.asoc.2020.107027

20.   Saad, H., Chakrabortty, R., Elsayed, S., & Ryan, M. (2021). Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling. IEEE Access. doi:10.1109/ACCESS.2021.3062790

21.   Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello Coello, C. A. (2021). Weighted pointwise prediction method for dynamic multiobjective optimisation. Information Sciences, 546, 349-367. doi:10.1016/j.ins.2020.08.015

22.   Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello Coello, C. A. (2021). Adaptive Multilevel Prediction Method for Dynamic Multimodal Optimization. IEEE Transactions on Evolutionary Computation, 25(3), 463-477. doi:10.1109/TEVC.2021.3051172

23.   Zaman, F., Elsayed, S., Sarker, R., Essam, D., & Coello Coello, C. A. (2021). An evolutionary approach for resource constrained project scheduling with uncertain changes. Computers and Operations Research, 125. doi:10.1016/j.cor.2020.105104

24.   Sallam, K. M., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2020). Landscape-assisted multi-operator differential evolution for solving constrained optimisation problems. Expert Systems with Applications, 162. doi:10.1016/j.eswa.2019.113033

25.   Elsayed, S., Singh, H., Debie, E., Perry, A., Campbell, B., Hunjel, R., & Abbass, H. (2020). Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution. 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2194-2201. doi:10.1109/SSCI47803.2020.9308572

26.   El-Fiqi, H., Campbell, B., Elsayed, S., Perry, A., Singh, H. K., Hunjet, R., & Abbass, H. A. (2020). The Limits of Reactive Shepherding Approaches for Swarm Guidance. IEEE Access, 8, 214658-214671. doi:10.1109/ACCESS.2020.3037325

27.   Meselhi, M. A., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2020). Contribution based co-evolutionary algorithm for large-scale optimisation problems. IEEE Access, 8, 203369-203381. doi:10.1109/ACCESS.2020.3036438

28.   Sallam, K., Elsayed, S., Chakrabortty, R., & Ryan, M. (2020). Evolutionary Framework with Reinforcement Learning-based Mutation Adaptation. IEEE Access. doi:10.1109/ACCESS.2020.3033593

29.   Li, K., Elsayed, S. M., Sarker, R., & Essam, D. (2020). Landscape-based similarity check strategy for dynamic optimisation problems. IEEE Access, 8, 178570-178586. doi:10.1109/ACCESS.2020.3026339

30.   Zaman, F., Elsayed, S. M., Sarker, R. A., & Essam, D. (2020). Resource Constrained Project Scheduling with Dynamic Disruption Recovery. IEEE Access, 8, 144866-144879. doi:10.1109/ACCESS.2020.3014940

31.   Zaman, F., Elsayed, S., Sarker, R., & Essam, D. (2020). Hybrid evolutionary algorithm for large-scale project scheduling problems. Computers and Industrial Engineering, 146. doi:10.1016/j.cie.2020.106567

32.   Elsayed, S., Sarker, R., Essam, D., & Coello Coello, C. A. (2020). Evolutionary approach for large-Scale mine scheduling. Information Sciences, 523, 77-90. doi:10.1016/j.ins.2020.02.074

33.   Harrison, K. R., Elsayed, S., Garanovich, I., Weir, T., Galister, M., Boswell, S., . . . Sarker, R. (2020). Portfolio Optimisation for Defence Applications. IEEE Access, 8, 60152-60178. doi:10.1109/ACCESS.2020.2983141

34.   Liu, C., Zhao, Q., Yan, B., Elsayed, S., & Sarker, R. (2019). Transfer learning-assisted multiobjective evolutionary clustering framework with decomposition for high-dimensional data. Information Sciences, 505, 440-456. doi:10.1016/j.ins.2019.07.099

35.   Zaman, F., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2019). Multi-Method based algorithm for multiobjective problems under uncertainty. Information Sciences, 481, 81-109. doi:10.1016/j.ins.2018.12.072

