Bio-inspired computation: where we stand and what's next

Article


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
TypeArticle
TitleBio-inspired computation: where we stand and what's next
AuthorsDel Ser, J., Osaba, E., Molina, D., Yang, X., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P., Coello Coello, C. and Herrera, F.
Abstract

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.

LanguageEnglish
PublisherElsevier
JournalSwarm and Evolutionary Computation
ISSN2210-6502
Publication dates
Online29 Apr 2019
Print01 Aug 2019
Publication process dates
Deposited10 Jun 2019
Accepted15 Apr 2019
Output statusPublished
Accepted author manuscript
License
Copyright Statement

© 2019. This accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1016/j.swevo.2019.04.008
Permalink -

https://repository.mdx.ac.uk/item/88501

Download files

  • 47
    total views
  • 28
    total downloads
  • 7
    views this month
  • 9
    downloads this month

Export as

Related outputs

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.
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
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
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
Generalized Firefly Algorithm for optimal transmit beamforming
Le, T. and Yang, X. 2023. Generalized Firefly Algorithm for optimal transmit beamforming. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2023.3328713
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
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
Flower pollination algorithm with pollinator attraction
Mergos, P. and Yang, X. 2022. Flower pollination algorithm with pollinator attraction. Evolutionary Intelligence. https://doi.org/10.1007/s12065-022-00700-7
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. pp. 469-487
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
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
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
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., 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), p. 07017023. https://doi.org/10.1061/(asce)ir.1943-4774.0001270
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
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
Optimization Techniques and Applications with Examples
Yang, X. 2018. Optimization Techniques and Applications with Examples. Hoboken, New Jersey John Wiley & Sons, Inc..
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
Mathematics for civil engineers: an introduction
Yang, X. 2017. Mathematics for civil engineers: an introduction. Edinburgh Dunedin Academic Press.
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
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
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
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
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
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
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
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
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.
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 Switzerland Springer.
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
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
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.
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
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
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.
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. https://doi.org/10.1007/s11432-015-5417-4
Introduction to computational mathematics
Yang, X. 2015. Introduction to computational mathematics. Singapore World Scientific Publishing.
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
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.
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.
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.
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
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 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.
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 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-8
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. pp. 74-82 https://doi.org/10.5220/0005596200740082
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 Institute of Electrical and Electronics Engineers (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. https://doi.org/10.1080/10798587.2015.1025480
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.
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.
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
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
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
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
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
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
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.
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. https://doi.org/10.1038/srep10794
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
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
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
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
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 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. Procedia Computer Science. 29, 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 Institute of Electrical and Electronics Engineers (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
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
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
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
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
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 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
Analysis of randomisation methods in swarm intelligence
Fister, I., 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
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
Mathematical analysis of energy efficiency optimality in multi-user OFDM systems
Chien, S., Ting, T., Yang, X. and Takahashi, K. 2014. 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
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
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
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
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
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
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
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
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 on Digital Information Management (ICDIM), 2012 IEEE Conference Publications. pp. 464-473
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
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
A new metaheuristic bat-inspired algorithm
Yang, X. 2010. A new metaheuristic bat-inspired algorithm. in: 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: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) Springer.
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. in: Networked Digital Technologies : Third International Conference, NDT 2011, Macau, China, July 11-13, 2011. Proceedings Springer.
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
Optimization and data mining for fracture prediction in geoscience
Shi, G. and Yang, X. 2010. Optimization and data mining for fracture prediction in geoscience. Procedia Computer Science. 1 (1), pp. 1359-1366. https://doi.org/10.1016/j.procs.2010.04.151
Bat algorithm for constrained optimization tasks, neural computing & applications
Gandomi, A., Yang, X., Alavi, A. and Talatahari, S. 2012. Bat algorithm for constrained optimization tasks, neural computing & applications. Neural Computing and Applications. 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.
Bat algorithm for multi-objective optimisation
Yang, X. 2011. Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation (IJBIC). 3 (5), pp. 267-274. https://doi.org/10.1504/IJBIC.2011.042259
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
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
Multiobjective firefly algorithm for continuous optimization
Yang, X. 2012. Multiobjective firefly algorithm for continuous optimization. Engineering with Computers.
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
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
Mixed variable structural optimization using firefly algorithm
Gandomi, A., Yang, X. and Alavi, A. 2011. Mixed variable structural optimization using firefly algorithm. Computers & Structures. 89 (23-24), pp. 2325-2336. https://doi.org/10.1016/j.compstruc.2011.08.002
Multiobjective cuckoo search for design optimization
Yang, X. and Deb, S. 2011. Multiobjective cuckoo search for design optimization. Computers & Operations Research. 40 (6), pp. 1616-1624. https://doi.org/10.1016/j.cor.2011.09.026
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
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
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
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
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