All Outputs (5)

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)
Conference Proceeding
Ramamoorthy, V. T., Ozcan, E., Parkes, A., Sreekumar, A., Jaouen, L., & Becot, F. (2020). Acoustic topology optimisation using CMA-ES. In Proceedings of ISMA2020 International Conference on Noise and Vibration Engineering: USD2020 International Conference on Uncertainty in Structural Dynamics

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.

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.

Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem (2020)
Conference Proceeding
Garza-Santisteban, F., Amaya, I., Cruz-Duarte, J., Ortiz-Bayliss, J. C., Ozcan, E., & Terashima-Marin, H. (2020). Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem. In 2020 IEEE Congress on Evolutionary Computation (CEC) (1-8). https://doi.org/10.1109/CEC48606.2020.9185709

This study presents an offline learning Simulated Annealing approach to generate a constructive hyper-heuristic evaluated through training and testing on a set of instances for solving the Job-Shop Scheduling problem. The generated hyperheuristic use... Read More about Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem.

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.