I am Senior Lecturer in Computer Science with UWE Bristol. Prior to this post, I have worked in academic and research positions for various universities.. I am editorial board member of a number of international peer-reviewed journals, and have been serving as committee member of various international conferences. I am also member of EPSRC Review College and fellow of Higher Education Academy.
Advances in Wireless Technologies and Telecommunication, 2019
In access networks, the radio resource management is designed to deal with the system capacity ma... more In access networks, the radio resource management is designed to deal with the system capacity maximization while the quality of service (QoS) requirements need be satisfied for different types of applications. In particular, the radio resource scheduling aims to allocate users' data packets in frequency domain at each predefined transmission time intervals (TTIs), time windows used to trigger the user requests and to respond them accordingly. At each TTI, the scheduling procedure is conducted based on a scheduling rule that aims to focus only on particular scheduling objective such as fairness, delay, packet loss, or throughput requirements. The purpose of this chapter is to formulate and solve an aggregate optimization problem that selects at each TTI the most convenient scheduling rule in order to maximize the satisfaction of all scheduling objectives concomitantly TTI-by-TTI. The use of reinforcement learning is proposed to solve such complex multi-objective optimization pro...
Research Anthology on Developing and Optimizing 5G Networks and the Impact on Society, 2021
The user experience constitutes an important quality metric when delivering high-definition video... more The user experience constitutes an important quality metric when delivering high-definition video services in wireless networks. Failing to provide these services within requested data rates, the user perceived quality is strongly degraded. On the radio interface, the packet scheduler is the key entity designed to satisfy the users' data rates requirements. In this chapter, a novel scheduler is proposed to guarantee the bit rate requirements for different types of services. However, the existing scheduling schemes satisfy the user rate requirements only at some extent because of their inflexibility to adapt for a variety of traffic and network conditions. In this sense, the authors propose an innovative framework able to select each time the most appropriate scheduling scheme. This framework makes use of reinforcement learning and neural network approximations to learn over time the scheduler type to be applied on each momentary state. The simulation results show the effectivene...
Operator selection plays a crucial role in the efficiency of heuristic-based problem solving algo... more Operator selection plays a crucial role in the efficiency of heuristic-based problem solving algorithms, especially, when a pool of operators is used to let algorithms dynamically select operators to produce new candidate solutions. A sequence of selected operators forms up throughout the search which impacts the success of the algorithms. Successive operators in a bespoke sequence can be complementary and therefore diversify the search while randomly selected operators are not expected to behave in this way. State of art adaptive selection schemes have been proposed to select the best next operator without considering the problem state in the process. In this study, a reinforcement learning algorithm is proposed to embed in a standard artificial bee colony algorithm for taking the problem state on board in operator selection process. The proposed approach implies mapping the problem states to the best fitting operators in the pool so as to achieve higher diversity and shape up an optimum operator sequence throughout the search process. The experimental study successfully demonstrates that the proposed idea works towards higher efficiency. The state of art approaches are outperformed with respect to the quality of solution in solving Set Union Knapsack problem over 30 benchmarking instances
Disruptive innovations of the last few decades, such as smart cities and Industry 4.0, were made ... more Disruptive innovations of the last few decades, such as smart cities and Industry 4.0, were made possible by higher integration of physical and digital elements. In today’s pervasive cyber-physical systems, connecting more devices introduces new vulnerabilities and security threats. With increasing cybersecurity incidents, cybersecurity professionals are becoming incapable of addressing what has become the greatest threat climate than ever before. This research investigates the spectrum of risk of a cybersecurity incident taking place in the cyber-physical-enabled world using the VERIS Community Database. The findings were that the majority of known actors were from the US and Russia, most victims were from western states and geographic origin tended to reflect global affairs. The most commonly targeted asset was information, with the majority of attack modes relying on privilege abuse. The key feature observed was extensive internal security breaches, most often a result of human e...
Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-... more Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-rolls with variable widths based on demands risen by customers. Since the variability is high due to collected various orders into a pool, the process turns to be production scheduling problem, which requires optimisation so as to minimise the final remaining amount of paper wasted. The problem holds characteristics similar one-dimensional bin-packing problem to some extends and differs with some respects. This paper introduces a modelling attempt as a scheduling problem with an integer programming approach for optimisation purposes. Then, a constructive heuristic algorithm revising one of well-known approaches, called Best-fit algorithm, is introduced to solve the problem. The illustrative examples provided shows the near optimum solution provided with very low complexity .
