Flower pollination algorithm with pollinator attraction

Article

Mergos, P. and Yang, X. 2023. Flower pollination algorithm with pollinator attraction. Evolutionary Intelligence. 16 (3), pp. 873-889. https://doi.org/10.1007/s12065-022-00700-7
TypeArticle
TitleFlower pollination algorithm with pollinator attraction
AuthorsMergos, P. and Yang, X.
Abstract

The Flower Pollination Algorithm (FPA) is a highly efficient optimization algorithm that is inspired by the evolution process of flowering plants. In the present study, a modified version of FPA is proposed accounting for an additional feature of flower pollination in nature that is the so-called pollinator attraction. Pollinator attraction represents the natural tendency of flower species to evolve in order to attract pollinators by using their colour, shape and scent as well as nutritious rewards. To reflect this evolution mechanism, the proposed FPA variant with Pollinator Attraction (FPAPA) provides fitter flowers of the population with higher probabilities of achieving pollen transfer via biotic pollination than other flowers. FPAPA is tested against a set of 28 benchmark mathematical functions, defined in IEEE-CEC’13 for real-parameter single-objective optimization problems, as well as structural optimization problems. Numerical experiments show that the modified FPA represents a statistically significant improvement upon the original FPA and that it can outperform other state-of-the-art optimization algorithms offering better and more robust optimal solutions. Additional research is suggested to combine FPAPA with other modified and hybridized versions of FPA to further increase its performance in challenging optimization problems.

KeywordsFlower pollination algorithm; Pollinator attraction; Metaheuristics; Evolutionary; Optimization
PublisherSpringer
JournalEvolutionary Intelligence
ISSN1864-5909
Electronic1864-5917
Publication dates
Online12 Jan 2022
PrintJun 2023
Publication process dates
Deposited24 Jan 2022
Submitted03 Jul 2021
Completed14 Dec 2021
Accepted31 Dec 2021
Output statusPublished
Accepted author manuscript
File Access Level
Open
Accepted author manuscript
File Access Level
Restricted
Copyright Statement

This version of the article has been accepted for publication, after peer review (when applicable) 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 of this article, first published in Evolutionary Intelligence, is available online at Publisher’s website: http://dx.doi.org/10.1007/s12065-022-00700-7

Digital Object Identifier (DOI)https://doi.org/10.1007/s12065-022-00700-7
Web of Science identifierWOS:000741877700001
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/89q4w

Log in to edit

Download files

Accepted author manuscript
Mergos_Yang_Final_Version.pdf
File access level: Open

  • 86
    total views
  • 12
    total downloads
  • 2
    views this month
  • 3
    downloads this month

