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Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation (2016)
Journal Article
Li, W., Özcan, E., & John, R. (2017). Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation. Renewable Energy, 105, https://doi.org/10.1016/j.renene.2016.12.022

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. Ho... Read More about Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation.

A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts (2016)
Conference Proceeding
Alqahtani, S. M., & John, R. (2016). A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts

The use of Internet has been increasing day by day and the internet traffic is exponentially increasing. The services providers such as web services providers, email services providers, and cloud service providers have to deal with mi... Read More about A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts.

A comparative study on the control of Quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers (2016)
Conference Proceeding
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (in press). A comparative study on the control of Quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers

Fuzzy logic controllers (FLCs) have extensively been used for the autonomous control and guidance of unmanned aerial vehicles (UAVs) due to their capability of handling uncertainties and delivering adequate control without the need for a precise, mat... Read More about A comparative study on the control of Quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers.

Evaluating decision making units under uncertainty using fuzzy multi-objective nonlinear programming (2016)
Journal Article
Zerafat Angiz, M., Nawawi, M., Khalid, R., Mustafa, A., Emrouznejad, A., John, R., & Kendall, G. (in press). Evaluating decision making units under uncertainty using fuzzy multi-objective nonlinear programming. Information Systems and Operational Research, 55(1), https://doi.org/10.1080/03155986.2016.1240944

This paper proposes a new method to evaluate Decision Making Units (DMUs) under uncertainty using fuzzy Data Envelopment Analysis (DEA). In the proposed multi-objective nonlinear programming methodology both the objective functions and the constraint... Read More about Evaluating decision making units under uncertainty using fuzzy multi-objective nonlinear programming.

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction (2016)
Conference Proceeding
Eyoh, I., John, R., & de Maere, G. (2016). Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

This paper presents an approach to prediction based on a new interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference. The gradient descent algorithm (GDA) is used to adapt the parame- ters of the IT2IFL... Read More about Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction.

Interval type–2 fuzzy decision making (2016)
Journal Article
Runkler, T., Coupland, S., & John, R. (2017). Interval type–2 fuzzy decision making. International Journal of Approximate Reasoning, 80, https://doi.org/10.1016/j.ijar.2016.09.007

This paper concerns itself with decision making under uncertainty and theconsideration of risk. Type-1 fuzzy logic by its (essentially) crisp nature is limited in modelling decision making as there is no uncertainty in the membership function. We are... Read More about Interval type–2 fuzzy decision making.

An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models (2016)
Conference Proceeding
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2016). An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

In this paper, an extended ANFIS architecture is proposed. By incorporating an extra layer for the fuzzification process, the extended architecture is able to fit both type-1 and interval type-2 models. The learning properties of the proposed archite... Read More about An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models.

A similarity-based inference engine for non-singleton fuzzy logic systems (2016)
Conference Proceeding
Wagner, C., Pourabdollah, A., McCulloch, J., John, R., & Garibaldi, J. M. (2016). A similarity-based inference engine for non-singleton fuzzy logic systems. https://doi.org/10.1109/FUZZ-IEEE.2016.7737703

In non-singleton fuzzy logic systems (NSFLSs) input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty such as sensor noise. The performance of NSFLSs in handling such uncertainties depends both on the actual input... Read More about A similarity-based inference engine for non-singleton fuzzy logic systems.

Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice (2016)
Journal Article
Almaraashia, M., John, R., Hopgood, A., & Ahmadi, S. (2016). Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice. Information Sciences, 360, https://doi.org/10.1016/j.ins.2016.03.047

This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval... Read More about Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice.

Quantification of R-Fuzzy sets (2016)
Journal Article
Singh Khuman, A., Yang, Y., & John, R. (2016). Quantification of R-Fuzzy sets. Expert Systems with Applications, 55, https://doi.org/10.1016/j.eswa.2016.02.010

The main aim of this paper is to connect R-Fuzzy sets and type-2 fuzzy sets, so as to provide a practical means to express complex uncertainty without the associated difficulty of a type-2 fuzzy set. The paper puts forward a significance measure, to... Read More about Quantification of R-Fuzzy sets.

