M. Ayob - Academia.edu (original) (raw)
Papers by M. Ayob
According to Tucker (1982), in a span of 25 years after the second world war, various technologic... more According to Tucker (1982), in a span of 25 years after the second world war, various technological media have been used in teaching and learning at institutions of higher learning such as television, video tapes, slides, audio tapes, including computers. At the end of 1980s until ...
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimiz... more ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimization problems such as unconstrained binary quadratic programming problem (UBQP). This because using more than two parents has increased the intensification process by exploiting the information shared by multi parents. However not all type of crossovers are suitable to solve vehicle routing problem (VRP). Therefore, this work introduces a multi parent insertion crossover in solving vehicle routing problem with time windows (VRPTW) by enhancing two parent insertion crossovers. This crossover exchange information among three parents instead of two. Result tested on Solomon VRPTW benchmarks demonstrate that multi parent crossover outperformed two parent crossover on same instances. This prove the effectiveness of having more parents for crossover that can be help the search to find better quality solution.
Asian Journal of Applied Sciences, 2014
2010 10th International Conference on Intelligent Systems Design and Applications, 2010
... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha... more ... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling. Proceeding in Journal of Scheduling, Volume 9, Number 5 / October, 2006, Springer Netherlands, pp. ...
2010 International Symposium on Information Technology, 2010
... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among the... more ... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among these approaches, Turabieh and Abdullah [24] (Ll), which applied Tabu Based Memetic have outperformed many other approaches in the literature (with regards to Socha benchmark datasets). ...
Procedia - Social and Behavioral Sciences, 2011
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically ... more ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically explore the neighborhood of current solution using a set of predefined neighborhood structures. Since different problem instances have different landscape and complexity, the choice of which neighborhood structure to be applied is a challenging task. Different neighborhood structures may lead to different solution space. Therefore, this work proposes a learning mechanism in a Variable Neighborhood Search (VNS), refer to hereafter as a Variable Neighborhood Guided Search (VNGS). Its effectiveness is illustrated by solving a course timetabling problems. The learning mechanism memorizes which neighborhood structure could effectively solve a specific soft constraint violations and used it to guide the selection of neighborhood structure to enhance the quality of a best solution. The performance of the VNGS is tested over Socha course timetabling dataset. Results demonstrate that the performance of the VNGS is comparable with the results of the other VNS variants and outperformed others in some instances. This demonstrates the effectiveness of applying a learning mechanism in a VNS algorithm.
2011 3rd Conference on Data Mining and Optimization (DMO), 2011
... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 da... more ... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 dataset. ... [11] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling, proceeding in Journal of Scheduling, Volume 9 ...
2009 2nd Conference on Data Mining and Optimization, 2009
Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to inc... more Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to increase the level of generality in which optimization methodologies can operate. In this work, we propose a scatter search based hyper-heuristic (SS-HH) approach for solving examination timetabling problems. The scatter search operates at high level of abstraction which intelligently evolves a sequence of low level heuristics to use for a given problem. Each low level heuristic represents a single neighborhood structure. We test our proposed approach on the un-capacitated Carter benchmarks datasets. Experimental results show the proposed SS-HH is capable of producing good quality solutions which are comparable to other hyper-heuristics approaches (with regarding to Carter benchmark datasets).
2012 IEEE International Conference on Power and Energy (PECon), 2012
ABSTRACT
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Information Sciences, 2013
IEEE Transactions on Evolutionary Computation, 2000
ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of pro... more ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g. different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems; exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state of the art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies which have reported best known results for some instances in both problem domains.
According to Tucker (1982), in a span of 25 years after the second world war, various technologic... more According to Tucker (1982), in a span of 25 years after the second world war, various technological media have been used in teaching and learning at institutions of higher learning such as television, video tapes, slides, audio tapes, including computers. At the end of 1980s until ...
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimiz... more ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimization problems such as unconstrained binary quadratic programming problem (UBQP). This because using more than two parents has increased the intensification process by exploiting the information shared by multi parents. However not all type of crossovers are suitable to solve vehicle routing problem (VRP). Therefore, this work introduces a multi parent insertion crossover in solving vehicle routing problem with time windows (VRPTW) by enhancing two parent insertion crossovers. This crossover exchange information among three parents instead of two. Result tested on Solomon VRPTW benchmarks demonstrate that multi parent crossover outperformed two parent crossover on same instances. This prove the effectiveness of having more parents for crossover that can be help the search to find better quality solution.
Asian Journal of Applied Sciences, 2014
2010 10th International Conference on Intelligent Systems Design and Applications, 2010
... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha... more ... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling. Proceeding in Journal of Scheduling, Volume 9, Number 5 / October, 2006, Springer Netherlands, pp. ...
2010 International Symposium on Information Technology, 2010
... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among the... more ... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among these approaches, Turabieh and Abdullah [24] (Ll), which applied Tabu Based Memetic have outperformed many other approaches in the literature (with regards to Socha benchmark datasets). ...
Procedia - Social and Behavioral Sciences, 2011
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically ... more ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically explore the neighborhood of current solution using a set of predefined neighborhood structures. Since different problem instances have different landscape and complexity, the choice of which neighborhood structure to be applied is a challenging task. Different neighborhood structures may lead to different solution space. Therefore, this work proposes a learning mechanism in a Variable Neighborhood Search (VNS), refer to hereafter as a Variable Neighborhood Guided Search (VNGS). Its effectiveness is illustrated by solving a course timetabling problems. The learning mechanism memorizes which neighborhood structure could effectively solve a specific soft constraint violations and used it to guide the selection of neighborhood structure to enhance the quality of a best solution. The performance of the VNGS is tested over Socha course timetabling dataset. Results demonstrate that the performance of the VNGS is comparable with the results of the other VNS variants and outperformed others in some instances. This demonstrates the effectiveness of applying a learning mechanism in a VNS algorithm.
2011 3rd Conference on Data Mining and Optimization (DMO), 2011
... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 da... more ... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 dataset. ... [11] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling, proceeding in Journal of Scheduling, Volume 9 ...
2009 2nd Conference on Data Mining and Optimization, 2009
Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to inc... more Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to increase the level of generality in which optimization methodologies can operate. In this work, we propose a scatter search based hyper-heuristic (SS-HH) approach for solving examination timetabling problems. The scatter search operates at high level of abstraction which intelligently evolves a sequence of low level heuristics to use for a given problem. Each low level heuristic represents a single neighborhood structure. We test our proposed approach on the un-capacitated Carter benchmarks datasets. Experimental results show the proposed SS-HH is capable of producing good quality solutions which are comparable to other hyper-heuristics approaches (with regarding to Carter benchmark datasets).
2012 IEEE International Conference on Power and Energy (PECon), 2012
ABSTRACT
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Information Sciences, 2013
IEEE Transactions on Evolutionary Computation, 2000
ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of pro... more ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g. different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems; exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state of the art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies which have reported best known results for some instances in both problem domains.