Export as

Related outputs

Firefly algorithm for movable antenna arrays
Kha, H., Le, T., Thuc, K., Luyen, T., Yang, X. and Ng, D. 2024. Firefly algorithm for movable antenna arrays. IEEE Wireless Communications Letters. 13 (11), pp. 3157-3161. https://doi.org/10.1109/LWC.2024.3456899
A generalized evolutionary metaheuristic (GEM) algorithm for engineering optimization
Yang, X. 2024. A generalized evolutionary metaheuristic (GEM) algorithm for engineering optimization. Cogent Engineering. 11 (1). https://doi.org/10.1080/23311916.2024.2364041
Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods
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
Cognitive beamforming design for dual-function radar-communications
Le, T., Ku, I., Yang, X., Masouros, C. and Le-Ngoc, T. 2024. Cognitive beamforming design for dual-function radar-communications. 2024 IEEE 99th Vehicular Technology Conference. Singapore 24 - 27 Jun 2024 IEEE. pp. 1-5 https://doi.org/10.1109/VTC2024-Spring62846.2024.10683337
Generalized Firefly Algorithm for optimal transmit beamforming
Le, T. and Yang, X. 2024. Generalized Firefly Algorithm for optimal transmit beamforming. IEEE Transactions on Wireless Communications. 23 (6), pp. 5863-5877. https://doi.org/10.1109/TWC.2023.3328713
A rank-one optimization framework and its applications to transmit beamforming
Le, T., Ng, D. and Yang, X. 2024. A rank-one optimization framework and its applications to transmit beamforming. IEEE Transactions on Vehicular Technology. 73 (1), pp. 620-636. https://doi.org/10.1109/TVT.2023.3303623
Multi-objective flower pollination algorithm: a new technique for EEG signal denoising
Alyasseri, Z., Khader, A., Al-Betar, M., Yang, X., Mohammed, M., Abdulkareem, K., Kadry, S. and Razzak, I. 2023. Multi-objective flower pollination algorithm: a new technique for EEG signal denoising. Neural Computing and Applications. 35 (11), p. 7943–7962. https://doi.org/10.1007/s00521-021-06757-2
Review of parameter tuning methods for nature-inspired algorithms
Joy, G., Huyck, C. and Yang, X. 2023. Review of parameter tuning methods for nature-inspired algorithms. in: Yang, X. (ed.) Benchmarks and Hybrid Algorithms in Optimization and Applications Singapore Springer. pp. 33-47
Firefly algorithm for beamforming design in RIS-aided communication systems
Le, T. and Yang, X. 2023. Firefly algorithm for beamforming design in RIS-aided communication systems. The 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). Florence, Italy 20 - 23 Jun 2023 IEEE. https://doi.org/10.1109/VTC2023-Spring57618.2023.10201127
A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data
Li, J., Wu, Y., Fong, S., Tallon-Ballesteros, A., Yang, X., Mohammed, S. and Wu, F. 2022. A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data. Journal of Supercomputing. 78, p. 7428–7463. https://doi.org/10.1007/s11227-021-04177-6
An elitism-based multi-objective evolutionary algorithm for min-cost network disintegration
Li, Q., Liu, S., Bai, Y., He, X. and Yang, X. 2022. An elitism-based multi-objective evolutionary algorithm for min-cost network disintegration. Knowledge-Based Systems. 239, pp. 1-19. https://doi.org/10.1016/j.knosys.2021.107944
Swarm and stochastic computing for global optimization
Yang, X. 2021. Swarm and stochastic computing for global optimization. in: Adamatzky, A. (ed.) Handbook of Unconventional Computing - Volume 1: Theory World Scientific Publishing Co. Pte Ltd. pp. 469-487
FPA clust: evaluation of the flower pollination algorithm for data clustering
Senthilnath, J., Kulkarni, S., Suresh, S., Yang, X.-S. and Benediktsson, J.A. 2021. FPA clust: evaluation of the flower pollination algorithm for data clustering. Evolutionary Intelligence. 14 (3), pp. 1189-1199. https://doi.org/10.1007/s12065-019-00254-1
MO-MFCGA: Multiobjective multifactorial cellular genetic algorithm for evolutionary multitasking
Osaba, E., Del Ser, J., Martinez, A., Lobo, J., Nebro, A. and Yang, X. 2021. MO-MFCGA: Multiobjective multifactorial cellular genetic algorithm for evolutionary multitasking. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, FL, USA 05 - 07 Dec 2021 IEEE. pp. 1-8 https://doi.org/10.1109/SSCI50451.2021.9660024
Flower pollination algorithm parameters tuning
Mergos, P. and Yang, X. 2021. Flower pollination algorithm parameters tuning. Soft Computing. 25 (22), pp. 14429-14447. https://doi.org/10.1007/s00500-021-06230-1
A nature-inspired feature selection approach based on hypercomplex information
