Joy, G.; Huyck, C.; Yang, X.

Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods

Conference paper

Joy, G., Huyck, C. and Yang, X. 2024. Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods. Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V., Dongarra, J. and Sloot, P. (ed.) 24th International Conference on Computational Science. Malaga, Spain 02 - 04 Jul 2024 Cham Springer. pp. 242–253 https://doi.org/10.1007/978-3-031-63775-9_17

ISBN
Type	Conference paper
Title	Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods
Authors	Joy, G., Huyck, C. and Yang, X.
Abstract	Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure that the algorithm used for optimization performs well and is sufficiently robust for solving different types of optimization problems. In this study, the Firefly Algorithm (FA) is used to evaluate the influence of its parameter values on its efficiency. Parameter values are randomly initialized using both the standard Monte Carlo method and the Quasi Monte-Carlo method. The values are then used for tuning the FA. Two benchmark functions and a spring design problem are used to test the robustness of the tuned FA. From the preliminary findings, it can be deduced that both the Monte Carlo method and Quasi-Monte Carlo method produce similar results in terms of optimal fitness values. Numerical experiments using the two different methods on both benchmark functions and the spring design problem showed no major variations in the final fitness values, irrespective of the different sample values selected during the simulations. This insensitivity indicates the robustness of the FA.
Keywords	Algorithm; Firefly algorithm; Parameter tuning; Monte Carlo; Quasi-Monte Carlo
Sustainable Development Goals	9 Industry, innovation and infrastructure
Middlesex University Theme	Creativity, Culture & Enterprise
Research Group	Artificial Intelligence group
Conference	24th International Conference on Computational Science
Page range	242–253
Proceedings Title	Computational Science – ICCS 2024: 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part V
Series	Lecture Notes in Computer Science
Editors	Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V., Dongarra, J. and Sloot, P.
ISSN	0302-9743
Electronic	1611-3349
Paperback	9783031637742
Electronic	9783031637759
Publisher	Springer
Place of publication	Cham
Publication dates
Online	28 Jun 2024
Publication process dates
Accepted	May 2024
Deposited	02 Jul 2024
Output status	Published
Accepted author manuscript	File Access Level Open
Copyright Statement	This version of the paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-ma...), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-63775-9_17
Digital Object Identifier (DOI)	https://doi.org/10.1007/978-3-031-63775-9_17
Web of Science identifier	WOS:001279327300017
Web address (URL) of conference proceedings	https://doi.org/10.1007/978-3-031-63775-9
Language	English

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Mitchell, I., Huyck, C. and Evans, C. 2016. PlaNeural: spiking neural networks that plan. 7th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2016. New York City, NY, USA 16 Jul 2016 Elsevier. pp. 198-204 https://doi.org/10.1016/j.procs.2016.07.425

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Sharma, H., Bansal, J., Arya, K. and Yang, X. 2016. Lévy flight artificial bee colony algorithm. International Journal of Systems Science. 47 (11), pp. 2652-2670. https://doi.org/10.1080/00207721.2015.1010748

A Physarum-inspired approach to supply chain network design

Zhang, X., Adamatzky, A., Yang, X., Yang, H., Mahadevan, S. and Deng, Y. 2016. A Physarum-inspired approach to supply chain network design. SCIENCE CHINA Information Sciences. 59 (5). https://doi.org/10.1007/s11432-015-5417-4

Stochastic decision-making in waste management using a firefly algorithm-driven simulation-optimization approach for generating alternatives

Imanirad, R., Yang, X. and Yeomans, J. 2016. Stochastic decision-making in waste management using a firefly algorithm-driven simulation-optimization approach for generating alternatives. in: Koziel, S., Leifsson, L. and Yang, X. (ed.) Simulation-Driven Modeling and Optimization: ASDOM, Reykjavik, August 2014 Springer.

Economic dispatch using chaotic bat algorithm

Adarsh, B., Raghunathan, T., Jayabarathi, T. and Yang, X. 2016. Economic dispatch using chaotic bat algorithm. Energy. 96, pp. 666-675. https://doi.org/10.1016/j.energy.2015.12.096

A novel approach for multispectral satellite image classification based on the bat algorithm

Senthilnath, J., Kulkarni, S., Benediktsson, J. and Yang, X. 2016. A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geoscience and Remote Sensing Letters. 13 (4), pp. 599-603. https://doi.org/10.1109/LGRS.2016.2530724

Mathematical analysis of energy efficiency optimality in multi-user OFDM systems

Chien, S., Ting, T., Yang, X. and Takahashi, K. 2016. Mathematical analysis of energy efficiency optimality in multi-user OFDM systems. Wireless Communications and Mobile Computing. 16 (3), pp. 252-263. https://doi.org/10.1002/wcm.2516

An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems

Osaba, E., Yang, X., Diaz, F., Lopez-Garcia, P. and Carballedo, R. 2016. An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Engineering Applications of Artificial Intelligence. 48, pp. 59-71. https://doi.org/10.1016/j.engappai.2015.10.006

Advancing ambient assisted living with caution

Huyck, C., Augusto, J., Gao, X. and Botia, J. 2015. Advancing ambient assisted living with caution. in: Helfert, M., Holzinger, A., Ziefle, M., Fred, A., O'Donoghue, J. and Röcker, C. (ed.) Information and Communication Technologies for Ageing Well and e-Health: First International Conference, ICT4AgeingWell 2015, Lisbon, Portugal, May 20-22, 2015. Revised Selected Papers Springer.

