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Mehmet E Aydin

Bristol UWE, Computer Science and Creative Technologies, Faculty Member

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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.

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American University in Cairo

Duke University

Stellenbosch University

Armando Marques-Guedes

UNL - New University of Lisbon

Dr. MD. Mamunur Rashid

Bangladesh Institute of Management

Oxford Brookes University

Dachyar Muhammad

University of Indonesia

Prof. Milan Padariya

Gujarat Technological University

University of Technology Sydney

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Papers by Mehmet E Aydin

Analysing the Predictivity of Features to Characterise the Search Space

Exploring search spaces is one of the most unpredictable challenges that has attracted the intere... more Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.

Multi Strategy Search with Crow Search Algorithm

IntechOpen eBooks, Nov 2, 2022

Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms develop... more Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.

An Integrated Approach of Support Vector Machine and Variable Neighborhood Search for Discovering Combinational Gene Signatures in Predicting Chemo-response of Osteosarcoma

Analyzing microarray data could help discover significant cancer genes and their mutual interacti... more Analyzing microarray data could help discover significant cancer genes and their mutual interactions, which can be used to generate hypothesis for the identification and validation of genetic biomarkers. However, the commonly used statistical significance analysis can only provide information of individual genes, thus neglecting influence of the mutual interactions. Therefore, methods aiming at discovering combinational gene signatures are highly valuable. In this paper, an integrated approach of support vector machine (SVM) and variable neighborhood search (VNS) is introduced in searching the gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients. Cross validation results show that this method outperforms other existing algorithms. Further validation with the test dataset shows that only one of the fourteen samples is misclassified. The high testing accuracy further suggests that the proposed method has capability of extracting the stable discriminative signatures from microarray data. Terms-support vector machine, variable neighborhood search, osteosarcoma, gene signatures. Microarray chip can simultaneously interrogate thousands of genes, which provides an extremely powerful tool for genomic studies of cancer. A few key genes (typically involving oncogenes and tumor suppressor genes), when mutated, will cause dysregulation of the transcription and translation of other genes through complicated signaling pathways to initiate oncogenesis, and ultimately leading to derangement of the cellular phenotype and the clinical manifestations of cancer. Analyzing microarray data could help discover some significant cancer genes and their mutual interactions, which can be used to generate hypothesis for the identification and validation of genetic biomarkers for diagnostic and therapeutic purposes . This approach has

Energy Efficient Water Resource Management and Optimisation: A Multi-Agent Approach

Water supply remains one of globally recognised challenging problems due to the scarcity in the s... more Water supply remains one of globally recognised challenging problems due to the scarcity in the sources, the environmental concerns and hardness in access to clean and fresh water. Besides, energy consumption is also under focus as much as access to fresh water is. Substantial research efforts have been spent to produce good solutions for each of these global problems. However, there is not much investigated on considering both issues together in the same problems structure. This paper focuses on how the efficiency of water supply in a middle size water reach city can be optimised with respect to a number of key performance indicators including energy efficiency, given a variety of water resources including constructed and natural reservoirs. A particular case scenario is considered and its mathematical model is developed in order to optimally plan and control fresh water supply to the metropolitan area of the city introduced in the scenario. It is assumed that the fresh water is supplied from a number of different types of resources/reservoirs; natural lakes, constructed reservoirs (dams) etc. The proposed framework also includes a multi agent approach to manage the system in water supply control phase.

Büyük Kağıt Rolelerinin Kesme Çizelgelemesi

Academic platform-Journal of engineering and science, Apr 1, 2013

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.

A Strategy-Based Algorithm for Moving Targets in an Environment with Multiple Agents

SN computer science, Aug 9, 2022

Most studies in the field of search algorithms have only focused on pursuing agents, while compar... more Most studies in the field of search algorithms have only focused on pursuing agents, while comparatively less attention has been paid to target algorithms that employ strategies to evade multiple pursuing agents. In this study, a state-of-the-art target algorithm, TrailMax, has been enhanced and implemented for multiple agent pathfinding problems. The presented algorithm aims to maximise the capture time if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks, measuring the capture cost, the success of escape and statistically analysing the results. The new algorithm, Multiple Pursuers TrailMax, doubles the escaping time steps until capture when compared with existing target algorithms and increases the target's escaping success by 13% and in some individual cases by 37%. This article is part of the topical collection "Agents and Artificial Intelligence" guest edited by Jaap van den Herik, Ana Paula Rocha and Luc Steels.