The petrochemical industry plays a crucial role in the economy of the Kingdom of Saudi Arabia. Th... more The petrochemical industry plays a crucial role in the economy of the Kingdom of Saudi Arabia. Therefore, the effectiveness and efficiency of this industry is of high importance. Data envelopment analysis (DEA) is found to be more acceptable in measuring the effectiveness of various industries when used in conjunction with non-parametric methods such as multiple regression, analytical hierarchy process (AHP), multidimensional scaling (MDS), and other multiple criteria decision making (MCDM) approaches. In this study, ten petrochemical companies in the Kingdom of Saudi Arabia are evaluated using Banker, Charnes and Cooper (BCC)/Charnes, Cooper, and Rhodes (CCR) models of DEA to compute the technical and super-efficiencies for ranking according to their relative performances. Data were collected from the Saudi Stock Exchange on key financial performance measures, five of which were chosen as inputs and five as outputs. Five DEA models were developed using different input–output combin...
In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popu... more In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the filed of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. Existing research, however, fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learnin...
Summary. Variable Neighborhood Search (VNS) is a recently invented meta-heuristic to use in solvi... more Summary. Variable Neighborhood Search (VNS) is a recently invented meta-heuristic to use in solving combinatorial optimization problems in which a systematic change of neighborhood with a local search is carried out. However, as happens with other meta-heuristics, it ...
Coordination of multi agent systems remains as a problem since there is no prominent method to co... more Coordination of multi agent systems remains as a problem since there is no prominent method to completely solve this problem. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems with metaheuristic algorithms. The idea borrowed from swarm intelligence seems working much better than those implementations suggested before. This paper reports the performance of swarms of simulated annealing agents collaborating with particle swarm optimization algorithm. The proposed approach is implemented for multidimensional knapsack problem and has resulted much better than some other works published before.
This paper presents a proof-of concept study for demonstrating the viability of building collabor... more This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via some sort competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory. Particles are devised with Q learning algorithm for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced results are supportive to the algorithmic structures suggesting that a substantive collaboration can be build via proposed learning algorithm.
Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-... more Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-rolls with variable widths based on demands risen by customers. Since the variability is high due to collected various orders into a pool, the process turns to be production scheduling problem, which requires optimisation so as to minimise the final remaining amount of paper wasted. The problem holds characteristics similar one-dimensional bin-packing problem to some extends and differs with some respects. This paper introduces a modelling attempt as a scheduling problem with an integer programming approach for optimisation purposes. Then, a constructive heuristic algorithm revising one of well-known approaches, called Best-fit algorithm, is introduced to solve the problem. The illustrative examples provided shows the near optimum solution provided with very low complexity .
Advances in Wireless Technologies and Telecommunication, 2019
In access networks, the radio resource management is designed to deal with the system capacity ma... more In access networks, the radio resource management is designed to deal with the system capacity maximization while the quality of service (QoS) requirements need be satisfied for different types of applications. In particular, the radio resource scheduling aims to allocate users' data packets in frequency domain at each predefined transmission time intervals (TTIs), time windows used to trigger the user requests and to respond them accordingly. At each TTI, the scheduling procedure is conducted based on a scheduling rule that aims to focus only on particular scheduling objective such as fairness, delay, packet loss, or throughput requirements. The purpose of this chapter is to formulate and solve an aggregate optimization problem that selects at each TTI the most convenient scheduling rule in order to maximize the satisfaction of all scheduling objectives concomitantly TTI-by-TTI. The use of reinforcement learning is proposed to solve such complex multi-objective optimization pro...
Research Anthology on Developing and Optimizing 5G Networks and the Impact on Society, 2021
The user experience constitutes an important quality metric when delivering high-definition video... more The user experience constitutes an important quality metric when delivering high-definition video services in wireless networks. Failing to provide these services within requested data rates, the user perceived quality is strongly degraded. On the radio interface, the packet scheduler is the key entity designed to satisfy the users' data rates requirements. In this chapter, a novel scheduler is proposed to guarantee the bit rate requirements for different types of services. However, the existing scheduling schemes satisfy the user rate requirements only at some extent because of their inflexibility to adapt for a variety of traffic and network conditions. In this sense, the authors propose an innovative framework able to select each time the most appropriate scheduling scheme. This framework makes use of reinforcement learning and neural network approximations to learn over time the scheduler type to be applied on each momentary state. The simulation results show the effectivene...