36.   Fernandez-Rojas, R., Perry, A., Singh, H., Campbell, B., Elsayed, S., Hunjet, R., & Abbass, H. A. (2019). Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey. IEEE Access, 7, 33304-33328. doi:10.1109/ACCESS.2019.2902812

37.   Elsayed, S., Sarker, R., Coello, C. C., & Ray, T. (2018). Adaptation of operators and continuous control parameters in differential evolution for constrained optimisation. Soft Computing, 22(19), 6595-6616. doi:10.1007/s00500-017-2712-6

38.   Liu, C., Zhao, Q., Yan, B., Elsayed, S., Ray, T., & Sarker, R. (2019). Adaptive Sorting-Based Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 23(2), 247-257. doi:10.1109/TEVC.2018.2848254

39.   Sallam, K. M., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2017). Landscape-based adaptive operator selection mechanism for differential evolution. Information Sciences, 418-419, 383-404. doi:10.1016/j.ins.2017.08.028

40.   Elsayed, S., Sarker, R., Ray, T., & Coello, C. C. (2017). Consolidated optimisation algorithm for resource-constrained project scheduling problems. Information Sciences, 418-419, 346-362. doi:10.1016/j.ins.2017.08.023

41.   Elsayed, S., Sarker, R., & Coello Coello, C. A. (2017). Fuzzy Rule-Based Design of Evolutionary Algorithm for Optimization. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2017.2772849

42.   Zaman, F., Elsayed, S. M., Ray, T., & Sarker, R. A. (2018). Evolutionary Algorithms for Finding Nash Equilibria in Electricity Markets. IEEE Transactions on Evolutionary Computation, 22(4), 536-549. doi:10.1109/TEVC.2017.2742502

43.   Shafi, K., Elsayed, S., Sarker, R., & Ryan, M. (2017). Scenario-based multi-period program optimisation for capability-based planning using evolutionary algorithms. Applied Soft Computing Journal, 56, 717-729. doi:10.1016/j.asoc.2016.07.009

44.   Zaman, F., Elsayed, S. M., Ray, T., & Sarker, R. A. (2017). Co-evolutionary approach for strategic bidding in competitive electricity markets. Applied Soft Computing Journal, 51, 1-22. doi:10.1016/j.asoc.2016.11.049

45.   Elsayed, S., Sarker, R., & Coello Coello, C. A. (2017). Sequence-Based Deterministic Initialisation for Evolutionary Algorithms. IEEE Transactions on Cybernetics, 47(9), 2911-2923. doi:10.1109/TCYB.2016.2630722

46.   Zaman, F., Elsayed, S. M., Ray, T., & Sarker, R. A. (2016). Evolutionary algorithms for power generation planning with uncertain renewable energy. Energy, 112, 408-419. doi:10.1016/j.energy.2016.06.083

47.   Zaman, M. F., Elsayed., Ray., & Sarker. (2016). Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems. Engineering Applications of Artificial Intelligence, 53, 105-125. doi:10.1016/j.engappai.2016.04.001

48.   Elsayed, S., & Sarker, R. (2016). Differential evolution framework for big data optimisation. Memetic Computing, 8(1), 17-33. doi:10.1007/s12293-015-0174-x

49.   Zaman, M. F., Elsayed, S. M., Ray, T., & Sarker, R. A. (2016). Evolutionary Algorithms for Dynamic Economic Dispatch Problems. IEEE Transactions on Power Systems, 31(2), 1486-1495. doi:10.1109/TPWRS.2015.2428714

50.   Elsayed, S., Sarker, R., & Essam, D. (2015). Survey of Uses of Evolutionary Computation Algorithms and Swarm Intelligence for Network Intrusion Detection. International Journal of Computational Intelligence and Applications, 14(4). doi:10.1142/S146902681550025X

