Projects
Competition on Dynamic Multimodal Optimization (at CEC'2022)
Project: Evolutionary Framework for High Dimensional Problems, ARC DP (2021-2024)
The project aims to develop a novel framework for solving high dimensional decision problems with and without changes. This research is driven by the fact, that there is a huge gap between current research and the methodology needed to solve practical decision problems. In the proposed framework, a number of algorithms will be developed and integrated to generate robust solutions for those problems. The intended scientific outcomes include a novel framework with new techniques, developed by exploiting the impractical assumptions of existing methodologies. Practical outcomes include a robust decision-making tool and strong research training. The developed tool will provide significant cost savings through better decision making in practice.
Selected Publications:
M. Meselhi, S. Elsayed, R. Sarker and D. Essam (2021) A Decomposition Approach for Large-scale Non-separable Optimization Problems, Applied Soft Computing, DOI: 10.1016/j.asoc.2021.108168
M. Meselhi, S. Elsayed, R. Sarker and D. Essam (2021) Parallel Evolutionary Algorithm for EEG Optimization Problems, 2021 IEEE Congress on Evolutionary Computation (CEC), DOI: 10.1109/CEC45853.2021.9504925
Firoz Mahmud, F. Zaman, A. Ahrari, R. Sarker and D. Essam (2021) Genetic Algorithm for Singular Resource Constrained Project Scheduling Problems, IEEE Access, DOI: 10.1109/ACCESS.2021.3114702
Project: Robust Evolutionary Analytics for Changing and Uncertain Environments, ARC DP (2019-2022)
The project aims to develop a novel framework for solving planning problems under changing environments with uncertainties. This research is driven by the fact, that there is a huge gap between current research and the methodology needed to solve practical planning problems. In the proposed framework, three algorithms will be developed and integrated to generate robust solutions for planning under dynamic changes with uncertainties. The intended scientific outcomes include a novel framework with new techniques, developed by exploiting the assumptions of existing methodologies. Practical outcomes will include a robust planning tool, strong research training, and high impact publications.
Selected Publications:
Ali Ahrari, S. Elsayed, R. Sarker, D. Essam and C. Coello Coello (2021) Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations, IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2021.3117116
Ahrari A; Elsayed S; Sarker R; Essam D; Coello Coello CA, 2021, 'Adaptive Multilevel Prediction Method for Dynamic Multimodal Optimization', IEEE Transactions on Evolutionary Computation, vol. 25, pp. 463 - 477, http://dx.doi.org/10.1109/TEVC.2021.3051172
Zaman F; Elsayed S; Sarker R; Essam D; Coello CAC, 2021, 'Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions', IEEE Transactions on Cybernetics, vol. PP, http://dx.doi.org/10.1109/TCYB.2021.3097312
Ahrari A; Elsayed S; Sarker R; Essam D; Coello CAC, 2021, 'A heredity-based adaptive variation operator for reinitialization in dynamic multi-objective problems', Applied Soft Computing, vol. 101, http://dx.doi.org/10.1016/j.asoc.2020.107027
Ahrari A; Elsayed S; Sarker R; Essam D; Coello CAC, 2021, 'A Novel Parametric Benchmark Generator for Dynamic Multimodal Optimization', Swarm and Evolutionary Computation, pp. 100924 - 100924, http://dx.doi.org/10.1016/j.swevo.2021.100924
Zaman F; Elsayed S; Sarker R; Essam D; Coello Coello CA, 2021, 'An evolutionary approach for resource constrained project scheduling with uncertain changes', Computers and Operations Research, vol. 125, http://dx.doi.org/10.1016/j.cor.2020.105104
Akl AM; Sarker RA; Essam DL, 2019, 'Adaptive simulation budget allocation in Simulation assisted Differential Evolution algorithm', Applied Soft Computing Journal, vol. 83, http://dx.doi.org/10.1016/j.asoc.2019.105678