Neural constraints and flexibility in language processing

Huyck, C. 2016. Neural constraints and flexibility in language processing. Behavioral and Brain Sciences: An International Journal of Current Research and Theory with Open Peer Commentary. 39, p. e78. https://doi.org/10.1017/s0140525x15000837

Navigability analysis of magnetic map with projecting pursuit-based selection method by using firefly algorithm

Ma, Y., Zhao, Y., Wu, L., He, Y. and Yang, X. 2015. Navigability analysis of magnetic map with projecting pursuit-based selection method by using firefly algorithm. Neurocomputing. 159, pp. 288-297. https://doi.org/10.1016/j.neucom.2015.01.028

Hybrid metaheuristic algorithms: past, present, and future

Ting, T., Yang, X., Cheng, S. and Huang, K. 2014. Hybrid metaheuristic algorithms: past, present, and future. in: Recent Advances in Swarm Intelligence and Evolutionary Computation Springer.

Self organising maps with a point neuron model

Huyck, C. and Mitchell, I. 2013. Self organising maps with a point neuron model. Intl Conf. on Cognitive and Neural Systems.

Binary flower pollination algorithm and its application to feature selection

Rodrigues, D., Yang, X., De Souza, A. and Papa, J. 2015. Binary flower pollination algorithm and its application to feature selection. in: Yang, X. (ed.) Recent advances in swarm intelligence and evolutionary computation Springer.

Computational intelligence and metaheuristic algorithms with applications

Yang, X., Chien, S. and Ting, T. 2014. Computational intelligence and metaheuristic algorithms with applications. The Scientific World Journal. 2014, pp. 1-4. https://doi.org/10.1155/2014/425853

Discrete Cuckoo search applied to job shop scheduling problem

Ouaarab, A., Ahiod, B. and Yang, X. 2015. Discrete Cuckoo search applied to job shop scheduling problem. in: Yang, X. (ed.) Recent advances in swarm intelligence and evolutionary computation Springer.

Swarm intelligence and evolutionary computation: overview and analysis

Yang, X. and He, X. 2015. Swarm intelligence and evolutionary computation: overview and analysis. in: Yang, X. (ed.) Recent advances in swarm intelligence and evolutionary computation Springer.

Adaptation and hybridization in nature-inspired algorithms

Fister, I., Strnad, D., Yang, X. and Fister, I. 2015. Adaptation and hybridization in nature-inspired algorithms. in: Fister, I. and Fister, I. (ed.) Adaptation and Hybridization in Computational Intelligence Springer.

Solutions of non-smooth economic dispatch problems by swarm intelligence

Hosseini, S., Yang, X., Gandomi, A. and Nemati, A. 2015. Solutions of non-smooth economic dispatch problems by swarm intelligence. in: Fister, I. and Fister, I. (ed.) Adaptation and Hybridization in Computational Intelligence Springer.

A short discussion about economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm

Fister, I., Fister, I. and Yang, X. 2015. A short discussion about economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm. Applied Thermal Engineering. 76, pp. 535-537. https://doi.org/10.1016/j.applthermaleng.2014.11.009

Feature selection in life science classification: metaheuristic swarm search

Fong, S., Deb, S., Yang, X. and Li, J. 2014. Feature selection in life science classification: metaheuristic swarm search. IT Professional. 16 (4), pp. 24-29. https://doi.org/10.1109/MITP.2014.50

Diversity and mechanisms in swarm intelligence

Yang, X. 2014. Diversity and mechanisms in swarm intelligence. International Journal of Swarm Intelligence Research. 5 (2), pp. 1-12. https://doi.org/10.4018/ijsir.2014040101

Swarm intelligence based algorithms: a critical analysis

Yang, X. 2014. Swarm intelligence based algorithms: a critical analysis. Evolutionary Intelligence. 7 (1), pp. 17-28. https://doi.org/10.1007/s12065-013-0102-2

A comparison of simple agents implemented in simulated neurons

Huyck, C., Evans, C. and Mitchell, I. 2015. A comparison of simple agents implemented in simulated neurons. Biologically Inspired Cognitive Architectures. 12, pp. 9-19. https://doi.org/10.1016/j.bica.2015.03.001