51.   Mabrok, M. A., Elsayed, S., & Ryan, M. J. (2015). Mathematical framework for recursive model-based system design. Nonlinear Dynamics, 84(1), 223-236. doi:10.1007/s11071-015-2418-1

52.   Sayed, E., Essam, D., Sarker, R., & Elsayed, S. (2015). Decomposition-based evolutionary algorithm for large scale constrained problems. Information Sciences, 316, 457-486. doi:10.1016/j.ins.2014.10.035

53.   Sarker, R., & Elsayed, S. (2015). Evolutionary algorithm for analysing higher degree research student recruitment and completion. Cogent Engineering, 2(1). doi:10.1080/23311916.2015.1063760

54.   Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2015). Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimisation. Applied Soft Computing Journal, 26, 515-522. doi:10.1016/j.asoc.2014.10.011

55.   Elsayed, S. M., Sarker, R. A., & Mezura-Montes, E. (2014). Self-adaptive mix of particle swarm methodologies for constrained optimisation. Information Sciences, 277, 216-233. doi:10.1016/j.ins.2014.01.051

56.   Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2014). A self-adaptive combined strategies algorithm for constrained optimisation using differential evolution. Applied Mathematics and Computation, 241, 267-282. doi:10.1016/j.amc.2014.05.018

57.   Hamza, N. M., Sarker, R. A., Essam, D. L., Deb, K., & Elsayed, S. M. (2014). A constraint consensus memetic algorithm for solving constrained optimisation problems. Engineering Optimisation, 46(11), 1447-1464. doi:10.1080/0305215X.2013.846336

58.   Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2014). A new genetic algorithm for solving optimisation problems. Engineering Applications of Artificial Intelligence, 27, 57-69. doi:10.1016/j.engappai.2013.09.013

59.   Sarker, R. A., Elsayed, S. M., & Ray, T. (2014). Differential evolution with dynamic parameters selection for optimisation problems. IEEE Transactions on Evolutionary Computation, 18(5), 689-707. doi:10.1109/TEVC.2013.2281528

60.   Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2013). Adaptive Configuration of evolutionary algorithms for constrained optimisation. Applied Mathematics and Computation, 222, 680-711. doi:10.1016/j.amc.2013.07.068

61.   Elsayed., Sarker, R., & Essam, D. (2012). On an Evolutionary Approach for Constrained Optimisation Problem Solving. Applied soft computing : the official journal of the World Federation on Soft Computing (WFSC), 12(10), 3208-3227. doi:10.1016/j.asoc.2012.05.013

62.   Elsayed., Sarker, R., & Essam, D. (2013). Self-adaptive differential evolution incorporating a heuristic mixing of operators. Computational Optimisation and Applications, 54(3), 771-790. doi:10.1007/s10589-012-9493-8

63.   Elsayed., Sarker, R., & Essam, D. (2013). An improved self-adaptive differential evolution algorithm for optimisation problems. IEEE Transactions on Industrial Informatics, 9(1), 89-99. doi:10.1109/TII.2012.2198658

 

Scholarly book chapters

65.   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 Adaptation, Learning, and Optimization (Vol. 26, pp. 89-123).

66.   Sarker, R. A., Harrison, K. R., & Elsayed, S. M. (2022). Evolutionary Approaches for Project Portfolio Optimization: An Overview. In Adaptation, Learning, and Optimisation (Vol. 26, pp. 9-35).

67.   Harrison, K. R., Garanovich, I. L., Weir, T., Boswell, S. G., Elsayed, S. M., & Sarker, R. A. (2022). Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction. In Adaptation, Learning, and Optimization (Vol. 26, pp. 1-8).

68.   Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Differential Evolution with Landscape-based Operator Selection for Solving numerical optimisation 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.

69.   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.