Development of Communicating Stream X-Machine Tool for Modeling and Generating Test Cases for Automated Teller Machine

Computer Science and Information Technology Trends

The improvement of this paper takes advantage of the existing formal method called Stream XMachin... more The improvement of this paper takes advantage of the existing formal method called Stream XMachine by optimizing the theory and applying it to practice in a large-scale system. This optimized formal approach called Communicating Stream X-Machine (CSXM) applied in software testing based on its formal specifications to a distributed system as it points out its advantages and limits of the use of the existing formal methods to this level. However, despite the tremendous works that has been done in the software testing research area, the origin of bugs or defects in a software is still cost and takes more time to detect. Therefore, this paper has proven that the current state of art challenge is due to that lack of a formal specification of what exactly a software system is supposed to do. In this paper, CSXM principles was used for the development of Automated Teller Machine (ATM) given formal specification which outputs conforms with the implementation. Moreso, the computational stren...

Feature-based search space characterisation for data-driven adaptive operator selection

Evolving Systems, Dec 21, 2023

Combinatorial optimisation problems are known as unpredictable and challenging due to their natur... more Combinatorial optimisation problems are known as unpredictable and challenging due to their nature and complexity. One way to reduce the unpredictability of such problems is to identify features and the characteristics that can be utilised to guide the search using domain-knowledge and act accordingly. Many problem solving algorithms use multiple complementary operators in patterns to handle such unpredictable cases. A well-characterised search space may help to evaluate the problem states better and select/apply a neighbourhood operator to generate more productive new problem states that allow for a smoother path to the final/optimum solutions. This applies to the algorithms that use multiple operators to solve problems. However, the remaining challenge is determining how to select an operator in an optimal way from the set of operators while taking the search space conditions into consideration. Recent research shows the success of adaptive operator selection to address this problem. However, efficiency and scalability issues persist in this regard. In addition, selecting the most representative features remains crucial in addressing problem complexity and inducing commonality for transferring experience across domains. This paper investigates if a problem can be represented by a number of features identified by landscape analysis, and whether an adaptive operator selection scheme can be constructed using Machine Learning (ML) techniques to address the efficiency and scalability problem. The proposed method determines the optimal categorisation by analysing the predictivity of a set of features using the most well-known supervised ML techniques. The identified set of features is then used to construct an adaptive operator selection scheme. The findings of the experiments demonstrate that supervised ML algorithms are highly effective when building adaptable operator selectors.

Smartness and Strategic Priority Assessment in Transition to Mobility 4.0 for Smart Cities

Journal of Intelligent Systems: Theory and Applications, 2021

Digital transformation of urban transportation services attracts significant attention within the... more Digital transformation of urban transportation services attracts significant attention within the scope of smart city studies. It is known as a strategic action with many investment opportunities for the future cities, therefore it attracts the attention of enterprises and institutions. A sound and sustainable digital transformation requires substantial decision support in order to customize the needs and priorities of cities, especially in developing countries, where a lack of knowledge and skills is the case. Mobility 4.0 is recognized as the most up-to-date state of art technology and vision for urban transportation in the age of smart cities. Mobility as a Service (MaAS) is one of the most prominent Mobility 4.0 components that needs to be developed with an integrated management approach, which is available for research and development investments that have defined conceptually yet. In this study, the smartness level of city transport services is identified with a four-level transition approach. The transition from Mobility 3.0 to Mobility 4.0 for cities has been carried out with conceptual integrated management design of MaAS and demonstrated with a case study. The study also adapted a series of new strategically targeted transformation priorities for cities with reference to research and development strategies.

Machine Learning in Radio Resource Scheduling

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...

Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks

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...

An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma

In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chi... more In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chip that interrogate thousands of genes is important for diagnosis and therapeutics. However, the commonly used statistical significance analysis can only provide information of each single gene, thus neglecting the intrinsic interactions among genes. Therefore, methods aiming at combinational gene signatures are highly valuable. Supervised classification is an effective way to assess the function of a gene combination in differentiating various groups of samples. In this paper, an evolutionary variable neighborhood search (EVNS) that integrated the approaches of evolutionary algorithm and variable neighborhood search (VNS) is introduced. It consists of a population of solutions that evolution is performed by a variable neighborhood search operator, instead of the more usual reproduction operators, crossover and mutation used in evolutionary algorithms. It is an efficient search algorithm especially suitable for tremendous solution space. The proposed EVNS can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. This method was applied in searching the combinational gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that EVNS outperforms the other existing approaches in classifying initial biopsy samples.

