All Outputs (111)

HCat-GNet: a Human-Interpretable GNN Tool for Ligand Optimization in Asymmetric Catalysis (2025)
Journal Article
Aguilar-Bejarano, E., Özcan, E., Rit, R. K., Li, H., Lam, H. W., Moore, J. C., Woodward, S., & Figueredo, G. (2025). HCat-GNet: a Human-Interpretable GNN Tool for Ligand Optimization in Asymmetric Catalysis. iScience, Article 111881. https://doi.org/10.1016/j.isci.2025.111881

Optimization of metal-ligand asymmetric catalysts is usually done by empirical trials, where the ligand is arbitrary modified, and the new catalyst is re-evaluated in the lab. This procedure is not efficient and alternative strategies are highly desi... Read More about HCat-GNet: a Human-Interpretable GNN Tool for Ligand Optimization in Asymmetric Catalysis.

Optimisation of additively manufactured coiled flow inverters for continuous viral inactivation processes (2024)
Journal Article
Barrera, M. C., Leech, D., Josifovic, A., Tolouei, A., Alford, G., Wallace, M. J., Bennett, N., Wildman, R., Irvine, D. J., Croft, A., Özcan, E., Florence, A. J., Johnston, B., Robertson, J., & Brown, C. J. (2025). Optimisation of additively manufactured coiled flow inverters for continuous viral inactivation processes. Chemical Engineering Research and Design, 213, 126-136. https://doi.org/10.1016/j.cherd.2024.11.040

This article reports the development and utilisation of an adaptive design workflow methodology for use as a platform technology for the printing, testing, and optimisation of biopharmaceutical processing reactors. This design strategy was developed... Read More about Optimisation of additively manufactured coiled flow inverters for continuous viral inactivation processes.

FiDRL: Flexible Invocation-Based Deep Reinforcement Learning for DVFS Scheduling in Embedded Systems (2024)
Journal Article
Li, J., Jiang, W., He, Y., Yang, Q., Gao, A., Ha, Y., Özcan, E., Bai, R., Cui, T., & Yu, H. (2024). FiDRL: Flexible Invocation-Based Deep Reinforcement Learning for DVFS Scheduling in Embedded Systems. IEEE Transactions on Computers, 74(1), 71 - 85. https://doi.org/10.1109/TC.2024.3465933

Deep Reinforcement Learning (DRL)-based Dynamic Voltage Frequency Scaling (DVFS) has shown great promise for energy conservation in embedded systems. While many works were devoted to validating its efficacy or improving its performance, few discuss t... Read More about FiDRL: Flexible Invocation-Based Deep Reinforcement Learning for DVFS Scheduling in Embedded Systems.

Gase: graph attention sampling with edges fusion for solving vehicle routing problems (2024)
Journal Article
Wang, Z., Bai, R., Khan, F., Özcan, E., & Zhang, T. (2024). Gase: graph attention sampling with edges fusion for solving vehicle routing problems. Memetic Computing, 16(3), 337–353. https://doi.org/10.1007/s12293-024-00428-0

Learning-based methods have become increasingly popular for solving vehicle routing problems (VRP) due to their near-optimal performance and fast inference speed. Among them, the combination of deep reinforcement learning and graph representation all... Read More about Gase: graph attention sampling with edges fusion for solving vehicle routing problems.

CUDA-based parallel local search for the set-union knapsack problem (2024)
Journal Article
Sonuç, E., & Özcan, E. (2024). CUDA-based parallel local search for the set-union knapsack problem. Knowledge-Based Systems, 299, Article 112095. https://doi.org/10.1016/j.knosys.2024.112095

The Set-Union Knapsack Problem (SUKP) is a complex combinatorial optimisation problem with applications in resource allocation, portfolio selection, and logistics. This paper presents a parallel local search algorithm for solving SU... Read More about CUDA-based parallel local search for the set-union knapsack problem.

Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts (2024)
Journal Article
Tyasnurita, R., Özcan, E., Drake, J. H., & Asta, S. (2024). Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts. Knowledge-Based Systems, 295, Article 111731. https://doi.org/10.1016/j.knosys.2024.111731

Hyper-heuristics are general purpose search methods for solving computationally difficult problems. A selection hyper-heuristic is composed of two key components: a heuristic selection method and move acceptance criterion. Under an iterative single-p... Read More about Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts.

A benchmark dataset for multi-objective flexible job shop cell scheduling (2023)
Journal Article
Deliktaş, D., Özcan, E., Ustun, O., & Torkul, O. (2024). A benchmark dataset for multi-objective flexible job shop cell scheduling. Data in Brief, 52, Article 109946. https://doi.org/10.1016/j.dib.2023.109946

This data article presents a description of a benchmark dataset for the multi-objective flexible job shop scheduling problem in a cellular manufacturing environment. This problem considers intercellular moves, exceptional parts, sequence-dependent fa... Read More about A benchmark dataset for multi-objective flexible job shop cell scheduling.

Ensemble strategy using particle swarm optimisation variant and enhanced local search capability (2023)
Journal Article
Hong, L., Wang, G., Özcan, E., & Woodward, J. (2023). Ensemble strategy using particle swarm optimisation variant and enhanced local search capability. Swarm and Evolutionary Computation, 84, Article 101452. https://doi.org/10.1016/j.swevo.2023.101452

Particle swarm optimisation is a population-based algorithm for evolutionary computation. A notable recent research direction has been to combine different effective mechanisms to enhance both exploration and exploitation capabilities while employing... Read More about Ensemble strategy using particle swarm optimisation variant and enhanced local search capability.

A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization (2023)
Journal Article
Hong, L., Yu, X., Tao, G., Özcan, E., & Woodward, J. (2024). A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization. Complex and Intelligent Systems, 10(2), 2421-2443. https://doi.org/10.1007/s40747-023-01269-z

Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-param... Read More about A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization.

An adaptive greedy heuristic for large scale airline crew pairing problems (2023)
Journal Article
Zeren, B., Özcan, E., & Deveci, M. (2024). An adaptive greedy heuristic for large scale airline crew pairing problems. Journal of Air Transport Management, 114, Article 102492. https://doi.org/10.1016/j.jairtraman.2023.102492

A crew pairing represents a sequence of flight legs that constitute a crew work allocation, starting and ending at the same crew base. A complete set of crew pairings covers all flight legs in the timetable of an airline for a given planning horizon.... Read More about An adaptive greedy heuristic for large scale airline crew pairing problems.

ML meets MLn: machine learning in ligand promoted homogeneous catalysis (2023)
Journal Article
Hirst, J. D., Boobier, S., Coughlan, J., Streets, J., Jacob, P. L., Pugh, O., Özcan, E., & Woodward, S. (2023). ML meets MLn: machine learning in ligand promoted homogeneous catalysis. Artificial Intelligence Chemistry, 1(2), Article 100006. https://doi.org/10.1016/j.aichem.2023.100006

The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised. We focus on the understanding and implementation of key concepts, which serve as condu... Read More about ML meets MLn: machine learning in ligand promoted homogeneous catalysis.

Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies (2023)
Journal Article
Ramamoorthy, V. T., Özcan, E., Parkes, A. J., Jaouen, L., & Bécot, F.-X. (2023). Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies. Journal of the Acoustical Society of America, 153(5), Article 2945. https://doi.org/10.1121/10.0019455

When designing passive sound-attenuation structures, one of the challenging problems that arise is optimally distributing acoustic porous materials within a design region so as to maximise sound absorption while minimising material usage. To identify... Read More about Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies.

Layup time for an Automated Fibre Placement process in the framework of a detailed sizing optimisation (2023)
Journal Article
Ntourmas, G., Glock, F., Daoud, F., Schuhmacher, G., Chronopoulos, D., Özcan, E., & Ninić, J. (2023). Layup time for an Automated Fibre Placement process in the framework of a detailed sizing optimisation. Composites Part B: Engineering, 258, Article 110714. https://doi.org/10.1016/j.compositesb.2023.110714

Automatic Fibre Placement manufacturing processes have become the aerospace industry standard for the production of large-scale composite components. Besides the challenges linked with the manufacturing of such components, their design process is als... Read More about Layup time for an Automated Fibre Placement process in the framework of a detailed sizing optimisation.