Programming the MIRTO robot with neurons

Huyck, C., Primiero, G. and Raimondi, F. 2014. Programming the MIRTO robot with neurons. Procedia Computer Science. 41, pp. 75-82. https://doi.org/10.1016/j.procs.2014.11.087

A neuro-computational approach to PP attachment ambiguity resolution

Nadh, K. and Huyck, C. 2012. A neuro-computational approach to PP attachment ambiguity resolution. Neural Computation. 24 (7), pp. 1906-1925. https://doi.org/10.1162/NECO_a_00298

A review of cell assemblies

Huyck, C. and Passmore, P. 2013. A review of cell assemblies. Biological Cybernetics. 107 (3), pp. 263-288. https://doi.org/10.1007/s00422-013-0555-5

Compensatory Hebbian learning for categorisation in simulated biological neural nets

Huyck, C. and Mitchell, I. 2013. Compensatory Hebbian learning for categorisation in simulated biological neural nets. Biologically Inspired Cognitive Architectures. 6 (5), pp. 3-7. https://doi.org/10.1016/j.bica.2013.06.003

Planning the sports training sessions with the bat algorithm

Fister, I., Rauter, S., Yang, X., Ljubič, K. and Fister, I. 2015. Planning the sports training sessions with the bat algorithm. Neurocomputing. 149, pp. 993-1002. https://doi.org/10.1016/j.neucom.2014.07.034

A short discussion about "Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm"

Fister, I. (Jr.), Fister, I. and Yang, X. 2015. A short discussion about "Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm". Applied Thermal Engineering. 76, pp. 535-537. https://doi.org/10.1016/j.applthermaleng.2014.11.009

Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions

De Rango, F., Palmieri, N., Yang, X. and Marano, S. 2015. Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions. SPECTS 2015 : International Symposium on Performance Evaluation of Computer and Telecommunication Systems. Chicago, IL, USA 26 - 29 Jul 2015 IEEE. pp. 1-8

Modified bat algorithm with quaternion representation

Fister, I., Brest, J., Fister, I. and Yang, X. 2015. Modified bat algorithm with quaternion representation. IEEE Congress on Evolutionary Computation (CEC 2015). Sendai, Japan 25 - 28 May 2015 IEEE. pp. 491-498 https://doi.org/10.1109/CEC.2015.7256930

A heuristic optimization method inspired by wolf preying behavior

Fong, S., Deb, S. and Yang, X. 2015. A heuristic optimization method inspired by wolf preying behavior. Neural Computing and Applications. 26 (7), pp. 1725-1738. https://doi.org/10.1007/s00521-015-1836-9

Sizing optimization of truss structures using flower pollination algorithm

Bekdaş, G., Nigdeli, S. and Yang, X. 2015. Sizing optimization of truss structures using flower pollination algorithm. Applied Soft Computing. 37, pp. 322-331. https://doi.org/10.1016/j.asoc.2015.08.037

Color image segmentation by cuckoo search

Nandy, S., Yang, X., Sarkar, P. and Das, A. 2015. Color image segmentation by cuckoo search. Intelligent Automation & Soft Computing: An International Journal . 21 (4), pp. 673-685.

Random-key cuckoo search for the travelling salesman problem

Ouaarab, A., Ahiod, B. and Yang, X. 2015. Random-key cuckoo search for the travelling salesman problem. Soft Computing. 19 (4), pp. 1099-1106. https://doi.org/10.1007/s00500-014-1322-9

A biologically inspired network design model

Zhang, X., Adamatzky, A., Chan, F., Deng, Y., Yang, H., Yang, X., Tsompanas, M., Sirakoulis, G. and Mahadevan, S. 2015. A biologically inspired network design model. Scientific Reports. 5 (1). https://doi.org/10.1038/srep10794

Optimum design of frame structures using the eagle strategy with differential evolution

Talatahari, S., Gandomi, A., Yang, X. and Deb, S. 2015. Optimum design of frame structures using the eagle strategy with differential evolution. Engineering Structures. 91, pp. 16-25. https://doi.org/10.1016/j.engstruct.2015.02.026

Analysis of randomisation methods in swarm intelligence

Fister, I. (Jr.), Yang, X., Brest, J., Fister, D. and Fister, I. 2015. Analysis of randomisation methods in swarm intelligence. International Journal of Bio-Inspired Computation. 7 (1), pp. 36-49. https://doi.org/10.1504/IJBIC.2015.067989

Analysis of quality-of-service aware orthogonal frequency division multiple access system considering energy efficiency

Ting, T., Yang, X., Lee, S. and Chien, S. 2014. Analysis of quality-of-service aware orthogonal frequency division multiple access system considering energy efficiency. IET Communications. 8 (11), pp. 1947-1954. https://doi.org/10.1049/iet-com.2013.1161

A novel improved accelerated particle swarm optimization algorithm for global numerical optimization