Li K; Elsayed SM; Sarker R; Essam D, 2020, 'Landscape-Based Similarity Check Strategy for Dynamic Optimization Problems', IEEE ACCESS, vol. 8, pp. 178570 - 178586, http://dx.doi.org/10.1109/ACCESS.2020.3026339
Rana MJ; Zaman F; Ray T; Sarker R, 2020, 'Heuristic enhanced evolutionary algorithm for community microgrid scheduling', IEEE Access, vol. 8, pp. 76500 - 76515, http://dx.doi.org/10.1109/ACCESS.2020.2989795
Meselhi, MA; Elsayed, SM; Sarker, RA; Essam, DL, 2020, Contribution Based Co-evolutionary Algorithm for Large-scale Optimization Problems, IEEE Access, https://ieeexplore.ieee.org/document/9250444
Mahmud F; Zaman F; Sarker R; Essam D, 2020, 'Memetic Algorithm for Heterogeneous Project Scheduling Problems', in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, IEEE, pp. 2778 - 2785, presented at 2020 SSCI, 01 - 04 December 2020, http://dx.doi.org/10.1109/SSCI47803.2020.9308318
Ahrari A; Elsayed S; Sarker R; Essam D; Coello CAC, 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, IEEE, pp. 1387 - 1394, presented at 2020 SSCI, 01 December 2020 - 04 December 2020, http://dx.doi.org/10.1109/SSCI47803.2020.9308464
A. Ahrari, S. Elsayed, R. Sarker, D. Essam and C. Coello Coello (2021) Modular Analysis and Development of a Genetic Algorithm with Standardized Representation for Resource-Constrained Project Scheduling, 2021 IEEE Congress on Evolutionary Computation (CEC), DOI: 10.1109/CEC45853.2021.9504950
Ahrari, A. and Essam, D. (2022) An Introduction to Evolutionary and Memetic Algorithms for Parameter Optimization, In Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling, Springer Book Series on Adaptation, Learning, and Optimization , Springer Nature Switzerland, pp. 37-63.
Projects: Intelligent Algorithms for Portfolio Selection in Future Force Design, DST Contract Research (2019-2021) and Development of Portfolio Optimisation Heuristics (1921-2022)
Selected Publications:
Harrison KR; Elsayed S; Garanovich IL; Weir T; Galister M; Boswell S; Taylor R; Sarker R, 2021, 'A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design', IEEE Access, vol. 9, pp. 83410 - 83430, http://dx.doi.org/10.1109/ACCESS.2021.3086070
Harrison KR; Elsayed S; Garanovich I; Weir T; Galister M; Boswell S; Taylor R; Sarker R, 2020, 'Portfolio Optimization for Defence Applications', IEEE Access, vol. 8, pp. 60152 - 60178, http://dx.doi.org/10.1109/ACCESS.2020.2983141
Harrison KR; Elsayed S; Garanovich I; Weir T; Galister M; Boswell S; Taylor R; Sarker R, 2020, Multi-Period Project Selection and Scheduling for Defence Capability-Based Planning, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, Canada, DOI: 10.1109/SMC42975.2020.9283334
Harrison KR; Elsayed S; Garanovich I; Weir T; Galister M; Boswell S; Taylor R; Sarker R, 2020, An Exploration of Meta-Heuristic Approaches for the Project Portfolio Selection and Scheduling Problem in a Defence Context, 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, DOI: 10.1109/SSCI47803.2020.9308608
K. Harrison, S. Elsayed, I. Garanovich, T. Weir, S. Boswell and R. Sarker (2021) Project Portfolio Selection with Defense Capability Options, GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion, July 2021, pp.1825–1826, DOI: https://doi.org/10.1145/3449726.3463126
K.R. Harrison, I.L. Garanovich, T. Weir, S.G. Boswell, S.M. Elsayed, and R.A. Sarker (2022) Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction, In Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling, Springer Nature Switzerland, Chapter 1, pp.1-8
K.R. Harrison, S.M. Elsayed, I.L. Garanovich, T. Weir, S.G. Boswell, and R.A. Sarker (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, Springer Nature Switzerland, Chapter 5, pp.91-126
K.R. Harrison, I.L. Garanovich, T. Weir, S.G. Boswell, S.M. Elsayed, and R.A. Sarker (editors) Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling, Springer Book Series on Adaptation, Learning, and Optimization , Springer Nature Switzerland, 2022, 224 pages.