Simulated Annealing and Multiuser Scheduling in Mobile Communication Networks

Simulated Annealing - Single and Multiple Objective Problems, 2012

Max-gain relay selection scheme for wireless networks

Engineering Science and Technology, an International Journal

Next generation wireless systems are supposed to handle high amount of data with broader coverage... more Next generation wireless systems are supposed to handle high amount of data with broader coverage and high quality of service (QoS). When a signal travels from a source to destination, the signal quality may suffer from the fading, which makes it difficult to receive correct messages. To handle the impact of fading, various diversity techniques are performed with Multiple Input Multiple Output (MIMO). Considering cooperative wireless networks, virtual MIMOs are being used, which also called cooperative diversity. In this paper, we propose a max-gain relay selection scheme (MGRS) for buffer-aided wireless cooperative networks. This scheme determines the best link using the maximum gain based on quality of link and available buffer size. The time slot is divided into two parts, one is used to choose the best link from the source to relay transmission (odd slot) and another time slot (even) is used based on the selection of the best link from the relay to destination. Markov chain model is use to measure buffer status and QoS parameters to evaluate the performance. The proposed scheme provides better QoS (12%) compared to the existing relay selection schemes with respect to throughput, end-to-end delay and outage probability.

Adaptive operator selection with reinforcement learning

Information Sciences, 2021

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

A multi agent-based approach for energy efficient water resource management

Computers & Industrial Engineering, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Threats on the horizon: understanding security threats in the era of cyber-physical systems

The Journal of Supercomputing, 2019

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...

Scheduling Cutting Process for Large Paper Rolls

ArXiv, 2013

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 .

An analytical framework for high-speed hardware particle swarm optimization

Microprocessors and Microsystems, 2020

Engineering optimization techniques are computationally intensive and can challenge implementatio... more Engineering optimization techniques are computationally intensive and can challenge implementations on tightly-constrained embedded systems. Particle Swarm Optimization (PSO) is a well-known bio-inspired algorithm that is adopted in various applications, such as, transportation, robotics, energy, etc. In this paper, a high-speed PSO hardware processor is developed with focus on outperforming similar state-of-the-art implementations. In addition, the investigation

Heuristic-based neural networks for stochastic dynamic lot sizing problem

Applied Soft Computing, 2013

Multi-period single-item lot sizing problem under stochastic environment has been tackled by few ... more Multi-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domainspecific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA.

Analysing the Predictivity of Features to Characterise the Search Space

Exploring search spaces is one of the most unpredictable challenges that has attracted the intere... more Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.

Multi Strategy Search with Crow Search Algorithm

IntechOpen eBooks, Nov 2, 2022

Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms develop... more Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.

An Integrated Approach of Support Vector Machine and Variable Neighborhood Search for Discovering Combinational Gene Signatures in Predicting Chemo-response of Osteosarcoma

Analyzing microarray data could help discover significant cancer genes and their mutual interacti... more Analyzing microarray data could help discover significant cancer genes and their mutual interactions, which can be used to generate hypothesis for the identification and validation of genetic biomarkers. However, the commonly used statistical significance analysis can only provide information of individual genes, thus neglecting influence of the mutual interactions. Therefore, methods aiming at discovering combinational gene signatures are highly valuable. In this paper, an integrated approach of support vector machine (SVM) and variable neighborhood search (VNS) is introduced in searching the gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients. Cross validation results show that this method outperforms other existing algorithms. Further validation with the test dataset shows that only one of the fourteen samples is misclassified. The high testing accuracy further suggests that the proposed method has capability of extracting the stable discriminative signatures from microarray data. Terms-support vector machine, variable neighborhood search, osteosarcoma, gene signatures. Microarray chip can simultaneously interrogate thousands of genes, which provides an extremely powerful tool for genomic studies of cancer. A few key genes (typically involving oncogenes and tumor suppressor genes), when mutated, will cause dysregulation of the transcription and translation of other genes through complicated signaling pathways to initiate oncogenesis, and ultimately leading to derangement of the cellular phenotype and the clinical manifestations of cancer. Analyzing microarray data could help discover some significant cancer genes and their mutual interactions, which can be used to generate hypothesis for the identification and validation of genetic biomarkers for diagnostic and therapeutic purposes . This approach has

Energy Efficient Water Resource Management and Optimisation: A Multi-Agent Approach