Corrigendum to “Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles” [Prog. Aero. Sci. 135 (2022) 100861] (2023)
Journal Article
Corrado, G., Ntourmas, G., Sferza, M., Tan, W., Traiforos, N., Arteiro, A., Brown, L., Chronopoulos, D., Daoud, F., Glock, F., Ninic, J., Ozcan, E., Reinoso, J., Schuhmacher, G., & Turner, T. (2023). Corrigendum to “Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles” [Prog. Aero. Sci. 135 (2022) 100861]. Progress in Aerospace Sciences, 139, Article 100903. https://doi.org/10.1016/j.paerosci.2023.100903

The authors consider that the manuscript could benefit from additional information regarding the seamless integration of software tools in Section 3.5. Hence, as the primary researcher working on this part of the study, Weijie Tan was invited to revi... Read More about Corrigendum to “Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles” [Prog. Aero. Sci. 135 (2022) 100861].

An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem (2023)
Journal Article
Sonuç, E., & Özcan, E. (2023). An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem. Expert Systems with Applications, 224, Article 119956. https://doi.org/10.1016/j.eswa.2023.119956

Metaheuristics, providing high level guidelines for heuristic optimisation, have successfully been applied to many complex problems over the past decades. However, their performances often vary depending on the choice of the initial settings for thei... Read More about An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem.

An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase (2023)
Journal Article
Hong, L., Yu, X., Wang, B., Woodward, J., & Özcan, E. (2023). An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase. Swarm and Evolutionary Computation, 78, Article 101278. https://doi.org/10.1016/j.swevo.2023.101278

Over the past two decades, to overcome the limitations of certain algorithms, ensemble strategies or self-adaptive mechanisms for evolutionary computation algorithms have been proposed. Regardless of how these strategies or mechanisms were designed,... Read More about An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase.

A generality analysis of multiobjective hyper-heuristics (2023)
Journal Article
Li, W., Özcan, E., Drake, J. H., & Maashi, M. (2023). A generality analysis of multiobjective hyper-heuristics. Information Sciences, 627, 34-51. https://doi.org/10.1016/j.ins.2023.01.047

Selection hyper-heuristics have emerged as high level general-purpose search methodologies that mix and control a set of low-level (meta) heuristics. Previous empirical studies over a range of single objective optimisation problems have shown that th... Read More about A generality analysis of multiobjective hyper-heuristics.

Stacking sequence optimisation of an aircraft wing skin (2023)
Journal Article
Ntourmas, G., Glock, F., Deinert, S., Daoud, F., Schuhmacher, G., Chronopoulos, D., Özcan, E., & Ninić, J. (2023). Stacking sequence optimisation of an aircraft wing skin. Structural and Multidisciplinary Optimization, 66(2), Article 31. https://doi.org/10.1007/s00158-022-03483-8

This paper demonstrates a stacking sequence optimisation process of a composite aircraft wing skin. A two-stage approach is employed to satisfy all sizing requirements of this industrial sized, medium altitude, long endurance drone. In the first stag... Read More about Stacking sequence optimisation of an aircraft wing skin.

Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup (2022)
Book Chapter
Kiraz, B., Asta, S., Özcan, E., Köle, M., & Etaner-Uyar, A. S. (2023). Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup. In Engineering Applications of Modern Metaheuristics (1-18). Springer. https://doi.org/10.1007/978-3-031-16832-1_1

Tuning a race car to improve its performance by adopting an effective setup is crucial and an extremely challenging task. The Open Racing Car Simulator, referred to as TORCS, is a well-known simulator in which a race car requires a configuration of t... Read More about Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup.

An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms (2022)
Journal Article
Gümüş, D. B., Özcan, E., Atkin, J., & Drake, J. H. (2023). An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms. Information Sciences, 619, 153-171. https://doi.org/10.1016/j.ins.2022.11.008

Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheuristic performance. There is growing interest in cross-domain search methods, which consider a range of optimisation problems rather than being specialise... Read More about An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms.