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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., …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.

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.

Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations (2020)
Journal Article
Türk, S., Deveci, M., Özcan, E., Canıtez, F., & John, R. (2021). Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations. Information Sciences, 547, 641-666. https://doi.org/10.1016/j.ins.2020.08.076

Electric vehicles are the key to facilitating the transition to low-carbon ‘green’ transport. However, there are concerns with their range and the location of the charging stations which delay a full-fledged adoption of their use. Hence, the electric... Read More about Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations.

A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method (2020)
Journal Article
Deveci, M., Özcan, E., John, R., Covrig, C., & Pamucar, D. (2020). A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method. Journal of Environmental Management, 270, Article 110916. https://doi.org/10.1016/j.jenvman.2020.110916

This study investigates the degree of importance of criteria affecting the optimal site selection of offshore wind farms. Firstly, forty two different influential criteria have been selected by reviewing the scientific literature on offshore wind far... Read More about A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method.

Recent advances in selection hyper-heuristics (2019)
Journal Article
Drake, J. H., Kheiri, A., Özcan, E., & Burke, E. K. (2020). Recent advances in selection hyper-heuristics. European Journal of Operational Research, 285(2), 405-428. https://doi.org/10.1016/j.ejor.2019.07.073

Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class o... Read More about Recent advances in selection hyper-heuristics.

A review on the self and dual interactions between machine learning and optimisation (2019)
Journal Article
Song, H., Triguero, I., & Özcan, E. (2019). A review on the self and dual interactions between machine learning and optimisation. Progress in Artificial Intelligence, 8(2), 143–165. https://doi.org/10.1007/s13748-019-00185-z

Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from... Read More about A review on the self and dual interactions between machine learning and optimisation.

Move acceptance in local search metaheuristics for cross-domain search (2018)
Journal Article
Jackson, W. G., Özcan, E., & John, R. I. (2018). Move acceptance in local search metaheuristics for cross-domain search. Expert Systems with Applications, 131, https://doi.org/10.1016/j.eswa.2018.05.006

Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and have been successfully applied to a range of computationally hard real-world problems. Local search metaheuristics operate under a single-point based s... Read More about Move acceptance in local search metaheuristics for cross-domain search.

Evolutionary computation for wind farm layout optimization (2018)
Journal Article
Wilson, D., Rodrigues, S., Segura, C., Loshchilov, I., Huttor, F., Buenfil, G. L., …Sylvain, C. (2018). Evolutionary computation for wind farm layout optimization. Renewable Energy, 126, https://doi.org/10.1016/j.renene.2018.03.052

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with... Read More about Evolutionary computation for wind farm layout optimization.

Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey (2018)
Journal Article
Deveci, M., Özcan, E., John, R., & Öner, S. C. (2018). Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey. Journal of Air Transport Management, 69, https://doi.org/10.1016/j.jairtraman.2018.01.008

This study investigates the level of service quality of domestic airlines in Turkey travelling between Istanbul and London and compares those airline companies according to a set of predetermined criteria. A practical multi-criteria decision making a... Read More about Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey.

To kit or not to kit: analysing the value of model-based kitting for additive manufacturing (2018)
Journal Article
Khajavi, S. H., Baumers, M., Holmström, J., Özcan, E., Atkin, J., Jackson, W. G., & Li, W. (2018). To kit or not to kit: analysing the value of model-based kitting for additive manufacturing. Computers in Industry, 98, https://doi.org/10.1016/j.compind.2018.01.022

The use of additive manufacturing (AM) for the production of functional parts is increasing. Thus, AM based practices that can reduce supply chain costs gain in importance. We take a forward-looking approach and study how AM can be used more effectiv... Read More about To kit or not to kit: analysing the value of model-based kitting for additive manufacturing.

A Learning Automata-Based Multiobjective Hyper-Heuristic (2017)
Journal Article
Li, W., Özcan, E., & John, R. (2019). A Learning Automata-Based Multiobjective Hyper-Heuristic. IEEE Transactions on Evolutionary Computation, 23(1), 59-73. https://doi.org/10.1109/TEVC.2017.2785346

© 1997-2012 IEEE. Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimization problems. However, once implemented, their application to a new problem domain or a slight... Read More about A Learning Automata-Based Multiobjective Hyper-Heuristic.

A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming (2017)
Journal Article
Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (in press). A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Applied Soft Computing, 62, https://doi.org/10.1016/j.asoc.2017.10.002

Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy,... Read More about A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming.

Automated generation of constructive ordering heuristics for educational timetabling (2017)
Journal Article
Pillay, N., & Özcan, E. (2017). Automated generation of constructive ordering heuristics for educational timetabling. Annals of Operations Research, 275, 181-208. https://doi.org/10.1007/s10479-017-2625-x

Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For... Read More about Automated generation of constructive ordering heuristics for educational timetabling.

Sparse, continuous policy representations for uniform online bin packing via regression of interpolants (2017)
Journal Article
Swan, J., Drake, J. H., Neumann, G., & Özcan, E. (2017). Sparse, continuous policy representations for uniform online bin packing via regression of interpolants. Lecture Notes in Artificial Intelligence, 10197, 189-200. https://doi.org/10.1007/978-3-319-55453-2_13

Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation o... Read More about Sparse, continuous policy representations for uniform online bin packing via regression of interpolants.

Multi-objective optimisation in inventory planning with supplier selection (2017)
Journal Article
Turk, S., Özcan, E., & John, R. (2017). Multi-objective optimisation in inventory planning with supplier selection. Expert Systems with Applications, 78, https://doi.org/10.1016/j.eswa.2017.02.014

Supplier selection and inventory planning are critical and challenging tasks in Supply Chain Management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this study, we pre... Read More about Multi-objective optimisation in inventory planning with supplier selection.