Project: Reactive Planning under Disruptions and Dynamic Changes, ARC DP (2017-2019).
This project aims to develop an algorithmic framework for reactive planning under unknown disturbances and dynamic changes. There is a huge gap between current research and the methodology needed to solve practical planning problems. The project will develop and integrate algorithms to ensure robust solutions for planning and re-planning under disruptions and dynamic changes. This project expects to develop an effective approach for solving complex decision problems, expand Australia’s knowledge base and research capability, and make it a leader in saving costs through better decision making.
Selected Publications:
· Ahrari A; Elsayed S; Sarker R; Essam D; Coello Coello CA, 2021, 'Weighted pointwise prediction method for dynamic multiobjective optimization', Information Sciences, vol. 546, pp. 349 - 367, http://dx.doi.org/10.1016/j.ins.2020.08.015
· Zaman F; Elsayed S; Sarker R; Essam D, 2020, 'Hybrid evolutionary algorithm for large-scale project scheduling problems', Computers and Industrial Engineering, vol. 146, http://dx.doi.org/10.1016/j.cie.2020.106567
· Elsayed S; Sarker R; Essam D; Coello Coello CA, 2020, 'Evolutionary approach for large-Scale mine scheduling', Information Sciences, vol. 523, pp. 77 - 90, http://dx.doi.org/10.1016/j.ins.2020.02.074
· Zaman F; Elsayed SM; Saker R; Essam D, 2020, 'Resource Constrained Project Scheduling with Dynamic Disruption Recovery', IEEE Access, vol. 8, pp. 144866 - 144879, http://dx.doi.org/10.1109/ACCESS.2020.3014940
· Hamza N; Sarker R; Essam D, 2020, 'Sensitivity-Based Change Detection for Dynamic Constrained Optimization', IEEE Access, vol. 8, pp. 103900 - 103912, http://dx.doi.org/10.1109/ACCESS.2020.2999161
· Paul SK; Sarker R; Essam D; Lee PTW, 2019, 'A mathematical modelling approach for managing sudden disturbances in a three-tier manufacturing supply chain', Annals of Operations Research, vol. 280, pp. 299 - 335, http://dx.doi.org/10.1007/s10479-019-03251-w
· Zaman F; Elsayed S; Sarker R; Essam D; Coello CAC, 2019, 'Multi-Method based algorithm for multi-objective problems under uncertainty', Information Sciences, vol. 481, pp. 81 - 109, http://dx.doi.org/10.1016/j.ins.2018.12.072
· Rahman HF; Sarker R; Essam D, 2018, 'Multiple-order permutation flow shop scheduling under process interruptions', International Journal of Advanced Manufacturing Technology, vol. 97, pp. 2781 - 2808, http://dx.doi.org/10.1007/s00170-018-2146-z
· Chakrabortty RK; Sarker RA; Essam DL, 2018, 'Single mode resource constrained project scheduling with unreliable resources', Operational Research, pp. 1 - 35, http://dx.doi.org/10.1007/s12351-018-0380-7
· Mahmud F; Zaman F; Sarker R; Essam D, 2020, 'Heuristic Embedded Genetic Algorithm for Heterogeneous Project Scheduling Problems', in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, http://dx.doi.org/10.1109/CEC48606.2020.9185712
· 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, IEEE Xplore, pp. 2268 - 2275, http://dx.doi.org/10.1109/CEC.2019.8790215
· 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, http://dx.doi.org/10.1109/CEC.2019.8790170
· 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, http://dx.doi.org/10.1109/CEC.2018.8477756
· Zaman F; Sarker R; Chang G, 2017, 'Dynamic scenario-based solution approach for scheduling solar-thermal generators', in 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, Beijing, China, pp. 492 - 497, http://dx.doi.org/10.1109/CIAPP.2017.8167266
· Akl AM; Sarker RA; Essam DL, 2017, 'Simulation optimization approach for solving stochastic programming', in 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, Beijing, China, pp. 161 - 165, http://dx.doi.org/10.1109/CIAPP.2017.8167200
Project: Robust Configuration of Evolutionary Algorithms, ARC DP (2015-2017)
The purpose of this project is to develop an intelligent framework for the robust configuration of evolutionary algorithms. This research is driven by the fact that the current design of evolutionary algorithms is sub-optimal and ineffective for many problem domains. In the proposed framework, a configuration is evolved while the algorithm is running for problem solving to ensure robust design. Its scientific outcomes are expected to include a novel framework for the automated design of algorithms and new techniques for exploiting assumptions in algorithmic design that may have been overlooked. Expected practical outcomes include the provision of a robust problem-solving tool, strong research training and high-impact publications.