70.   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)

71.   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)

72.   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 Optimisation, vol. 5, pp. 357-369. Springer International Publishing, Cham (2016)

73.   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)

74.   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)

75.   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 Optimisation, pp. 523-534. Springer International Publishing, (2015)

76.   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)

77.   Elsayed, S., Sarker, R., Essam, D.: A Three-Strategy Based Differential Evolution Algorithm for Constrained Optimisation. 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)

78.   Elsayed, S., Sarker, R., Essam, D.: A Comparative Study of Different Variants of Genetic Algorithms for Constrained Optimisation. 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

79.   Elsayed, S., & Mabrok, M. (2024). Large-Scale Swarm Control in Cluttered Environments. In Social Robotics (Vol. 14453, pp. 384-395). Springer Nature.

80.   Hamza, N., Sarker, R., Essam, D. & Elsayed, S. (2024) Constraint Consensus for Solving Large-scale Constrained Optimization Problems. IEEE Congress on Evolutionary Computation, CEC 2024 (accepted)

81.   Elfeky, E., Sherman, G., Elsayed, S., Shovon, H., Lodge, R., Campbell, B., Essam, D. & Sarker, R. (2024) Differential Evolution Algorithm for Battlefield Surveillance Sensor Placement. IEEE Congress on Evolutionary Computation, CEC 2024 (accepted)

82.   Liu, L., Essam, D., Elsayed, D., Sarker, R., Garanovich, I. Weir, T. & Boswell. S. (2024) Large-scale Project Portfolio Selection and Scheduling Problem: A Comparison of Exact Solvers and Metaheuristics. IEEE Congress on Evolutionary Computation, CEC 2024 (accepted)

83.   Li, K., Elsayed, S., Sarker, R., & Essam, D. (2023). Landscape-Based Genetic Algorithm with Quantum Entanglement for Dynamic Optimization Problems. In 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 187-192). IEEE.

84.   Paul, D., Mo, H., Elsayed, S., Chakrabortty, R. Predicting Energy Consumption of Battery-operated Electric Vehicles: A Comparative Performance Assessment. In 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), in press.

85.   Elsayed, S. (2023). Towards Mitigating ChatGPT's Negative Impact on Education: Optimizing Question Design Through Bloom's Taxonomy. In 2023 IEEE Region 10 Symposium, TENSYMP 2023. doi:10.1109/TENSYMP55890.2023.10223662

86.   Elsayed, S., & Hassanin, M. (2023). Improved Shepherding Model for Large-scale Swarm Control. In International Conference on Smart Computing and Application, ICSCA 2023. doi:10.1109/ICSCA57840.2023.10087385

87.   Mara, S. T. W., Elsayed, S., Essam, D., & Sarker, R. (2023). Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST Vol. 486 LNICST (pp. 197-214). doi:10.1007/978-3-031-30855-0_14

88.   Nguyen, D. T., Singh, H., Elsayed, S., Hunjet, R., & Abbass, H. A. (2023). Multi-agent Knowledge Transfer in a Society of Interpretable Neural Network Minds for Dynamic Context Formation in Swarm Shepherding. In Proceedings of the International Joint Conference on Neural Networks Vol. 2023-June. doi:10.1109/IJCNN54540.2023.10191371

89.   Liu, J., Singh, H., Elsayed, S., Hunjet, R., & Abbass, H. A. (2023). Effective Robotic Swarm Shepherding in the Presence of Obstacles. In 2023 IEEE Congress on Evolutionary Computation, CEC 2023. doi:10.1109/CEC53210.2023.10254040

90.   Elfeky, E., Cochrane, M., Marsh, L., Elsayed, S., Sims, B., Crase, S., . . . Sarker, R. (2022). Coevolutionary Algorithm for Evolving Competitive Strategies in the Weapon Target Assignment Problem. In ACM International Conference Proceeding Series (pp. 9-18). doi:10.1145/3533050.3533052

91.   Hamza, N., Elsayed, S., Sarker, R., & Essam, D. (2022). Solving constrained problems with dynamic objective functions. In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870354