Wang, G., Hossein Gandomi, A., Yang, X. and Hossein Alavi, A. 2014. A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Engineering Computations. 31 (7), pp. 1198-1220. https://doi.org/10.1108/EC-10-2012-0232

Oil supply between OPEC and non-OPEC based on game theory

Chang, Y., Yi, J., Yan, W., Yang, X., Zhang, S., Gao, Y. and Wang, X. 2014. Oil supply between OPEC and non-OPEC based on game theory. International Journal of Systems Science. 45 (10), pp. 2127-2132. https://doi.org/10.1080/00207721.2012.762562

Advances of swarm intelligent systems in gene expression data classification

Talatahari, E., Talatahari, S., Gandomi, A. and Yang, X. 2014. Advances of swarm intelligent systems in gene expression data classification. Journal of Multiple-Valued Logic and Soft Computing. 22 (3), pp. 307-315.

Bat algorithm is better than intermittent search strategy

Yang, X., Deb, S. and Fong, S. 2014. Bat algorithm is better than intermittent search strategy. Journal of Multiple-Valued Logic and Soft Computing. 22 (3), pp. 223-237.

Nature-inspired optimization algorithms

Yang, X. 2014. Nature-inspired optimization algorithms. Elsevier.

Non-dominated sorting cuckoo search for multiobjective optimization

He, X., Li, N. and Yang, X. 2014. Non-dominated sorting cuckoo search for multiobjective optimization. 2014 IEEE Symposium on Swarm Intelligence (SIS). Orlando, Florida 09 - 12 Dec 2014 IEEE. pp. 1-7 https://doi.org/10.1109/SIS.2014.7011772

Computational optimization, modelling and simulation: past, present and future

Yang, X., Koziel, S. and Leifsson, L. 2014. Computational optimization, modelling and simulation: past, present and future. 5th workshop on Computational Optimization, Modelling and Simulations (COMS 2014) at the ICCS 2014. Cairns, Australia 10 - 12 Jun 2014 Elsevier. pp. 754-758 https://doi.org/10.1016/j.procs.2014.05.067

Discrete cuckoo search algorithm for job shop scheduling problem

Ouaarab, A., Ahiod, B., Yang, X. and Abbad, M. 2014. Discrete cuckoo search algorithm for job shop scheduling problem. 2014 IEEE International Symposium on Intelligent Control (ISIC). France 08 - 10 Oct 2014 IEEE. pp. 1872-1876 https://doi.org/10.1109/ISIC.2014.6967636

Nature-inspired framework for hyperspectral band selection

Nakamura, R., Garcia Fonseca, L., Dos Santos, J., Da S. Torres, R., Yang, X. and Papa, J. 2014. Nature-inspired framework for hyperspectral band selection. IEEE Transactions on Geoscience and Remote Sensing. 52 (4), pp. 2126-2137. https://doi.org/10.1109/TGRS.2013.2258351

A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems

Marichelvam, M., Prabaharan, T. and Yang, X. 2014. A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Transactions on Evolutionary Computation. 18 (2), pp. 301-305. https://doi.org/10.1109/TEVC.2013.2240304

Binary bat algorithm

Mirjalili, S., Mirjalili, S. and Yang, X. 2014. Binary bat algorithm. Neural Computing and Applications. 25 (3-4), pp. 663-681. https://doi.org/10.1007/s00521-013-1525-5

Bat algorithm based on simulated annealing and Gaussian perturbations

He, X., Ding, W. and Yang, X. 2014. Bat algorithm based on simulated annealing and Gaussian perturbations. Neural Computing and Applications. 25 (2), pp. 459-468. https://doi.org/10.1007/s00521-013-1518-4

Discrete cuckoo search algorithm for the travelling salesman problem

Ouaarab, A., Ahiod, B. and Yang, X. 2014. Discrete cuckoo search algorithm for the travelling salesman problem. Neural Computing and Applications. 24 (7-8), pp. 1659-1669. https://doi.org/10.1007/s00521-013-1402-2

Cuckoo search: recent advances and applications

Yang, X. and Deb, S. 2014. Cuckoo search: recent advances and applications. Neural Computing and Applications. 24 (1), pp. 169-174. https://doi.org/10.1007/s00521-013-1367-1

Chaotic bat algorithm

Gandomi, A. and Yang, X. 2014. Chaotic bat algorithm. Journal of Computational Science. 5 (2), pp. 224-232. https://doi.org/10.1016/j.jocs.2013.10.002

Mathematical modelling and parameter optimization of pulsating heat pipes

Yang, X., Karamanoglu, M., Luan, T. and Koziel, S. 2014. Mathematical modelling and parameter optimization of pulsating heat pipes. Journal of Computational Science. 5 (2), pp. 119-125. https://doi.org/10.1016/j.jocs.2013.12.003