Operator selection plays a crucial role in the efficiency of heuristic-based problem solving algo... more Operator selection plays a crucial role in the efficiency of heuristic-based problem solving algorithms, especially, when a pool of operators is used to let algorithms dynamically select operators to produce new candidate solutions. A sequence of selected operators forms up throughout the search which impacts the success of the algorithms. Successive operators in a bespoke sequence can be complementary and therefore diversify the search while randomly selected operators are not expected to behave in this way. State of art adaptive selection schemes have been proposed to select the best next operator without considering the problem state in the process. In this study, a reinforcement learning algorithm is proposed to embed in a standard artificial bee colony algorithm for taking the problem state on board in operator selection process. The proposed approach implies mapping the problem states to the best fitting operators in the pool so as to achieve higher diversity and shape up an optimum operator sequence throughout the search process. The experimental study successfully demonstrates that the proposed idea works towards higher efficiency. The state of art approaches are outperformed with respect to the quality of solution in solving Set Union Knapsack problem over 30 benchmarking instances
Disruptive innovations of the last few decades, such as smart cities and Industry 4.0, were made ... more Disruptive innovations of the last few decades, such as smart cities and Industry 4.0, were made possible by higher integration of physical and digital elements. In today’s pervasive cyber-physical systems, connecting more devices introduces new vulnerabilities and security threats. With increasing cybersecurity incidents, cybersecurity professionals are becoming incapable of addressing what has become the greatest threat climate than ever before. This research investigates the spectrum of risk of a cybersecurity incident taking place in the cyber-physical-enabled world using the VERIS Community Database. The findings were that the majority of known actors were from the US and Russia, most victims were from western states and geographic origin tended to reflect global affairs. The most commonly targeted asset was information, with the majority of attack modes relying on privilege abuse. The key feature observed was extensive internal security breaches, most often a result of human e...
Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-... more Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-rolls with variable widths based on demands risen by customers. Since the variability is high due to collected various orders into a pool, the process turns to be production scheduling problem, which requires optimisation so as to minimise the final remaining amount of paper wasted. The problem holds characteristics similar one-dimensional bin-packing problem to some extends and differs with some respects. This paper introduces a modelling attempt as a scheduling problem with an integer programming approach for optimisation purposes. Then, a constructive heuristic algorithm revising one of well-known approaches, called Best-fit algorithm, is introduced to solve the problem. The illustrative examples provided shows the near optimum solution provided with very low complexity .
The petrochemical industry plays a crucial role in the economy of the Kingdom of Saudi Arabia. Th... more The petrochemical industry plays a crucial role in the economy of the Kingdom of Saudi Arabia. Therefore, the effectiveness and efficiency of this industry is of high importance. Data envelopment analysis (DEA) is found to be more acceptable in measuring the effectiveness of various industries when used in conjunction with non-parametric methods such as multiple regression, analytical hierarchy process (AHP), multidimensional scaling (MDS), and other multiple criteria decision making (MCDM) approaches. In this study, ten petrochemical companies in the Kingdom of Saudi Arabia are evaluated using Banker, Charnes and Cooper (BCC)/Charnes, Cooper, and Rhodes (CCR) models of DEA to compute the technical and super-efficiencies for ranking according to their relative performances. Data were collected from the Saudi Stock Exchange on key financial performance measures, five of which were chosen as inputs and five as outputs. Five DEA models were developed using different input–output combin...
In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popu... more In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the filed of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. Existing research, however, fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learnin...
Summary. Variable Neighborhood Search (VNS) is a recently invented meta-heuristic to use in solvi... more Summary. Variable Neighborhood Search (VNS) is a recently invented meta-heuristic to use in solving combinatorial optimization problems in which a systematic change of neighborhood with a local search is carried out. However, as happens with other meta-heuristics, it ...
Coordination of multi agent systems remains as a problem since there is no prominent method to co... more Coordination of multi agent systems remains as a problem since there is no prominent method to completely solve this problem. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems with metaheuristic algorithms. The idea borrowed from swarm intelligence seems working much better than those implementations suggested before. This paper reports the performance of swarms of simulated annealing agents collaborating with particle swarm optimization algorithm. The proposed approach is implemented for multidimensional knapsack problem and has resulted much better than some other works published before.
This paper presents a proof-of concept study for demonstrating the viability of building collabor... more This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via some sort competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory. Particles are devised with Q learning algorithm for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced results are supportive to the algorithmic structures suggesting that a substantive collaboration can be build via proposed learning algorithm.
Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-... more Paper cutting is a simple process of slicing large rolls of paper, jumbo-reels, into various sub-rolls with variable widths based on demands risen by customers. Since the variability is high due to collected various orders into a pool, the process turns to be production scheduling problem, which requires optimisation so as to minimise the final remaining amount of paper wasted. The problem holds characteristics similar one-dimensional bin-packing problem to some extends and differs with some respects. This paper introduces a modelling attempt as a scheduling problem with an integer programming approach for optimisation purposes. Then, a constructive heuristic algorithm revising one of well-known approaches, called Best-fit algorithm, is introduced to solve the problem. The illustrative examples provided shows the near optimum solution provided with very low complexity .
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Papers by Mehmet E Aydin