92.   Mohamed, R. E., Elsayed, S., Hunjet, R., & Abbass, H. (2022). Reinforcement Learning for Solving Communication Problems in Shepherding. In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 1626-1635). doi:10.1109/SSCI51031.2022.10022160

93.   Hamza, N., Elsayed, S., Sarker, R., & Essam, D. (2022). Evolutionary Constrained Optimization with Dynamic Changes and Uncertainty in the Objective Function. In International Conference on Software, Knowledge Information, Industrial Management and Applications, SKIMA Vol. 2022-December (pp. 54-60). doi:10.1109/SKIMA57145.2022.10029469

94.   Elfeky, E., Cochrane, M., Crase, S., Elsayed, S., Sims, B., Essam, D., & Sarker, R. (2022). Coevolution with Danger Zone Levels Strategy for the Weapon Target Assignment Problem. In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 596-603). doi:10.1109/SSCI51031.2022.10022268

95.   Harrison, K. R., Elsayed, S. M., Weir, T., Garanovich, I. L., Boswell, S. G., & Sarker, R. A. (2022). A Novel Multi-Objective Project Portfolio Selection and Scheduling Problem. In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 480-487). doi:10.1109/SSCI51031.2022.10022287

96.   Islam, T., Elsayed, S., Essam, D., & Sarker, R. (2022). A Comparative Study of Different Forecasting Models for Energy Demand Forecasting. In Smart Innovation, Systems and Technologies Vol. 281 (pp. 553-564). Tekkali, India. doi:10.1007/978-981-16-9447-9_42

97.   Saad, H., Chakrabortty, R., & Elsayed, S. (2021). Quantum-Inspired Differential Evolution for Resource-Constrained Project-Scheduling: Preliminary study. In 2021 IEEE Congress on Evolutionary Computation. Krakow, Poland: IEEE. doi:10.1109/CEC45853.2021.9504970

98.   Harrison, K. R., Elsayed, S., Sarker, R. A., Garanovich, I. L., Weir, T., & Boswell, S. G. (2021). Project portfolio selection with defense capability options. In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 1825-1826). doi:10.1145/3449726.3463126

99.   Meselhi, M. A., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2021). Parallel Evolutionary Algorithm for EEG Optimization Problems. In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 2577-2584). doi:10.1109/CEC45853.2021.9504925

100. Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello Coello, C. A. (2021). Modular Analysis and Development of a Genetic Algorithm with Standardized Representation for Resource-Constrained Project Scheduling. In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 612-619). doi:10.1109/CEC45853.2021.9504950

101. Mohamed, R. E., Hunjet, R., Elsayed, S., & Abbass, H. (2021). Deep Learning for Noisy Communication System. In 2021 31st International Telecommunication Networks and Applications Conference, ITNAC 2021 (pp. 40-47). doi:10.1109/ITNAC53136.2021.9652171

102. 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. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 2634-2641). doi:10.1109/SMC52423.2021.9659082

103. Mohamed, R. E., Elsayed, S., Hunjet, R., & Abbass, H. (2021). A Graph-based Approach for Shepherding Swarms with Limited Sensing Range. In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 2315-2322). doi:10.1109/CEC45853.2021.9504706

104. Ahrari, A., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2020). Towards a More Practically Sound Formulation of Dynamic Problems and Performance Evaluation of Dynamic Search Methods. In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 1387-1394). doi:10.1109/SSCI47803.2020.9308464

105. Elsayed, S., Singh, H., Debie, E., Perry, A., Campbell, B., Hunjel, R., & Abbass, H. (2020). Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020) (pp. 2194-2201). Due to COVID-19 restrictions, the event was held virtually with the broadcast from Canberra, Australia.: IEEE. doi:10.1109/SSCI47803.2020.9308572

106. Harrison, K. R., Elsayed, S., Weir, T., Garanovich, I. L., Taylor, R., & Sarker, R. (2020). An Exploration of Meta-Heuristic Approaches for the Project Portfolio Selection and Scheduling Problem in a Defence Context. In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 1395-1402). doi:10.1109/SSCI47803.2020.9308608