An empirical study of test effort estimation based on bat algorithm

Srivastava, P., Bidwai, A., Khan, A., Rathore, K., Sharma, R. and Yang, X. 2014. An empirical study of test effort estimation based on bat algorithm. International Journal of Bio-Inspired Computation. 6 (1), pp. 57-70. https://doi.org/10.1504/IJBIC.2014.059966

Bio-inspired computation: success and challenges of IJBIC

Yang, X. and Cui, Z. 2014. Bio-inspired computation: success and challenges of IJBIC. International Journal of Bio-Inspired Computation. 6 (1), pp. 1-6. https://doi.org/10.1504/IJBIC.2014.059969

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Rodrigues, D., Pereira, L., Nakamura, R., Costa, K., Yang, X., Souza, A. and Papa, J. 2014. A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest. Expert Systems with Applications. 41 (5), pp. 2250-2258. https://doi.org/10.1016/j.eswa.2013.09.023

Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

Marichelvam, M., Prabaharan, T. and Yang, X. 2014. Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Applied Soft Computing. 19, pp. 93-101. https://doi.org/10.1016/j.asoc.2014.02.005

A bio-inspired algorithm for identification of critical components in the transportation networks

Zhang, X., Adamatzky, A., Yang, H., Mahadaven, S., Yang, X., Wang, Q. and Deng, Y. 2014. A bio-inspired algorithm for identification of critical components in the transportation networks. Applied Mathematics and Computation. 248, pp. 18-27. https://doi.org/10.1016/j.amc.2014.09.055

Post and pre-compensatory Hebbian Learning for categorisation

Huyck, C. and Mitchell, I. 2014. Post and pre-compensatory Hebbian Learning for categorisation. Cognitive Neurodynamics. 8 (4), pp. 299-311. https://doi.org/10.1007/s11571-014-9282-4

A firefly-inspired method for protein structure prediction in lattice models

Maher, B., Albrecht, A., Loomes, M., Yang, X. and Steinhofel, K. 2014. A firefly-inspired method for protein structure prediction in lattice models. Biomolecules. 4 (1), pp. 56-75. https://doi.org/10.3390/biom4010056

True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms

Yang, X., Huyck, C., Karamanoglu, M. and Khan, N. 2013. True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms. International Journal of Bio-Inspired Computation. 5 (6), pp. 329-335. https://doi.org/10.1504/IJBIC.2013.058910

Applications and analysis of bio-inspired eagle strategy for engineering optimization

Yang, X., Karamanoglu, M., Ting, T. and Zhao, Y. 2014. Applications and analysis of bio-inspired eagle strategy for engineering optimization. Neural Computing and Applications. 25 (2), pp. 411-420. https://doi.org/10.1007/s00521-013-1508-6

Flower pollination algorithm: a novel approach for multiobjective optimization

Yang, X., Karamanoglu, M. and He, X. 2014. Flower pollination algorithm: a novel approach for multiobjective optimization. Engineering Optimization. 46 (9), pp. 1222-1237. https://doi.org/10.1080/0305215X.2013.832237

Random walks, Lévy flights, Markov chains and metaheuristic optimization

Yang, X., Ting, T. and Karamanoglu, M. 2013. Random walks, Lévy flights, Markov chains and metaheuristic optimization. in: Future information communication technology and applications: ICFICE 2013 Springer Netherlands.

Are motorways rational from slime mould's point of view?

Adamatzky, A., Akl, S., Alonso-Sanz, R., Van Dessel, W., Ibrahim, Z., Ilachinski, A., Jones, J., Kayem, A., Martínez, G., De Oliveira, P., Prokopenko, M., Schubert, T., Sloot, P., Strano, E. and Yang, X. 2013. Are motorways rational from slime mould's point of view? International Journal of Parallel, Emergent and Distributed Systems. 28 (3), pp. 230-248. https://doi.org/10.1080/17445760.2012.685884

Advances in simulation-driven optimization and modelling

Koziel, S., Leifsson, L. and Yang, X. 2012. Advances in simulation-driven optimization and modelling. Journal of Computational Methods in Science and Engineering. 12 (1-2), pp. 1-4. https://doi.org/10.3233/JCM-2012-0400

Metaheuristic algorithms for self-organizing systems: a tutorial

Yang, X. 2012. Metaheuristic algorithms for self-organizing systems: a tutorial. in: Self-Adaptive and Self-Organizing Systems (SASO), 2012 IEEE Sixth International Conference on IEEE Conference Publications. pp. 249 -250

Integrating nature-inspired optimization algorithms to K-means clustering

Tang, R., Fong, S., Yang, X. and Deb, S. 2012. Integrating nature-inspired optimization algorithms to K-means clustering. in: Seventh International Conference on Digital Information Management (ICDIM), IEEE Conference Publications. pp. 116-123

Wolf search algorithm with ephemeral memory

Rui, T., Fong, S., Yang, X. and Deb, S. 2012. Wolf search algorithm with ephemeral memory. in: Seventh International Conference on Digital Information Management (ICDIM), 2012 IEEE Conference Publications. pp. 165 -172

Metaheuristic applications in structures and infrastructures

Gandom, A., Yang, X., Talatahari, S. and Alavi, A. 2013. Metaheuristic applications in structures and infrastructures. Elsevier.