Water supply remains one of globally recognised challenging problems due to the scarcity in the s... more Water supply remains one of globally recognised challenging problems due to the scarcity in the sources, the environmental concerns and hardness in access to clean and fresh water. Besides, energy consumption is also under focus as much as access to fresh water is. Substantial research efforts have been spent to produce good solutions for each of these global problems. However, there is not much investigated on considering both issues together in the same problems structure. This paper focuses on how the efficiency of water supply in a middle size water reach city can be optimised with respect to a number of key performance indicators including energy efficiency, given a variety of water resources including constructed and natural reservoirs. A particular case scenario is considered and its mathematical model is developed in order to optimally plan and control fresh water supply to the metropolitan area of the city introduced in the scenario. It is assumed that the fresh water is supplied from a number of different types of resources/reservoirs; natural lakes, constructed reservoirs (dams) etc. The proposed framework also includes a multi agent approach to manage the system in water supply control phase.

Büyük Kağıt Rolelerinin Kesme Çizelgelemesi

Academic platform-Journal of engineering and science, Apr 1, 2013

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.

A Strategy-Based Algorithm for Moving Targets in an Environment with Multiple Agents

SN computer science, Aug 9, 2022

Most studies in the field of search algorithms have only focused on pursuing agents, while compar... more Most studies in the field of search algorithms have only focused on pursuing agents, while comparatively less attention has been paid to target algorithms that employ strategies to evade multiple pursuing agents. In this study, a state-of-the-art target algorithm, TrailMax, has been enhanced and implemented for multiple agent pathfinding problems. The presented algorithm aims to maximise the capture time if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks, measuring the capture cost, the success of escape and statistically analysing the results. The new algorithm, Multiple Pursuers TrailMax, doubles the escaping time steps until capture when compared with existing target algorithms and increases the target's escaping success by 13% and in some individual cases by 37%. This article is part of the topical collection "Agents and Artificial Intelligence" guest edited by Jaap van den Herik, Ana Paula Rocha and Luc Steels.

Development of Communicating Stream X-Machine Tool for Modeling and Generating Test Cases for Automated Teller Machine

Computer Science and Information Technology Trends

The improvement of this paper takes advantage of the existing formal method called Stream XMachin... more The improvement of this paper takes advantage of the existing formal method called Stream XMachine by optimizing the theory and applying it to practice in a large-scale system. This optimized formal approach called Communicating Stream X-Machine (CSXM) applied in software testing based on its formal specifications to a distributed system as it points out its advantages and limits of the use of the existing formal methods to this level. However, despite the tremendous works that has been done in the software testing research area, the origin of bugs or defects in a software is still cost and takes more time to detect. Therefore, this paper has proven that the current state of art challenge is due to that lack of a formal specification of what exactly a software system is supposed to do. In this paper, CSXM principles was used for the development of Automated Teller Machine (ATM) given formal specification which outputs conforms with the implementation. Moreso, the computational stren...

Feature-based search space characterisation for data-driven adaptive operator selection

Evolving Systems, Dec 21, 2023

Combinatorial optimisation problems are known as unpredictable and challenging due to their natur... more Combinatorial optimisation problems are known as unpredictable and challenging due to their nature and complexity. One way to reduce the unpredictability of such problems is to identify features and the characteristics that can be utilised to guide the search using domain-knowledge and act accordingly. Many problem solving algorithms use multiple complementary operators in patterns to handle such unpredictable cases. A well-characterised search space may help to evaluate the problem states better and select/apply a neighbourhood operator to generate more productive new problem states that allow for a smoother path to the final/optimum solutions. This applies to the algorithms that use multiple operators to solve problems. However, the remaining challenge is determining how to select an operator in an optimal way from the set of operators while taking the search space conditions into consideration. Recent research shows the success of adaptive operator selection to address this problem. However, efficiency and scalability issues persist in this regard. In addition, selecting the most representative features remains crucial in addressing problem complexity and inducing commonality for transferring experience across domains. This paper investigates if a problem can be represented by a number of features identified by landscape analysis, and whether an adaptive operator selection scheme can be constructed using Machine Learning (ML) techniques to address the efficiency and scalability problem. The proposed method determines the optimal categorisation by analysing the predictivity of a set of features using the most well-known supervised ML techniques. The identified set of features is then used to construct an adaptive operator selection scheme. The findings of the experiments demonstrate that supervised ML algorithms are highly effective when building adaptable operator selectors.