de Rosa, G., Papa, J. and Yang, X. 2020. A nature-inspired feature selection approach based on hypercomplex information. Applied Soft Computing. 94. https://doi.org/10.1016/j.asoc.2020.106453
Influence of initialization on the performance of metaheuristic optimizers
Li, Q., Liu, S. and Yang, X. 2020. Influence of initialization on the performance of metaheuristic optimizers. Applied Soft Computing. 91. https://doi.org/10.1016/j.asoc.2020.106193
White learning methodology: a case study of cancer-related disease factors analysis in real-time PACS environment
Li, T., Fong, S., Siu, S., Yang, X., Liu, L. and Mohammed, S. 2020. White learning methodology: a case study of cancer-related disease factors analysis in real-time PACS environment. Computer Methods and Programs in Biomedicine. 197, pp. 1-18. https://doi.org/10.1016/j.cmpb.2020.105724
Nature-inspired optimization algorithms: challenges and open problems
Yang, X. 2020. Nature-inspired optimization algorithms: challenges and open problems. Journal of Computational Science. 46. https://doi.org/10.1016/j.jocs.2020.101104
Atomic scheduling of appliance energy consumption in residential smart grids
Kim, K., Lee, S., Ting, T. and Yang, X. 2019. Atomic scheduling of appliance energy consumption in residential smart grids. Energies. 12 (19). https://doi.org/10.3390/en12193666
Improved tabu search and simulated annealing methods for nonlinear data assimilation
Nino-Ruiz, E. and Yang, X. 2019. Improved tabu search and simulated annealing methods for nonlinear data assimilation. Applied Soft Computing. 83, p. 105624. https://doi.org/10.1016/j.asoc.2019.105624
Bio-inspired computation: where we stand and what's next
Del Ser, J., Osaba, E., Molina, D., Yang, X., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P., Coello Coello, C. and Herrera, F. 2019. Bio-inspired computation: where we stand and what's next. Swarm and Evolutionary Computation. 48, pp. 220-250. https://doi.org/10.1016/j.swevo.2019.04.008
Comparison of constraint-handling techniques for metaheuristic optimization
He, X.-S., Fan, Q.-W., Karamanoglu, M. and Yang, X. 2019. Comparison of constraint-handling techniques for metaheuristic optimization. 19th International Conference on Computational Science - ICCS 2019. Faro, Portugal 12 - 14 Jun 2019 Switzerland Springer. pp. 357-366 https://doi.org/10.1007/978-3-030-22744-9_28
Enhancing security of MME handover via fractional programming and Firefly algorithm
Vien, Q., Le, T., Yang, X. and Duong, T. 2019. Enhancing security of MME handover via fractional programming and Firefly algorithm. IEEE Transactions on Communications. 67 (9), pp. 6206-6220. https://doi.org/10.1109/TCOMM.2019.2920353
Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption
Palmieri, N., Yang, X., De Rango, F. and Marano, S. 2019. Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption. Neural Computing and Applications. 31 (1), pp. 263-286. https://doi.org/10.1007/s00521-017-2998-4
Optimization Techniques and Applications with Examples
Yang, X. 2018. Optimization Techniques and Applications with Examples. Hoboken, New Jersey John Wiley & Sons, Inc..
Mathematics for civil engineers: an introduction
Yang, X. 2017. Mathematics for civil engineers: an introduction. Edinburgh Dunedin Academic Press.
Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team
Palmieri, N., Yang, X., Rango, F. and Santamaria, A. 2018. Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.03.038
Discussion of “Estimation of Reference Evapotranspiration Using Neural Networks and Cuckoo Search Algorithm” by Shahaboddin Shamshirband, Mohsen Amirmojahedi, Milan Gocić, Shatirah Akib, Dalibor Petković, Jamshid Piri, and Slavisa Trajkovic
Fister, I. (Jr.), Fister, I. and Yang, X. 2018. Discussion of “Estimation of Reference Evapotranspiration Using Neural Networks and Cuckoo Search Algorithm” by Shahaboddin Shamshirband, Mohsen Amirmojahedi, Milan Gocić, Shatirah Akib, Dalibor Petković, Jamshid Piri, and Slavisa Trajkovic. Journal of Irrigation and Drainage Engineering. 144 (2). https://doi.org/10.1061/(ASCE)IR.1943-4774.0001270
Global convergence analysis of the bat algorithm using a markovian framework and dynamical system theory
Chen, S., Peng, G., He, X. and Yang, X. 2018. Global convergence analysis of the bat algorithm using a markovian framework and dynamical system theory. Expert Systems with Applications. 114, pp. 173-182. https://doi.org/10.1016/j.eswa.2018.07.036
Metaheuristic optimization of reinforced concrete footings
Nigdeli, S., Bekdaş, G. and Yang, X. 2018. Metaheuristic optimization of reinforced concrete footings. KSCE Journal of Civil Engineering. 22 (11), pp. 4555-4563. https://doi.org/10.1007/s12205-018-2010-6
Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks
De Rango, F., Palmieri, N., Yang, X. and Marano, S. 2018. Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks. Soft Computing. 22 (13), pp. 4251-4266. https://doi.org/10.1007/s00500-017-2819-9
How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics
Fong, S., Deb, S. and Yang, X. 2018. How meta-heuristic algorithms contribute to deep learning in the hype of big data analytics. ICACNI 2016: 4th International Conference on Advanced Computing, Networking and Informatics. Odisha , India 22 - 24 Sep 2016 Springer. https://doi.org/10.1007/978-981-10-3373-5_1
Handling dropout probability estimation in convolution neural networks using meta-heuristics
De Rosa, G., Papa, J. and Yang, X. 2018. Handling dropout probability estimation in convolution neural networks using meta-heuristics. Soft Computing. 22 (18), pp. 6147-6156. https://doi.org/10.1007/s00500-017-2678-4
Quaternion-based deep belief networks fine-tuning
Papa, J., Rosa, G., Pereira, D. and Yang, X. 2017. Quaternion-based deep belief networks fine-tuning. Applied Soft Computing. 60, pp. 328-335. https://doi.org/10.1016/j.asoc.2017.06.046
Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach
He, X., Yang, X., Karamanoglu, M. and Zhao, Y. 2017. Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach. International Conference on Computational Science, ICCS 2017. Zurich, Switzerland 12 - 14 Jun 2017 Elsevier. https://doi.org/10.1016/j.procs.2017.05.020
On the handover security key update and residence management in LTE networks
Vien, Q., Le, T., Yang, X. and Duong, T. 2017. On the handover security key update and residence management in LTE networks. IEEE Wireless Communications and Networking Conference (WCNC 2017). San Francisco, CA, USA 19 - 22 Mar 2017 IEEE. pp. 1-6 https://doi.org/10.1109/WCNC.2017.7925678
New directional bat algorithm for continuous optimization problems
Chakri, A., Khelif, R., Benouaret, M. and Yang, X. 2017. New directional bat algorithm for continuous optimization problems. Expert Systems with Applications. 69, pp. 159-175. https://doi.org/10.1016/j.eswa.2016.10.050
Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism
Wang, H., Cui, Z., Sun, H., Rahnamayan, S. and Yang, X. 2017. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Computing. 21 (18), pp. 5325-5339. https://doi.org/10.1007/s00500-016-2116-z
A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy
Osaba, E., Yang, X., Diaz, F., Onieva, E., Masegosa, A. and Perallos, A. 2017. A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Computing. 21 (18), pp. 5295-5308. https://doi.org/10.1007/s00500-016-2114-1
Bio-inspired computation and applications in image processing
Yang, X. and Papa, J. 2016. Bio-inspired computation and applications in image processing. Academic Press.
Nature-inspired computation: an unconventional approach to optimization
Yang, X. 2016. Nature-inspired computation: an unconventional approach to optimization. in: Adamatzky, A. (ed.) Advances in Unconventional Computing - Volume 2: Prototypes, Models and Algorithms Cham Springer.
Hybrid local diffusion maps and improved cuckoo search algorithm for multiclass dataset analysis
Jia, B., Yu, B., Wu, Q., Yang, X., Wei, C., Law, R. and Fu, S. 2016. Hybrid local diffusion maps and improved cuckoo search algorithm for multiclass dataset analysis. Neurocomputing. 189, pp. 106-116. https://doi.org/10.1016/j.neucom.2015.12.066
Large-scale global optimization via swarm intelligence
Cheng, S., Ting, T. and Yang, X. 2014. Large-scale global optimization via swarm intelligence. in: Solving Computationally Expensive Engineering Problems Springer.
Cuckoo search and firefly algorithm: overview and analysis
Yang, X. 2013. Cuckoo search and firefly algorithm: overview and analysis. in: Cuckoo Search and Firefly Algorithm 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: Adaptation and Hybridization in Computational Intelligence Springer.
Analysis of firefly algorithms and automatic parameter tuning
Yang, X. 2014. Analysis of firefly algorithms and automatic parameter tuning. in: Emerging Research on Swarm Intelligence and Algorithm Optimization Information Science Reference, IGI-Global. pp. 36-49
Nature-inspired algorithms: success and challenges