Multi-objective flower algorithm for optimization

Yang, X., Karamanoglu, M. and He, X. 2013. Multi-objective flower algorithm for optimization. 2013 International Conference on Computational Science . Barcelona, Spain. 05 - 07 Jun 2013 Elsevier. pp. 861- 868 https://doi.org/10.1016/j.procs.2013.05.251

Optimal test sequence generation using firefly algorithm

Srivatsava, P., Mallikarjun, B. and Yang, X. 2013. Optimal test sequence generation using firefly algorithm. Swarm and Evolutionary Computation. 8, pp. 44-53. https://doi.org/10.1016/j.swevo.2012.08.003

Multiobjective firefly algorithm for continuous optimization

Yang, X. 2013. Multiobjective firefly algorithm for continuous optimization. Engineering with Computers. 29 (2), pp. 175-184. https://doi.org/10.1007/s00366-012-0254-1

Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

Gandomi, A., Yang, X. and Alavi, A. 2013. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers. 29 (1), pp. 17-35. https://doi.org/10.1007/s00366-011-0241-y

Multiobjective cuckoo search for design optimization

Yang, X. and Deb, S. 2013. Multiobjective cuckoo search for design optimization. Computers and Operations Research. 40 (6), pp. 1616-1624. https://doi.org/10.1016/j.cor.2011.09.026

Flower pollination algorithm for global optimization

Yang, X. 2012. Flower pollination algorithm for global optimization. in: Unconventional Computation and Natural Computation: 11th International Conference, UCNC 2012, Orléan, France, September 3-7, 2012. Proceedings Berlin Springer.

Parameter estimation from laser flash experiment data

Wright, L., Yang, X., Matthews, C., Chapman, L. and Roberts, S. 2011. Parameter estimation from laser flash experiment data. in: Computational optimization and applications in engineering and industry studies in computational intelligence Berlin Springer.

Benchmark problems in structural optimization

Gandomi, A. and Yang, X. 2011. Benchmark problems in structural optimization. in: Koziel, S. and Yang, X. (ed.) Computational optimization, methods and algorithms Springer.

Computational optimization: an overview

Yang, X. and Koziel, S. 2011. Computational optimization: an overview. in: Computational Optimization, Methods and Algorithms Springer.

Engineering optimisation by cuckoo search

Yang, X. and Deb, S. 2010. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation. 1 (4), pp. 330 -343. https://doi.org/10.1504/IJMMNO.2010.03543

Engineering optimization: an introduction with metaheuristic applications

Yang, X. 2010. Engineering optimization: an introduction with metaheuristic applications. New Jersey John Wiley & Sons.

Metaheuristics in water, geotechnical and transport engineering

Yang, X., Gandomi, A., Talatahari, S. and Alavi, A. 2012. Metaheuristics in water, geotechnical and transport engineering. Elsevier.

Bat algorithm for topology optimization in microelectronic applications

Yang, X., Karamanoglu, M. and Fong, S. 2012. Bat algorithm for topology optimization in microelectronic applications. 1st International Conference on Future Generation Communication Technology. London, UK 12 - 14 Dec 2012 IEEE. pp. 150-155 https://doi.org/10.1109/FGCT.2012.6476566

Rare events forecasting using a residual-feedback GMDH neural network

Fong, S., Nannan, Z., Wong, R. and Yang, X. 2012. Rare events forecasting using a residual-feedback GMDH neural network. in: Seventh International Conference onDigital Information Management (ICDIM), 2012 IEEE Conference Publications. pp. 464-473

Cuckoo search for business optimization applications

Yang, X., Deb, S., Karamanoglu, M. and He, X. 2012. Cuckoo search for business optimization applications. National Conference on Computing and Communication Systems (NCCCS). Durgapur, West Bengal, India 21 - 22 Nov 2012 IEEE. https://doi.org/10.1109/NCCCS.2012.6412973

Bat algorithm for constrained optimization tasks

Gandomi, A., Yang, X., Alavi, A. and Talatahari, S. 2012. Bat algorithm for constrained optimization tasks. Neural Computing and Applications. 22 (6), pp. 1239-1255. https://doi.org/10.1007/s00521-012-1028-9

Efficiency analysis of swarm intelligence and randomization techniques

Yang, X. 2012. Efficiency analysis of swarm intelligence and randomization techniques. Journal of Computational and Theoretical Nanoscience. 9 (2), pp. 189-198. https://doi.org/10.1166/jctn.2012.2012

Modelling of a pulsating heat pipe and start up asymptotics

Yang, X. and Luan, T. 2012. Modelling of a pulsating heat pipe and start up asymptotics. Procedia Computer Science. 9, pp. 784-791. https://doi.org/10.1016/j.procs.2012.04.084

Computational optimization, modelling and simulation: smart algorithms and better models

Yang, X., Koziel, S. and Leifsson, L. 2012. Computational optimization, modelling and simulation: smart algorithms and better models. Procedia Computer Science. 9, pp. 852-856. https://doi.org/10.1016/j.procs.2012.04.091

Free lunch or no free lunch: that is not just a question?

