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All Outputs (111)

Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles (2022)
Journal Article
Corrado, G., Ntourmas, G., Sferza, M., Traiforos, N., Arteiro, A., Brown, L., Chronopoulos, D., Daoud, F., Glock, F., Ninic, J., Ozcan, E., Reinoso, J., Schuhmacher, G., & Turner, T. (2022). Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles. Progress in Aerospace Sciences, 135, Article 100861. https://doi.org/10.1016/j.paerosci.2022.100861

Designing an airframe is a complex process as it requires knowledge from multiple disciplines such as aerodynamics, structural mechanics, manufacturing, flight dynamics, which individually lead to very different optimal designs. Furthermore, the grow... Read More about Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles.

Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations (2022)
Journal Article
Hey, J., Siebers, P. O., Nathanail, P., Ozcan, E., & Robinson, D. (2022). Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations. Journal of Building Performance Simulation, https://doi.org/10.1080/19401493.2022.2106309

Modelling energy retrofit adoption in domestic urban building stocks is vital for policymakers aiming to reduce emissions. The use of surrogate models to evaluate building performance combined with optimization procedures can optimize small building... Read More about Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations.

Many-objective test case generation for graphical user interface applications via search-based and model-based testing (2022)
Journal Article
de Santiago, V. A., Özcan, E., & Balera, J. M. (2022). Many-objective test case generation for graphical user interface applications via search-based and model-based testing. Expert Systems with Applications, 208, Article 118075. https://doi.org/10.1016/j.eswa.2022.118075

The majority of the studies that generate test cases for graphical user interface (GUI) applications are based on or address functional requirements only. In spite of the fact that interesting approaches have been proposed, they do not address functi... Read More about Many-objective test case generation for graphical user interface applications via search-based and model-based testing.

A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse (2022)
Journal Article
Deveci, M., Mishra, A. R., Gokasar, I., Rani, P., Pamucar, D., & Ozcan, E. (2023). A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse. IEEE Transactions on Fuzzy Systems, 31(2), 475-484. https://doi.org/10.1109/TFUZZ.2022.3190613

Blockchain technology and metaverse advancements allow people to create virtual personalities and spend time online. Integrating public transportation into the metaverse could improve services and collect user data. This study introduces a hybrid dec... Read More about A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse.

Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task (2022)
Journal Article
Kok, Y. E., Woodward, S., Özcan, E., & Torres Torres, M. (2022). Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task. Molecular Informatics, 41(12), Article 2200068. https://doi.org/10.1002/minf.202200068

Chirality, the ability of some molecules to exist as two non-superimposable mirror images, profoundly influences both chemistry and biology. Advances in deep learning enable the automatic recognition of chemical structure diagrams, however, studies o... Read More about Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task.

A fusion spatial attention approach for few-shot learning (2021)
Journal Article
Song, H., Deng, B., Pound, M., Özcan, E., & Triguero, I. (2022). A fusion spatial attention approach for few-shot learning. Information Fusion, 81, 187-202. https://doi.org/10.1016/j.inffus.2021.11.019

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few image... Read More about A fusion spatial attention approach for few-shot learning.

Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection (2021)
Journal Article
Deveci, M., Öner, S. C., Ciftci, M. E., Özcan, E., & Pamucar, D. (2022). Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection. Applied Soft Computing, 114, Article 108076. https://doi.org/10.1016/j.asoc.2021.108076

Choosing the most appropriate aircraft type for a given route is one of the crucial issues that the decision makers at airline companies have to address under uncertainty based on various commercial, marketing and operational criteria. A novel multi-... Read More about Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection.

Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems (2021)
Journal Article
de Carvalho, V. R., Özcan, E., & Sichman, J. S. (2021). Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems. Applied Sciences, 11(19), Article 9153. https://doi.org/10.3390/app11199153

As exact algorithms are unfeasible to solve real optimization problems, due to their computational complexity, meta-heuristics are usually used to solve them. However, choosing a meta-heuristic to solve a particular optimization problem is a non-triv... Read More about Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems.

Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials (2021)
Journal Article
Ramamoorthy, V. T., Özcan, E., Parkes, A. J., Sreekumar, A., Jaouen, L., & Bécot, F.-X. (2021). Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials. Journal of the Acoustical Society of America, 150(4), 3164-3175. https://doi.org/10.1121/10.0006784

When designing sound packages, often fully filling the available space with acoustic materials is not the most absorbing solution. Better solutions can be obtained by creating cavities of air pockets, but determining the most optimal shape and topolo... Read More about Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials.

Evolutionary algorithms for multi-objective flexible job shop cell scheduling (2021)
Journal Article
Deliktaş, D., Özcan, E., Ustun, O., & Torkul, O. (2021). Evolutionary algorithms for multi-objective flexible job shop cell scheduling. Applied Soft Computing, 113(Part A), Article 107890. https://doi.org/10.1016/j.asoc.2021.107890

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is a challenging real-world problem. This recently introduced scheduling problem variant considers exceptional parts, intercellular moves, intercellular transpor... Read More about Evolutionary algorithms for multi-objective flexible job shop cell scheduling.

Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming (2021)
Journal Article
Hong, L., Woodward, J. R., Özcan, E., & Liu, F. (2021). Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming. Complex and Intelligent Systems, 7(6), 3135-3163. https://doi.org/10.1007/s40747-021-00507-6

Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Lévy distribution. This study... Read More about Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming.

Generic stacks and application of composite rules for the detailed sizing of laminated structures (2021)
Journal Article
Ntourmas, G., Glock, F., Daoud, F., Schuhmacher, G., Chronopoulos, D., & Özcan, E. (2021). Generic stacks and application of composite rules for the detailed sizing of laminated structures. Composite Structures, 276, Article 114487. https://doi.org/10.1016/j.compstruct.2021.114487

Two-stage approaches are commonly applied to optimise the stacking sequence of large-scale composite structures. The two stages consist of a gradient and non-gradient based optimisation addressing the mixed nature of continuous and discrete constrain... Read More about Generic stacks and application of composite rules for the detailed sizing of laminated structures.

Metaheuristics “In the Large” (2021)
Journal Article
Swan, J., Adriaensen, S., Johnson, C. G., Kheiri, A., Krawiec, F., Merelo, J. J., Minku, L. L., Özcan, E., Pappa, G. L., García-Sánchez, P., Sörensen, K., Voß, S., Wagner, M., & White, D. R. (2022). Metaheuristics “In the Large”. European Journal of Operational Research, 297(2), 393-406. https://doi.org/10.1016/j.ejor.2021.05.042

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need... Read More about Metaheuristics “In the Large”.

Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS (2021)
Journal Article
Deveci, M., Özcan, E., John, R., Pamucar, D., & Karaman, H. (2021). Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS. Applied Soft Computing, 109, Article 107532. https://doi.org/10.1016/j.asoc.2021.107532

Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electr... Read More about Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS.

L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout (2021)
Journal Article
Song, H., Torres Torres, M., Özcan, E., & Triguero, I. (2021). L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout. Neurocomputing, 442, 200-208. https://doi.org/10.1016/j.neucom.2021.02.024

Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A successful approach to tackle this problem is to compare the similarity between examples in a learned metric space based on convolutional neural network... Read More about L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout.

Mixed Integer Linear Programming formulations of the stacking sequence and blending optimisation of composite structures (2021)
Journal Article
Ntourmas, G., Glock, F., Daoud, F., Schuhmacher, G., Chronopoulos, D., & Özcan, E. (2021). Mixed Integer Linear Programming formulations of the stacking sequence and blending optimisation of composite structures. Composite Structures, 264, 113660. https://doi.org/10.1016/j.compstruct.2021.113660

This manuscript proposes two novel formulations for the manufacturable stacking sequence retrieval of laminated composite structures. Detailed sizing of composite structures is commonly tackled by a two-stage optimisation approach, the first stage be... Read More about Mixed Integer Linear Programming formulations of the stacking sequence and blending optimisation of composite structures.

Many-objective Optimisation for an Integrated Supply Chain Management Problem (2021)
Book Chapter
Türk, S., Özcan, E., & John, R. (2021). Many-objective Optimisation for an Integrated Supply Chain Management Problem. In R. Matoušek, & J. Kůdela (Eds.), Recent Advances in Soft Computing and Cybernetics (97-111). Springer Nature. https://doi.org/10.1007/978-3-030-61659-5_9

Due to the complexity of the supply chain with multiple conflicting objectives requiring a search for a set of trade-off solutions, there has been a range of studies applying multi-objective methods. In recent years, there has been a growing interest... Read More about Many-objective Optimisation for an Integrated Supply Chain Management Problem.

Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance (2020)
Journal Article
Santiago Júnior, V. A. D., Özcan, E., & Carvalho, V. R. D. (2020). Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance. Applied Soft Computing, 97(Part A), Article 106760. https://doi.org/10.1016/j.asoc.2020.106760

In this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement Learn... Read More about Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance.

Acoustic topology optimisation using CMA-ES (2020)
Presentation / Conference Contribution
Ramamoorthy, V. T., Ozcan, E., Parkes, A., Sreekumar, A., Jaouen, L., & Becot, F. (2020, September). Acoustic topology optimisation using CMA-ES. Presented at ISMA-USD 2020 - International Conference on Noise and Vibration Engineering, Leuven, Belgium

Structural topology optimisation techniques are increasingly being applied to acoustic materials. Most acoustic topology optimisation applications use the solid-isotropic-material-with-penalization (SIMP) approach [1]–[4] which is a derivative-based... Read More about Acoustic topology optimisation using CMA-ES.