Good Laboratory Practice for optimization research (2015)
Journal Article
Kendall, G., Bai, R., Blazewicz, J., De Causmaecker, P., Gendreau, M., John, R., …Yee, A. (2016). Good Laboratory Practice for optimization research. Journal of the Operational Research Society, 67(4), https://doi.org/10.1057/jors.2015.77

Good Laboratory Practice has been a part of non-clinical research for over 40 years yet. Optimization Research, despite having many papers discussing standards being published over the same period of time, has yet to embrace standards that underpin i... Read More about Good Laboratory Practice for optimization research.

Computing Nash equilibria and evolutionarily stable states of evolutionary games (2015)
Journal Article
Li, J., Kendall, G., & John, R. (in press). Computing Nash equilibria and evolutionarily stable states of evolutionary games. IEEE Transactions on Evolutionary Computation, 20(3), doi:10.1109/TEVC.2015.2490076

Stability analysis is an important research direction in evolutionary game theory. Evolutionarily stable states have a close relationship with Nash equilibria of repeated games, which are characterized by the folk theorem. When applying the folk theo... Read More about Computing Nash equilibria and evolutionarily stable states of evolutionary games.

A data analysis framework to rank HGV drivers (2015)
Conference Proceeding
Figueredo, G. P., Quinlan, P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2015). A data analysis framework to rank HGV drivers

We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top... Read More about A data analysis framework to rank HGV drivers.

Fuzzy multi-criteria decision making for carbon dioxide geological storage in Turkey (2015)
Journal Article
Deveci, M., Çetin Demirel, N., John, R., & Özcan, E. (in press). Fuzzy multi-criteria decision making for carbon dioxide geological storage in Turkey. Journal of Natural Gas Science and Engineering, 27(2), https://doi.org/10.1016/j.jngse.2015.09.004

The problem of choosing the best location for CO2 storage is a crucial and challenging multi-criteria decision problem for some companies. This study compares the performance of three fuzzy-based multi-criteria decision making (MCDM) methods, includi... Read More about Fuzzy multi-criteria decision making for carbon dioxide geological storage in Turkey.

Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing (2015)
Conference Proceeding
Tyasnurita, R., Özcan, E., Shahriar, A., & John, R. (2015). Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing

A hyper-heuristic is a heuristic optimisation method which generates or selects heuristics (move operators) based on a set of components while solving a computationally difficult problem. Apprenticeship learning arises while observing the behavior of... Read More about Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing.

A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation (2015)
Conference Proceeding
He, F., Qu, R., & John, R. (2015). A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation

In this paper we investigate a multi-objective portfolio selection model with three criteria: risk, return and liquidity for investors. Non-probabilistic uncertainty factors in the market, such as imprecision and vagueness of investors’ preference an... Read More about A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation.

A simulated annealing approach to supplier selection aware inventory planning (2015)
Conference Proceeding
Turk, S., Miller, S., Özcan, E., & John, R. (2015). A simulated annealing approach to supplier selection aware inventory planning. In 2015 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2015.7257105

Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. Also, appropriate inventory management is critical to the success of a supply chain operation. In recent years, there has been a gro... Read More about A simulated annealing approach to supplier selection aware inventory planning.

Fuzzy adaptive parameter control of a late acceptance hyper-heuristic (2014)
Conference Proceeding
Jackson, W. G., Özcan, E., & John, R. I. (2014). Fuzzy adaptive parameter control of a late acceptance hyper-heuristic. In 2014 14th UK Workshop on Computational Intelligence (UKCI)

A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies on two core components, a method for selecting a heuristic to apply at a given point, and a method to decide whether or not to accept the result of t... Read More about Fuzzy adaptive parameter control of a late acceptance hyper-heuristic.


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