Yang, X. 2015. Nature-inspired algorithms: success and challenges. in: Engineering and Applied Sciences Optimization Springer.
Introduction to computational mathematics
Yang, X. 2015. Introduction to computational mathematics. Singapore World Scientific Publishing Co. Pte Ltd.
Nature-inspired optimization algorithms in engineering: overview and applications
Yang, X. and He, X. 2016. Nature-inspired optimization algorithms in engineering: overview and applications. in: Yang, X. (ed.) Nature-Inspired Computation in Engineering Springer.
Parameterless bat algorithm and its performance study
Fister, I., Mlakar, U., Yang, X. and Fister, I. 2016. Parameterless bat algorithm and its performance study. in: Nature-Inspired Computation in Engineering Springer.
A literature survey of benchmark functions for global optimisation problems
Jamil, M. and Yang, X. 2013. A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation. 4 (2), pp. 150-194. https://doi.org/10.1504/IJMMNO.2013.055204
Synthesizing cross-ambiguity functions using the improved bat algorithm
Jamil, M., Zepernick, H. and Yang, X. 2014. Synthesizing cross-ambiguity functions using the improved bat algorithm. in: Recent Advances in Swarm Intelligence and Evolutionary Computation Springer.
Multi-robot cooperative tasks using combined nature-inspired techniques
Palmieri, N., De Rango, F., Yang, X. and Marano, S. 2015. Multi-robot cooperative tasks using combined nature-inspired techniques. ECTA 2015: 7th International Conference on Evolutionary Computation Theory and Applications (part of IJCCI, the 7th International Joint Conference on Computational Intelligence). Lisbon, Portugal 12 - 14 Nov 2015 SCITEPRESS - Science and Technology Publications. pp. 74-82 https://doi.org/10.5220/0005596200740082
Review and applications of metaheuristic algorithms in civil engineering
Yang, X., Bekdaş, G. and Nigdeli, S. 2016. Review and applications of metaheuristic algorithms in civil engineering. in: Metaheuristics and Optimization in Civil Engineering Springer.
Application of the flower pollination algorithm in structural engineering
Nigdeli, S., Bekdaş, G. and Yang, X. 2016. Application of the flower pollination algorithm in structural engineering. in: Metaheuristics and Optimization in Civil Engineering Springer.
Cuckoo search: from Cuckoo reproduction strategy to combinatorial optimization
Ouaarab, A. and Yang, X. 2016. Cuckoo search: from Cuckoo reproduction strategy to combinatorial optimization. in: Nature-Inspired Computation in Engineering Springer.
Attraction and diffusion in nature-inspired optimization algorithms
Yang, X., Deb, S., Hanne, T. and He, X. 2015. Attraction and diffusion in nature-inspired optimization algorithms. Neural Computing and Applications. https://doi.org/10.1007/s00521-015-1925-9
EEG-based person identification through binary flower pollination algorithm
Rodrigues, D., Silva, G., Papa, J., Marana, A. and Yang, X. 2016. EEG-based person identification through binary flower pollination algorithm. Expert Systems with Applications. 62, pp. 81-90. https://doi.org/10.1016/j.eswa.2016.06.006
A novel hybrid firefly algorithm for global optimization
Zhang, L., Liu, L., Yang, X. and Dai, Y. 2016. A novel hybrid firefly algorithm for global optimization. PLoS ONE. 11 (9), pp. 1-17. https://doi.org/10.1371/journal.pone.0163230
From swarm intelligence to metaheuristics: nature-inspired optimization algorithms
Yang, X., Deb, S., Fong, S., He, X. and Zhao, Y. 2016. From swarm intelligence to metaheuristics: nature-inspired optimization algorithms. Computer. 49 (9), pp. 52-59. https://doi.org/10.1109/MC.2016.292
Lévy flight artificial bee colony algorithm
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
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.
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
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
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
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
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
Firefly algorithms for multimodal optimization
Yang, X. 2009. Firefly algorithms for multimodal optimization. in: Stochastic Algorithms: Foundations and Applications; 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings Springer.
Cuckoo search via Lévy flights
Yang, X. and Deb, S. 2009. Cuckoo search via Lévy flights. in: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) IEEE Publications. pp. 210-214