107. Harrison, K. R., Elsayed, S., Weir, T., Garanovich, I. L., Galister, M., Boswell, S., . . . Sarker, R. (2020). Multi-Period Project Selection and Scheduling for Defence Capability-Based Planning. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics Vol. 2020-October (pp. 4044-4050). doi:10.1109/SMC42975.2020.9283334

108. Sallam, K., Elsayed, S., Chakrabortty, R., & Ryan, M. (2020). Multi-Operator Differential Evolution Algorithm for Solving Real-World Constrained Optimization Problems. In IEEE Congress on Evolutionary Computation (CEC). Glasgow, United Kingdom. doi:10.1109/CEC48606.2020.9185722.

109. Sallam, K., Elsayed, S., Chakrabortty, R., & Ryan, M. (2020). Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Glasgow, United Kingdom: IEEE. doi:10.1109/CEC48606.2020.9185577

110. El-Fiqi, H., Campbell, B., Elsayed, S., Perry, A., Singh, H. K., Hunjet, R., & Abbass, H. (2020). A preliminary study towards an improved shepherding model. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 75-76). doi:10.1145/3377929.3390067

111. Elsayed, S., Sarker, R., Hamza, N., Coello, C. A. C., & Mezura-Montes, E. (2020). Enhancing Evolutionary Algorithms by Efficient Population Initialization for Constrained Problems. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185509

112. 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). Wellington, New Zealand, New Zealand: IEEE Xplore. doi:10.1109/CEC.2019.8790215

113. 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). doi:10.1109/CEC.2019.8790303

114. 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). doi:10.1109/CEC.2019.8790228

115. Zaman, F., Elsayed, S., Sarker, R., Essam, D., & Coello Coello, C. A. (2019). Evolutionary Algorithm for Project Scheduling under Irregular Resource Changes. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 403-410). doi:10.1109/CEC.2019.8790170

116. Zaman, F., Elsayed, S., Sarker, R., & Essam, D. (2018). A New Hybrid Approach for the Multimode Resource-Constrained Project Scheduling Problems. In The 48th International Conference on Computers and Industrial Engineering (CIE 48),. The University of Auckland, New Zealand.

117. Zaman, F., Elsayed, S., Sarker, R., & Essam, D. (2018). Scenario-Based Solution Approach for Uncertain Resource Constrained Scheduling Problems. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477756

118. Sallam, K., Elsayed, S., Sarker, R., & Essam, D. (2018). Landscape-Based Differential Evolution for Constrained Optimization Problems. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477900

119. Sallam, K. M., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2018). Improved United Multi-Operator Algorithm for Solving Optimization Problems. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477759

120. Liu, C., Zhao, Q., Yan, B., Elsayed, S., & Sarker, R. (2018). An Improved Multi-Objective Evolutionary Approach for Clustering High-Dimensional Data. In Proceedings - 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2018 (pp. 184-190). doi:10.1109/BDCAT.2018.00030

121. Meselhi, M. A., Elsayed, S. M., Essam, D. L., & Sarker, R. A. (2017). Fast differential evolution for big optimization. In International Conference on Software, Knowledge Information, Industrial Management and Applications, SKIMA Vol. 2017-December. doi:10.1109/SKIMA.2017.8294137

122. Meselhi, M. A., Sarker, R. A., Essam, D. L., & Elsayed, S. M. (2018). Enhanced differential grouping for large scale optimization. In IJCCI 2018 - Proceedings of the 10th International Joint Conference on Computational Intelligence Vol. 1 (pp. 217-224). Seville, Spain: SciTePress. doi:10.5220/0006938902170224

123. 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 Vol. 2018-December (pp. 9 pages). Auckland, New Zealand.

