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Your search keyword '"Kheiri, Ahmed"' showing total 110 results
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110 results on '"Kheiri, Ahmed"'

1. Exploring the Optimal Camera Placement Problem and Its Relationship with the Set Covering Problem

Author

Almousa, Malek, Ehrgott, Matthias, Kheiri, Ahmed, Barbosa-Povoa, Ana Paula, Editorial Board Member, de Almeida, Adiel Teixeira, Editorial Board Member, Gans, Noah, Editorial Board Member, Gupta, Jatinder N. D., Editorial Board Member, Heim, Gregory R., Editorial Board Member, Hua, Guowei, Editorial Board Member, Kimms, Alf, Editorial Board Member, Li, Xiang, Editorial Board Member, Masri, Hatem, Editorial Board Member, Nickel, Stefan, Editorial Board Member, Qiu, Robin, Editorial Board Member, Shankar, Ravi, Editorial Board Member, Slowiński, Roman, Editorial Board Member, Tang, Christopher S., Editorial Board Member, Wu, Yuzhe, Editorial Board Member, Zhu, Joe, Editorial Board Member, Zopounidis, Constantin, Editorial Board Member, Emrouznejad, Ali, editor, Zervopoulos, Panagiotis D., editor, Ozturk, Ilhan, editor, Jamali, Dima, editor, and Rice, John, editor

Published
2024
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4. Metaheuristics 'In the Large'

Author

Swan, Jerry, Adriaensen, Steven, Brownlee, Alexander E. I., Hammond, Kevin, Johnson, Colin G., Kheiri, Ahmed, Krawiec, Faustyna, Merelo, J. J., Minku, Leandro L., Özcan, Ender, Pappa, Gisele L., García-Sánchez, Pablo, Sörensen, Kenneth, Voß, Stefan, Wagner, Markus, and White, David R.

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, 68W99
Abstract

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. We argue that, via principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.

Published
2020
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7. Metaheuristics “In the Large”

Author

Swan, Jerry, Adriaensen, Steven, Brownlee, Alexander E.I., Hammond, Kevin, Johnson, Colin G., Kheiri, Ahmed, Krawiec, Faustyna, Merelo, J.J., Minku, Leandro L., Özcan, Ender, Pappa, Gisele L., García-Sánchez, Pablo, Sörensen, Kenneth, Voß, Stefan, Wagner, Markus, and White, David R.

Published
2022
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8. Combining Monte-Carlo and Hyper-heuristic methods for the Multi-mode Resource-constrained Multi-project Scheduling Problem

Author

Asta, Shahriar, Karapetyan, Daniel, Kheiri, Ahmed, Özcan, Ender, and Parkes, Andrew J.

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of "hidden" instances, i.e. instances not available at the algorithm design phase.

Published
2015

13. Multi-stage hyper-heuristics for optimisation problems

Author

Kheiri, Ahmed

Subjects
006.3, QA 75 Electronic computers. Computer science, T Technology (General)
Abstract

There is a growing interest towards self configuring/tuning automated general-purpose reusable heuristic approaches for combinatorial optimisation, such as, hyper-heuristics. Hyper-heuristics are search methodologies which explore the space of heuristics rather than the solutions to solve a broad range of hard computational problems without requiring any expert intervention. There are two common types of hyper-heuristics in the literature: selection and generation methodologies. This work focuses on the former type of hyper-heuristics. Almost all selection hyper-heuristics perform a single point based iterative search over the space of heuristics by selecting and applying a suitable heuristic to the solution in hand at each decision point. Then the newly generated solution is either accepted or rejected using an acceptance method. This improvement process is repeated starting from an initial solution until a set of termination criteria is satisfied. The number of studies on the design of hyper-heuristic methodologies has been rapidly increasing and currently, we already have a variety of approaches, each with their own strengths and weaknesses. It has been observed that different hyper-heuristics perform differently on a given subset of problem instances and more importantly, a hyper-heuristic performs differently as the set of low level heuristics vary. This thesis introduces a general "multi-stage" hyper-heuristic framework enabling the use and exploitation of multiple selection hyper-heuristics at different stages during the search process. The goal is designing an approach utilising multiple hyper-heuristics for a more effective and efficient overall performance when compared to the performance of each constituent selection hyper-heuristic. The level of generality that a hyper-heuristic can achieve has always been of interest to the hyper-heuristic researchers. Hence, a variety of multi-stage hyper-heuristics based on the framework are not only applied to the real-world combinatorial optimisation problems of high school timetabling, multi-mode resource-constrained multi-project scheduling and construction of magic squares, but also tested on the well known hyper-heuristic benchmark of CHeSC 2011. The empirical results show that the multi-stage hyper-heuristics designed based on the proposed framework are still inherently general, easy-to-implement, adaptive and reusable. They can be extremely effective solvers considering their success in the competitions of ITC 2011 and MISTA 2013. Moreover, a particular multi-stage hyper-heuristic outperformed the state-of-the-art selection hyper-heuristic from CHeSC 2011.