Yang, X. 2012. Free lunch or no free lunch: that is not just a question? International Journal on Artificial Intelligence Tools. 21 (3). https://doi.org/10.1142/S0218213012400106

Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization

Gandomi, A., Yang, X., Talatahari, S. and Deb, S. 2012. Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Computers and Mathematics with Applications. 63 (1), pp. 191-200. https://doi.org/10.1016/j.camwa.2011.11.010

Bat algorithm: a novel approach for global engineering optimization

Yang, X. and Gandomi, A. 2012. Bat algorithm: a novel approach for global engineering optimization. Engineering Computations. 29 (5), pp. 464-483. https://doi.org/10.1108/02644401211235834

Evolutionary boundary constraint handling scheme

Gandomi, A. and Yang, X. 2012. Evolutionary boundary constraint handling scheme. Neural Computing and Applications. 21 (6), pp. 1449-1462. https://doi.org/10.1007/s00521-012-1069-0

Two-stage eagle strategy with differential evolution

Yang, X. and Deb, S. 2012. Two-stage eagle strategy with differential evolution. International Journal of Bio-Inspired Computation. 4 (1), pp. 1-5. https://doi.org/10.1504/IJBIC.2012.044932

Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect

Yang, X., Hosseini, S. and Gandomi, A. 2012. Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing. 12 (3), pp. 1180-1186. https://doi.org/10.1016/j.asoc.2011.09.017

Cell assemblies for query expansion in information retrieval

Volpe, I., Moreira, V. and Huyck, C. 2011. Cell assemblies for query expansion in information retrieval. 2011 International Joint Conference on Neural Networks (IJCNN). San Jose, CA, USA 31 Jul - 05 Aug 2011 IEEE. pp. 551-558 https://doi.org/10.1109/IJCNN.2011.6033269

Accelerated particle swarm optimization and support vector machine for business optimization and applications

Yang, X., Deb, S. and Fong, S. 2011. Accelerated particle swarm optimization and support vector machine for business optimization and applications. Fong, S. (ed.) NDT 2011: International Conference on Networked Digital Technologies. Macau, China 11 - 13 Jul 2011 Springer. pp. 53-66 https://doi.org/10.1007/978-3-642-22185-9_6

Bat algorithm for multi-objective optimisation

Yang, X. 2011. Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation. 3 (5), pp. 267-274. https://doi.org/10.1504/IJBIC.2011.042259

Mixed variable structural optimization using Firefly Algorithm

Gandomi, A., Yang, X. and Alavi, A. 2011. Mixed variable structural optimization using Firefly Algorithm. Computers and Structures. 89 (23-24), pp. 2325-2336. https://doi.org/10.1016/j.compstruc.2011.08.002

Conflict resolution and learning probability matching in a neural cell-assembly architecture

Belavkin, R. and Huyck, C. 2011. Conflict resolution and learning probability matching in a neural cell-assembly architecture. Cognitive Systems Research. 12 (2), pp. 93-101. https://doi.org/10.1016/j.cogsys.2010.08.003

A Pong playing agent modelled with massively overlapping cell assemblies

Nadh, K. and Huyck, C. 2010. A Pong playing agent modelled with massively overlapping cell assemblies. Neurocomputing. 73 (16-18), pp. 2928-2934. https://doi.org/10.1016/j.neucom.2010.07.013

Multi-associative memory in fLIF cell assemblies.

Huyck, C. and Nadh, K. 2009. Multi-associative memory in fLIF cell assemblies. 9th International Conference on Cognitive Modeling. Manchester 24 - 26 Jul 2009

Processing with cell assemblies

Byrne, E. and Huyck, C. 2010. Processing with cell assemblies. Neurocomputing. 74 (1-3), pp. 76-83. https://doi.org/10.1016/j.neucom.2009.09.024

Using cohesive devices to recognize rhetorical relations in text.

Le, H., Abeysinghe, G. and Huyck, C. 2003. Using cohesive devices to recognize rhetorical relations in text. 4th Computational Linguistics UK Research Colloquium (CLUK-4). Edinburgh University Jan 2003 pp. 123-128

Automated discourse segmentation by syntactic information and cue phrases.