Selected Publications:
· Elsayed S; Sarker R; Coello Coello CA, 2017, 'Sequence-Based Deterministic Initialization for Evolutionary Algorithms', IEEE Transactions on Cybernetics, vol. 47, pp. 2911 - 2923, http://dx.doi.org/10.1109/TCYB.2016.2630722
· Zaman F; Elsayed SM; Ray T; Sarker RA, 2018, 'Evolutionary Algorithms for Finding Nash Equilibria in Electricity Markets', IEEE Transactions on Evolutionary Computation, vol. 22, pp. 536 - 549, http://dx.doi.org/10.1109/TEVC.2017.2742502
· Elsayed S; Sarker R; Coello Coello CA, 2017, 'Fuzzy Rule-Based Design of Evolutionary Algorithm for Optimization', IEEE Transactions on Cybernetics, vol. 49, pp. 301 - 314, http://dx.doi.org/10.1109/TCYB.2017.2772849
· Elsayed S; Sarker R; Coello CC; Ray T, 2018, 'Adaptation of operators and continuous control parameters in differential evolution for constrained optimization', Soft Computing, 22, pp. 6595 - 6616, http://dx.doi.org/10.1007/s00500-017-2712-6
· Elsayed S; Sarker R; Ray T; Coello CC, 2017, 'Consolidated optimization algorithm for resource-constrained project scheduling problems', Information Sciences, vol. 418-419, pp. 346 - 362, http://dx.doi.org/10.1016/j.ins.2017.08.023
· Zaman F; Elsayed SM; Ray T; Sarker RA, 2017, 'Co-evolutionary approach for strategic bidding in competitive electricity markets', Applied Soft Computing Journal, vol. 51, pp. 1 - 22, http://dx.doi.org/10.1016/j.asoc.2016.11.049
· Elsayed S; Sarker, R, 2015, 'An Adaptive Configuration of Differential Evolution Algorithms for Big Data', IEEE Congress on Evolutionary Computation, Sendai, Japan, presented at IEEE Congress on Evolutionary Computation, Sendai, Japan, http://dx.doi.org/10.1109/CEC.2015.7256958
· Zaman MF; Elsayed SABER; Ray ; Sarker , 2016, 'An Evolutionary Framework for Bi-objective Dynamic Economic and Environmental Dispatch Problems', in Leu ; Singh ; Elsayed (eds.), INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016, Springer, pp. 495 - 508, http://dx.doi.org/10.1007/978-3-319-49049-6_36
· Elsayed S; Zaman M; Sarker , 2015, 'Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems', in Lavangnananda K; PhonAmnuaisuk S; Engchuan W; Chan JH (ed.), Intelligent and Evolutionary Systems The 19th Asia Pacific Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings, edn. Proceedings in Adaptation Learning and Optimization, Springer, pp. 357 - 372, http://dx.doi.org/10.1007/978-3-319-27000-5_29
· Elsayed SM; Sarker R, 2016, 'Dynamic configuration of differential evolution control parameters and operators', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 78 - 88, http://dx.doi.org/10.1007/978-3-319-28270-1_7
· Debie E; Elsayed SM; Essam DL; Sarker RA, 2016, 'Investigating multi-operator differential evolution for feature selection', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 273 - 284, http://dx.doi.org/10.1007/978-3-319-28270-1_23
· Elsayed S; Sarker R; Coello CC, 2016, 'Enhanced multi-operator differential evolution for constrained optimization', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 4191 - 4198, http://dx.doi.org/10.1109/CEC.2016.7744322
· 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, http://dx.doi.org/10.1109/CEC.2016.7744164