124. Sallam, K. M., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2017). Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 1350-1357). San Sebastian, Spain. doi:10.1109/CEC.2017.7969461

125. Sallam, K. M., Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2017). Two-phase differential evolution framework for solving optimization problems. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Athens, Greece. doi:10.1109/SSCI.2016.7850258

126. Aguilar-Justo, A. E., Mezura-Montes, E., Elsayed, S. M., & Sarker, R. A. (2017). Decomposition of large-scale constrained problems using a genetic-based search. In 2016 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2016. Ixtapa, Mexico. doi:10.1109/ROPEC.2016.7830614

127. Elsayed, S., Hamza, N., & Sarker, R. (2016). Testing united multi-operator evolutionary algorithms-II on single objective optimization problems. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 2966-2973). doi:10.1109/CEC.2016.7744164

128. Elsayed, S., Sarker, R., & Coello, C. C. (2016). Enhanced multi-operator differential evolution for constrained optimization. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 4191-4198). doi:10.1109/CEC.2016.7744322

129. Zaman, M. F., Elsayed, S., Ray, T., & Sarker. (2016). A Co-evolutionary approach for optimal bidding strategy of multiple electricity suppliers. In Evolutionary Computation (CEC), 2016 IEEE Congress on. Vancouver, Canada. doi:10.1109/CEC.2016.7744234

130. Zaman, M. F., Elsayed, S., Ray, T., & Sarker, R. (2015). An Evolutionary Approach for Scheduling Solar-thermal Power Generation System. In International Conference on Computers & Industrial Engineering (CIE). Metz, France. doi:10.13140/RG.2.1.1577.5129

131. Sallam., Sarker., Essam., & Elsayed, S. M. (2015). Neurodynamic differential evolution algorithm and solving CEC2015 competition problems. In IEEE Congress on Evolutionary Computation,. Sendai, Japan.

132. Ali, I. M., Elsayed, S. A. B. E. R., Ray, T., & Sarker, R. (2015). Memetic Algorithm for solving Resource Constrained Project Scheduling Problems. In Evolutionary Computation. Sendai: Massachusetts Institute of Technology Press (MIT Press): STM Titles. doi:10.1109/CEC.2015.7257231

133. Elsayed, S. M., Sarker, R., & Slay. (2015). Evaluating the performance of a differential evolution algorithm in anomaly detection. In IEEE Congress on Evolutionary Computation. Sendai, Japan. doi:10.1109/CEC.2015.7257194

134. Elsayed, S. M., Sarker, R. (2015). An Adaptive Configuration of Differential Evolution Algorithms for Big Data. In IEEE Congress on Evolutionary Computation. Sendai, Japan.

135. Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2014). United multi-operator evolutionary algorithms. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1006-1013). doi:10.1109/CEC.2014.6900237

136. Elsayed, S. M., Sarker, R. A., Essam, D. L., & Hamza, N. M. (2014). Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1650-1657). doi:10.1109/CEC.2014.6900308

137. Greenwood, G. W., Elsayed, S., Sarker, R., & Abbass, H. A. (2014). Online generation of trajectories for autonomous vehicles using a multi-agent system. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1218-1224). doi:10.1109/CEC.2014.6900345

138. Elsayed, S. M., Ray, T., & Sarker, R. A. (2014). A surrogate-assisted differential evolution algorithm with dynamic parameters selection for solving expensive optimization problems. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1062-1068). doi:10.1109/CEC.2014.6900351

139. Sayed, E., Essam, D., Sarker, R., & Elsayed, S. (2014). A decomposition-based algorithm for dynamic economic dispatch problems. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1898-1905). doi:10.1109/CEC.2014.6900459

140. Elsayed, S. M., & Sarker, R. A. (2013). Differential Evolution with automatic population injection scheme for constrained problems. In Proceedings of the 2013 IEEE Symposium on Differential Evolution, SDE 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (pp. 112-118). doi:10.1109/SDE.2013.6601450

141. Elsayed., Sarker, R., & Mezura-montes, E. (2013). Particle Swarm Optimizer for Constrained Optimization. In 2013 IEEE Congress on Evolutionary Computation (pp. 1703-1711). Cancún, México. doi:10.1109/CEC.2013.6557896

142. Elsayed., Sarker, R., & Ray, T. (2013). Differential Evolution with Automatic Parameter Configuration for Solving the CEC2013 Competition on Real-Parameter Optimization. In 2013 IEEE Congress on Evolutionary Computation (pp. 1932-1937). Cancún, México. doi:10.1109/CEC.2013.6557795

143. Elsayed., & Sarker, R. (2013). Differential Evolution with Automatic Population Injection Scheme. In 2013 IEEE Symposium on Differential Evolution (SDE 2013) (pp. 1-8). Singapore: IEEE.

144. Elsayed., Sarker, R., & Essam, D. (2013). A Genetic Algorithm for Solving the CEC'2013 Competition Problems on Real-Parameter Optimization. In 2013 IEEE Congress on Evolutionary Computation (pp. 356-360). Cancún, México. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6557591

145. Elsayed., Sarker, R., & Ray, T. (2012). Parameters adaptation in differential evolution. In Evolutionary Computation (CEC), 2012 IEEE Congress on (pp. 1-8). USA: IEEE Press. doi:10.1109/CEC.2012.6252931

146. Elsayed., Sarker, R., & Essam, D. (2012). Memetic multi-topology particle swarm optimizer for constrained optimization. In Evolutionary Computation (CEC), 2012 IEEE Congress on (pp. 1-8). USA: IEEE Press. doi:10.1109/CEC.2012.6256110

147. Elsayed., Sarker, R., & Essam, D. (2011). Improved genetic algorithm for constrained optimization. In Computer Engineering & Systems (ICCES), 2011 International Conference on (pp. 111-115). USA: IEEE Press. doi:10.1109/ICCES.2011.6141022

148. Elsayed., Sarker, R., & Essam, D. (2011). Integrated strategies differential evolution algorithm with a local search for constrained optimization. In 2011 IEEE Congress of Evolutionary Computation (pp. 2618-2625). New Orleans, LA: IEEE. doi:10.1109/CEC.2011.5949945

149. Elsayed., Sarker, R., & Essam, D. (2011). GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In 2011 IEEE Congress of Evolutionary Computation (pp. 1034-1040). New Orleans, LA: IEEE. doi:10.1109/CEC.2011.5949731

150. Elsayed., Sarker, R., & Essam, D. (2011). GA with a new multi-parent crossover for constrained optimization. In 2011 IEEE Congress of Evolutionary Computation (pp. 857-864). New Orleans, LA: IEEE. doi:10.1109/CEC.2011.5949708

151. Elsayed., Sarker, R., & Essam, D. (2011). Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems. In 2011 IEEE Congress of Evolutionary Computation (pp. 1041-1048). New Orleans, LA: IEEE. doi:10.1109/CEC.2011.5949732

152. Hamza, N. M., Elsayed., Essam, D., & Sarker, R. (2011). Differential evolution combined with constraint consensus for constrained optimization. In 2011 IEEE Congress of Evolutionary Computation (pp. 865-872). New Orleans, LA: IEEE. doi:10.1109/CEC.2011.5949709

153. Elsayed, S., Abd EL-Wahed, W., Ismail, N.: A Hybrid Genetic Scatter Search Algorithm for Solving Optimisation Problems. In: the 6th International Conference on Informatics and Systems, Cairo, Egypt, pp. DS-12: DS1-7, 2008.

154. Elsayed, S., Abd EL-Wahed, W., Ismail, N.: Parallel Scatter Search Algorithm for Solving Optimisation Problems. In:  the 17th International Conference on Computer Theory and Applications (ICCTA’2007), Alexandria, Egypt, September 2007.