Le, H., Abeysinghe, G. and Huyck, C. 2004. Automated discourse segmentation by syntactic information and cue phrases. IASTED International Conference on Artificial Intelligence and Applications (AIA 2004). Innsbruck, Austria 16 - 18 Feb 2004 pp. 293-298

Generating discourse structures for written texts

Le, H., Abeysinghe, G. and Huyck, C. 2004. Generating discourse structures for written texts. International Conference on Computational Linguistics, (COLING 2004). University of Geneva, Switzerland 23 - 27 Aug 2004 pp. 329-355

A study to improve the efficiency of a discourse parsing system

Le, H., Abeysinghe, G. and Huyck, C. 2003. A study to improve the efficiency of a discourse parsing system. 4th International Conference on Intelligent Text Processing and Computational Linguistics, (CICLing’03). Mexico City 16 - 22 Feb 2003 pp. 101-114

Emergence of rules in cell assemblies of fLIF neurons.

Belavkin, R. and Huyck, C. 2008. Emergence of rules in cell assemblies of fLIF neurons. The 18th European Conference on Artificial Intelligence. University of Patras, Greece 21 - 25 Jul 2008

A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies.

Belavkin, R. and Huyck, C. 2009. A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies. 9th International conference on cognitive modelling {ICCM 2009]. University of Manchester 24 - 26 Jul 2009

Models of cell assembly decay

Passmore, P. and Huyck, C. 2008. Models of cell assembly decay. Institute of Electrical and Electronics Engineers. pp. 1-6 https://doi.org/10.1109/UKRICIS.2008.4798946

Dialogue based interfaces for universal access.

Huyck, C. 2010. Dialogue based interfaces for universal access. Universal Access in the Information Society. https://doi.org/10.1007/s10209-010-0209-5

A psycholinguistic model of natural language parsing implemented in simulated neurons

Huyck, C. 2009. A psycholinguistic model of natural language parsing implemented in simulated neurons. Cognitive Neurodynamics. 3 (4), pp. 316-330. https://doi.org/10.1007/s11571-009-9080-6

Variable binding by synaptic strength change

Huyck, C. 2009. Variable binding by synaptic strength change. Connection Science. 21 (4), pp. 327-357. https://doi.org/10.1080/09540090902954188

A new metaheuristic bat-inspired algorithm

Yang, X. 2010. A new metaheuristic bat-inspired algorithm. in: González, J., Pelta, D., Cruz, C., Terrazas, G. and Krasnogor, N. (ed.) Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) Berlin Springer.

Eagle strategy using Lévy walk and firefly algorithm for stochastic optimization

Yang, X. and Deb, S. 2010. Eagle strategy using Lévy walk and firefly algorithm for stochastic optimization. in: González, J., Pelta, D, Cruz, C., Terrazas, G. and Krasnogor, N. (ed.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) Springer. pp. 101-111

Optimization and data mining for fracture prediction in geosciences

Shi, G. and Yang, X. 2010. Optimization and data mining for fracture prediction in geosciences. Procedia Computer Science. 1 (1), pp. 1359-1366. https://doi.org/10.1016/j.procs.2010.04.151

Firefly algorithm, stochastic test functions and design optimisation

Yang, X. 2010. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation. 2 (2), pp. 78-84. https://doi.org/10.1504/IJBIC.2010.032124

Oil and gas assessment of the Kuqa depression of Tarim Basin in western China by simple fluid flow models of primary and secondary migrations of hydrocarbons

Shi, G., Zhang, Q., Yang, X. and Mi, S. 2010. Oil and gas assessment of the Kuqa depression of Tarim Basin in western China by simple fluid flow models of primary and secondary migrations of hydrocarbons. Journal of Petroleum Science and Engineering. 75 (1-2), pp. 77-90. https://doi.org/10.1016/j.petrol.2010.10.009

Prepositional phrase attachment ambiguity resolution using semantic hierarchies

Nadh, K. and Huyck, C. 2009. Prepositional phrase attachment ambiguity resolution using semantic hierarchies. Hamza, M. (ed.) 9th IASTED International Conference on Artificial Intelligence and Applications. Innsbruck, Austria 17 - 18 Feb 2009 Acta Press.

Neural cell assemblies for practical applications.

Huyck, C. and Bavan, A. 2002. Neural cell assemblies for practical applications. in: Callaos, N. (ed.) SCI 2002: ISAS: the 6th world multiconference on systemics, cybernetics and informatics: proceedings. Orlando, Florida. International Institute of Informatics and Systemics.. pp. 174-177

Agent design method for enhancing accessibility.

Cook, J., Huyck, C. and Whitney, G. 2004. Agent design method for enhancing accessibility. in: McLoughlin, C. and Cantoni, L. (ed.) ED-MEDIA 2004: world conference on educational multimedia, hypermedia and telecommunications: proceedings of ED-MEDIA 2004. Association for the Advancement of Computing in Education.