Published
2014

14. Evolutionary computation for wind farm layout optimization

Author

Wilson, Dennis, Rodrigues, Silvio, Segura, Carlos, Loshchilov, Ilya, Hutter, Frank, Buenfil, Guillermo López, Kheiri, Ahmed, Keedwell, Ed, Ocampo-Pineda, Mario, Özcan, Ender, Peña, Sergio Ivvan Valdez, Goldman, Brian, Rionda, Salvador Botello, Hernández-Aguirre, Arturo, Veeramachaneni, Kalyan, and Cussat-Blanc, Sylvain

Published
2018
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18. Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Author

Almutairi, Alhanof, Özcan, Ender, Kheiri, Ahmed, Jackson, Warren G., Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Czachórski, Tadeusz, editor, Gelenbe, Erol, editor, Grochla, Krzysztof, editor, and Lent, Ricardo, editor

Published
2016
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19. Ensemble Move Acceptance in Selection Hyper-heuristics

Author

Kheiri, Ahmed, Mısır, Mustafa, Özcan, Ender, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Czachórski, Tadeusz, editor, Gelenbe, Erol, editor, Grochla, Krzysztof, editor, and Lent, Ricardo, editor

Published
2016
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24. A Two Stage Approach for High School Timetabling

Author

Shambour, Moh’d Khaled Yousef, Khader, Ahamad Tajudin, Kheiri, Ahmed, Özcan, Ender, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lee, Minho, editor, Hirose, Akira, editor, Hou, Zeng-Guang, editor, and Kil, Rhee Man, editor

Published
2013
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25. A Hyper-heuristic with a Round Robin Neighbourhood Selection

Author

Kheiri, Ahmed, Özcan, Ender, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Middendorf, Martin, editor, and Blum, Christian, editor

Published
2013
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33. Exact and hyper?heuristic solutions for the distribution?installation problem from the VeRoLog 2019 challenge

Author

Kheiri, Ahmed, Ahmed, Leena, Boyac?, Burak, Gromicho, Joaquim, and Mumford, Christine

Subjects
Computer Networks and Communications, Hardware and Architecture, Software, Information Systems
Abstract

This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper?heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real?world size revealed the success of the hyper?heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper?heuristic framework.

Published
2020

43. Constructing operating theatre schedules using partitioned graph colouring techniques

Author

Kheiri, Ahmed, Lewis, Rhyd, Thompson, Jonathan, and Harper, Paul

Abstract

ABSTRACTIn hospitals, scheduled operations can often be cancelled in large numbers due to the unavailability of beds for post-operation recovery. Operating theatre scheduling is known to be an -hard optimisation problem. Previous studies have shown that the correct scheduling of surgical procedures can have a positive impact on the availability of beds in hospital wards, thereby allowing a reduction in number of elective operation cancellations. This study proposes an exact technique based on the partitioned graph colouring problem for constructing optimal master surgery schedules, with the goal of minimising the number of cancellations. The resultant schedules are then simulated in order to measure how well they cope with the stochastic nature of patient arrivals. Our results show that the utilisation of post-operative beds can be increased, whilst the number of cancellations can be decreased, which may ultimately lead to greater patient throughput and reduced waiting times. A scenario-based model has also been employed to integrate the stochastic-nature associated with the bed requirements into the optimisation process. The results indicate that the proposed model can lead to more robust solutions.

Published
2021
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46. Summary of evolutionary computation for wind farm layout optimization

Author

Wilson, Dennis G, primary, Rodrigues, Silvio, additional, Segura, Carlos, additional, Loshchilov, Ilya, additional, Hutter, Frank, additional, Buenfil, Guillermo López, additional, Kheiri, Ahmed, additional, Keedwell, Ed, additional, Ocampo-Pineda, Mario, additional, Özcan, Ender, additional, Pea, Sergio Iwan Valdez, additional, Goldman, Brian, additional, Rionda, Salvador Botello, additional, Hernndez-Aguirre, Arturo, additional, Veeramachaneni, Kalyan, additional, and Cussat-Blanc, Sylvain, additional

Published
2018
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48. Constructing constrained-version of magic squares using selection hyper-heuristics

Author

Kheiri, Ahmed

Subjects
late acceptance, Computational design, Heuristic solutions, Magic square, Hyper-heuristics, Computational problem, Hyperheuristic, Square matrices, Heuristic methods, Number theory
Abstract

A square matrix of distinct numbers in which every row, column and both diagonals have the same total is referred to as a magic square. Constructing a magic square of a given order is considered a difficult computational problem, particularly when additional constraints are imposed. Hyper-heuristics are emerging high-level search methodologies that explore the space of heuristics for solving a given problem. In this study, we present a range of effective selection hyper-heuristics mixing perturbative low-level heuristics for constructing the constrained version of magic squares. The results show that selection hyper-heuristics, even the non-learning ones deliver an outstanding performance, beating the best-known heuristic solution on average.

Published
2014

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