Smartness and Strategic Priority Assessment in Transition to Mobility 4.0 for Smart Cities

Journal of Intelligent Systems: Theory and Applications, 2021

Digital transformation of urban transportation services attracts significant attention within the... more Digital transformation of urban transportation services attracts significant attention within the scope of smart city studies. It is known as a strategic action with many investment opportunities for the future cities, therefore it attracts the attention of enterprises and institutions. A sound and sustainable digital transformation requires substantial decision support in order to customize the needs and priorities of cities, especially in developing countries, where a lack of knowledge and skills is the case. Mobility 4.0 is recognized as the most up-to-date state of art technology and vision for urban transportation in the age of smart cities. Mobility as a Service (MaAS) is one of the most prominent Mobility 4.0 components that needs to be developed with an integrated management approach, which is available for research and development investments that have defined conceptually yet. In this study, the smartness level of city transport services is identified with a four-level transition approach. The transition from Mobility 3.0 to Mobility 4.0 for cities has been carried out with conceptual integrated management design of MaAS and demonstrated with a case study. The study also adapted a series of new strategically targeted transformation priorities for cities with reference to research and development strategies.

Machine Learning in Radio Resource Scheduling

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...

Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks

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...

An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma

In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chi... more In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chip that interrogate thousands of genes is important for diagnosis and therapeutics. However, the commonly used statistical significance analysis can only provide information of each single gene, thus neglecting the intrinsic interactions among genes. Therefore, methods aiming at combinational gene signatures are highly valuable. Supervised classification is an effective way to assess the function of a gene combination in differentiating various groups of samples. In this paper, an evolutionary variable neighborhood search (EVNS) that integrated the approaches of evolutionary algorithm and variable neighborhood search (VNS) is introduced. It consists of a population of solutions that evolution is performed by a variable neighborhood search operator, instead of the more usual reproduction operators, crossover and mutation used in evolutionary algorithms. It is an efficient search algorithm especially suitable for tremendous solution space. The proposed EVNS can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. This method was applied in searching the combinational gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that EVNS outperforms the other existing approaches in classifying initial biopsy samples.

Simulated Annealing and Multiuser Scheduling in Mobile Communication Networks

Simulated Annealing - Single and Multiple Objective Problems, 2012

Max-gain relay selection scheme for wireless networks

Engineering Science and Technology, an International Journal

Next generation wireless systems are supposed to handle high amount of data with broader coverage... more Next generation wireless systems are supposed to handle high amount of data with broader coverage and high quality of service (QoS). When a signal travels from a source to destination, the signal quality may suffer from the fading, which makes it difficult to receive correct messages. To handle the impact of fading, various diversity techniques are performed with Multiple Input Multiple Output (MIMO). Considering cooperative wireless networks, virtual MIMOs are being used, which also called cooperative diversity. In this paper, we propose a max-gain relay selection scheme (MGRS) for buffer-aided wireless cooperative networks. This scheme determines the best link using the maximum gain based on quality of link and available buffer size. The time slot is divided into two parts, one is used to choose the best link from the source to relay transmission (odd slot) and another time slot (even) is used based on the selection of the best link from the relay to destination. Markov chain model is use to measure buffer status and QoS parameters to evaluate the performance. The proposed scheme provides better QoS (12%) compared to the existing relay selection schemes with respect to throughput, end-to-end delay and outage probability.

Adaptive operator selection with reinforcement learning

Information Sciences, 2021

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

A multi agent-based approach for energy efficient water resource management

Computers & Industrial Engineering, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Threats on the horizon: understanding security threats in the era of cyber-physical systems

The Journal of Supercomputing, 2019

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...

Scheduling Cutting Process for Large Paper Rolls

ArXiv, 2013

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 .

An analytical framework for high-speed hardware particle swarm optimization

Microprocessors and Microsystems, 2020

Engineering optimization techniques are computationally intensive and can challenge implementatio... more Engineering optimization techniques are computationally intensive and can challenge implementations on tightly-constrained embedded systems. Particle Swarm Optimization (PSO) is a well-known bio-inspired algorithm that is adopted in various applications, such as, transportation, robotics, energy, etc. In this paper, a high-speed PSO hardware processor is developed with focus on outperforming similar state-of-the-art implementations. In addition, the investigation

Heuristic-based neural networks for stochastic dynamic lot sizing problem

Applied Soft Computing, 2013

Multi-period single-item lot sizing problem under stochastic environment has been tackled by few ... more Multi-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domainspecific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA.