Publications

Refereed Journal Articles

1. 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, (Q1, IF:3.557, SNIP: 1.758)

2. Ahrari, A., Elsayed, S., Sarker, R., Essam, D. and Coello Coello, C.: Adaptive Multilevel Prediction Method for Dynamic Multimodal Optimization. IEEE Transactions on Evolutionary Computation, in press, 2021 (Q1, IF: 11.169, SNIP: 5.365)

3. 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 (Q1, IF: 5.472, SNIP: 2.52)

4. Elsayed, S., Sarker, R., Essam, D., Coello Coello, C.: Evolutionary Approach for Large-Scale Mine Scheduling. Information Sciences, vol. 523, pp. 77- 90, 2020 (Q1, IF: 5.910, SNIP: 2.688)

5. Meselhi, M., Elsayed, S., Sarker, R., Essam, D.:" Contribution Based Co-evolutionary Algorithm for Large Problems," IEEE Access, vol 8, pp. 203369 - 203381, 2020 (Q1, IF:3.557, SNIP: 1.758)

6. El-Fiqi, H., Campbell, B., Elsayed, S., Perry, A., Singh, H., Hunjet, R., Abbass. H.: The Limits of Reactive Shepherding Approaches for Swarm Guidance," IEEE Access, in press, 2020 (Q1, IF:3.557, SNIP: 1.758)

7. 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 (Q1, IF: 5.910, SNIP: 2.688)

8. Sallam, K; Elsayed S.; Chakrabortty, R.; Ryan, M.: Evolutionary Framework with Reinforcement Learning-based Mutation Adaptation, IEEE Access, vol. 8, pp.194045-194071, 2020 (Q1, IF:3.557, SNIP: 1.758)

9. 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 (Q1, IF:3.557, SNIP: 1.758)

10. 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, in press, 2020. (Q1, IF:3.424, SNIP: 2.289)

11. Zaman, F; Elsayed, S.; Sarker, R; Essam, D.: Resource Constrained Project Scheduling with Dynamic Disruption Recovery, IEEE Access, vol. 146, 2020. (Q1, IF:3.557, SNIP: 1.758)

12. Zaman, F; Elsayed, S.; Sarker, R; Essam, D.: Hybrid evolutionary algorithm for large-scale project scheduling problems, Computers and Industrial Engineering, vol. 146, 2020. (Q1, IF: 3.518, SNIP: 1.755)

13. 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 (Q1, IF:3.557, SNIP: 1.758)

14. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Landscape-Assisted Multi-Operator Differential Evolution for Solving Constrained Optimization Problems. Expert Systems With Applications, 2019, in press (Q1, IF:4.29, SNIP: 2.696)

15. 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 (Q1, IF: 5.910, SNIP: 2.688)

16. 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. [ Download for Free] (Q1, IF:3.557, SNIP: 1.758)

17. 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. (Q1, IF: 5.910, SNIP: 2.688)

18. 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 (Q1, IF: 11.169, SNIP: 5.365)

19. 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 (Q1, IF:7.384, SNIP: 2.827)

20. Elsayed, S., Sarker, R., Coello Coello, C.: Sequence-based Deterministic Initialization for Evolutionary Algorithms. IEEE Transactions on Cybernetics, 47(9), 2911-2923 (2017) (Q1, IF: 11.079, SNIP: 3.786)

21. 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. (Q1, IF: 11.169, SNIP: 5.365)

22. Elsayed, S., Sarker, R., Ray, T., Coello Coello, C.: Consolidated Optimization Algorithm for Resource-constrained Project Scheduling Problems. Information Sciences, 418, 346-368 (2017) (Q1, IF: 5.910, SNIP: 2.688)

23. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Landscape-Based Adaptive Operator Selection Mechanism for Differential Evolution. Information Sciences, 2017, 418, 383-404 (Q1, IF: 5.910, SNIP: 2.688)

24. Elsayed, S., Sarker, R., Coello Coello, C., Ray, T.: Adaptation of Operators and Continuous Control Parameters in Differential Evolution for Constrained Optimization. Soft Computing, 2017, in press. (Q2, IF:2.472, SNIP: 1.204)

25. 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. (Q1, IF:3.541, SNIP: 2.037)

26. 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). (Q1, IF:3.541, SNIP: 2.037)

27. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Power Generation Planning with Uncertain Renewable Energy. Energy, 112, 408-419 (2016). (Q1, IF:4.52, SNIP: 1.798)

28. 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). (Q1, IF:2.894, SNIP: 1.940)

29. Elsayed, S., Sarker, R.: Differential evolution framework for big data optimization. Memetic Computing, 8 (1), 17-33, (2016). (Q2, IF:2.205, SNIP: 1.336)

30. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Dynamic Economic Dispatch Problems. IEEE Transactions on Power Systems, 1-10 (2015). (Q1, IF:5.68, SNIP: 3.624)

31. ayed, E., Essam, D., Sarker, R., Elsayed, S.: Decomposition-based evolutionary algorithm for large scale constrained problems. Information Sciences, 316, 457-486 (2015). (Q1, IF: 5.910, SNIP: 2.688)

32. Mabrok, M.A., Elsayed, S., Ryan, M.J.: Mathematical framework for recursive model-based system design. Nonlinear Dynamics, 84, 223–236 (2016). (Q1, IF:3.464, SNIP: 1.523)

33. 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). (Q1, IF:3.541, SNIP: 2.037)

34. 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) (SNIP: 0.350).

35. 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). (Q1, IF: 11.169, SNIP: 5.365)

36. Elsayed, S., Sarker, R., Mezura-Montes, E.: Self-adaptive mix of particle swarm methodologies for constrained optimization. Information Sciences, 27, 216-233 (2014). (Q1, IF: 5.910, SNIP: 2.688)

37. 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). (Q2, IF:1.380, SNIP: 1.149)

38. Sarker, R., Elsayed, S.: Evolutionary algorithm for analyzing higher degree research student recruitment and completion. Cogent Engineering, 2(1), 1063760 (2015) (SNIP: 0.507).

39. 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). (Q1, IF:1.738, SNIP: 1.203)

40. 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). (Q1, IF:2.894, SNIP: 1.940)

41. Elsayed, S., Sarker, R., Essam, D.L.: Adaptive Configuration of evolutionary algorithms for constrained optimization. Applied Mathematics and Computation, 222, 680-711 (2013). (Q1, IF:1.738, SNIP: 1.203)

42. 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). (Q1, IF:1.52, SNIP: 1.323)

43. 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). (Q1, IF:6.764, SNIP: 3.339)

44. Elsayed, S., Sarker, R., Essam, D.: On an Evolutionary Approach for Constrained Optimization Problem Solving. Applied Soft Computing, 12(10), 3208-3227 (2012). (Q1, IF:3.541, SNIP: 2.037)

45. 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). (Q1, IF:2.600, SNIP: 2.151)

Edited Books

46. 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

Scholarly book chapters

47. 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.

48. 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.

49. 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)

50. 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)

51. 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)

52. 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)

53. 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)

54. 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)

55. 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)

56. 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)

57. 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

58. 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. IEEE Symposium Series on Computational Intelligence, Canberra, Australia, in press, 2020.

59. 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. IEEE Symposium Series on Computational Intelligence, Canberra, Australia, in press, 2020.

60. 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. IEEE Symposium Series on Computational Intelligence, Canberra, Australia, in press, 2020.

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. 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

68. 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

69. 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.

70. 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

71. 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, in press

72. 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

73. 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

74. 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

75. 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

76. 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

77. 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.

78. 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.

79. 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.

80. 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.

81. 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.

82. 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.

83. Elsayed, S., Sarker, R.: An Adaptive Configuration of Differential Evolution Algorithms for Big Data. In: IEEE Congress on Evolutionary Computation 2015, pp. 695-702.

84. 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.

85. 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.

86. 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.

87. 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.

88. 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.

89. 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.

90. Elsayed, S., Sarker, R., Essam, D.L.: United multi-operator evolutionary algorithms. In: IEEE Congress on Evolutionary Computation 2014, pp. 1006-1013.

91. 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.

92. 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.

93. 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.

94. 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.

95. 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.

96. Elsayed, S., Sarker, R., Ray, T.: Parameters Adaptation in Differential Evolution. In: IEEE Congress on Evolutionary Computation, Brisbane 2012, pp. 1- 8.

97. 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.

98. 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.

99. 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.

100. 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.

101. 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.

102. 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.

103. 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.

104. 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.

105. 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.