Abstract
Population-based algorithms have been used in many real-world problems. Bat algorithm (BA) is one of the states of the art of these approaches. Because of the super bat, on the one hand, BA can converge quickly; on the other hand, it is easy to fall into local optimum. Therefore, for typical BA algorithms, the ability of exploration and exploitation is not strong enough and it is hard to find a precise result. In this paper, we propose a novel bat algorithm based on cross boundary learning (CBL) and uniform explosion strategy (UES), namely BABLUE in short, to avoid the above contradiction and achieve both fast convergence and high quality. Different from previous opposition-based learning, the proposed CBL can expand the search area of population and then maintain the ability of global exploration in the process of fast convergence. In order to enhance the ability of local exploitation of the proposed algorithm, we propose UES, which can achieve almost the same search precise as that of firework explosion algorithm but consume less computation resource. BABLUE is tested with numerous experiments on unimodal, multimodal, one-dimensional, high-dimensional and discrete problems, and then compared with other typical intelligent optimization algorithms. The results show that the proposed algorithm outperforms other algorithms.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Change history
22 June 2021
A Correction to this paper has been published: https://doi.org/10.1007/s11766-021-3714-9
References
Chengjing W. A trust region method with a conic model for nonlinearly constrained optimization, Applied Mathematics-A Journal of Chinese Universities, 2006, 21(3): 263–275.
Xiaojiao T, Shuzi Z. A trust region algorithm for a class of nonlinear optimization, Applied Mathematics-A Journal of Chinese Universities, 2000, 15(1): 93–98.
Li H, He F, Yan X. IBEA-SVM: An Indicator-based Evolutionary Algorithm Based on Preselection with Classification Guided by SVM, Applied Mathematics-A Journal of Chinese Universities, 2019, 34(1): 1–26
Zhenhai, L Yehui P. A derivative-free algorithm for unconstrained optimization, Applied Mathematics-A Journal of Chinese Universities, 2005, 20(4): 491–498.
Pan Y, He F, Yu H. A Novel Enhanced Collaborative Autoencoder with Knowledge Distillation for Top-N Recommender Systems, Neurocomputing, 2019, 332: 137–148.
Zhu H. Maximizing group performance while minimizing budget, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 99: 1–13.
Sun J, He F, Chen Y, Chen X. A multiple template approach for robust tracking of fast motion target, Applied Mathematics-A Journal of Chinese Universities, 2016, 31(2): 177–197.
Carotenuto P, Galiano G, Giordani S, et al. A Hybrid Metaheuristic Approach for Customer-Service Level in the Vehicle Routing Problem, Working Paper, Istituto di Tecnologie Industriali e Automazione-Sezione di Roma, Italy, 2005.
Chen X, He F, Yu H. A Matting Method Based on Full Feature Coverage, Multimedia Tools and Applications, Multimedia Tools and Applications, 2019, 78(9): 11173–11201.
Garg H. A hybrid PSO-GA algorithm for constrained optimization problems, Applied Mathematics and Computation, 2016, 274: 292–305.
Li K, He F, Yu H. Robust Visual Tracking based on Convolutional Features with Illumination and Occlusion Handing, Journal of Computer Science and Technology, 2018, 33(1): 223–236.
Xiong X, Zhang Y C, Zhang Q D. An improved self-adaptive PSO algorithm with detection function for multimodal function optimization problems, Mathematical Problems in Engineering, 2013, 2013.
Zhou Y, He F, Hou N, Qiu Y. Parallel Ant Colony Optimization on Multi-core SIMD CPUs, Future Generation Computer Systems, 2018, 79(2): 473–487.
Zhou Yi, He F, Qiu Y. Dynamic Strategy based Parallel Ant Colony Optimization on CPUs for TSPs, Science China Information Sciences, 2017, 60(6): 068102.
Yan X, He F, Hou N, Ai H. An Efficient Particle Swarm Optimization for Large Scale Hard-ware/Software Co-design System, International Journal of Cooperative Information Systems, 2018, 27(1): 1741001.
Geem Z W, Kim J H, Loganathan G V. A new heuristic optimization algorithm: harmony search, simulation, 2001, 76(2): 60–68.
Hou N, He F, Zhou Y, Chen Y. An Efficient GPU-based Parallel Tabu Search Algorithm for Hardware/Software Co-design, Frontiers of Computer Science. DOI: 10.1007/sll704-019-8184-3.
Lewis A, Mirjalili S, Mirjalili S M. Grey wolf optimizer, Advances in engineering software, 2014, 69: 46–61.
Li H, He F, Liang Y, Quan Q. A Dividing-based Many-objectives Evolutionary Algorithm for Large-scale Feature Selection. Soft Computing, DOI: 10.1007/s00500-019-04324-5
Simon D. Biogeography-based optimization, IEEE transactions on evolutionary computation, 2008, 12(6): 702–713.
Yang X S. A new metaheuristic bat—inspired algorithm, Nature inspired cooperative strategies for optimization (2010), pp.65–74.
Yang X S, He X. Bat algorithm: literature review and applications, International Journal of Bio-Inspired Computation, 2013, 5(3): 141–149.
Satapathy S C, Raja N S M, Rajinikanth V, et al. Multi-level image thresholding using Otsu and chaotic bat algorithm, Neural Computing and Applications, 2018: 1–23.
Shan X, Liu K, Sun P L. Modified bat algorithm based on levy flight and opposition based learning, Scientific Programming, 2016, Article ID 8031560.
Luo J, He F, Yong J. An Efficient and Robust Bat Algorithm with Fusion of opposition-based learning and Whale Optimization Algorithm. Intelligent Data Analysis. 2016, 24(3): 13–29.
Huang X, Zeng X, Han R. Dynamic inertia weight binary bat algorithm with neighborhood search, Computational intelligence and neuroscience, 2017, Article ID 3235720.
Sabba S, Chikhi S. A discrete binary version of bat algorithm for multidimensional knapsack problem, International Journal of Bio-Inspired Computation, 2014, 6(2): 140–152.
Yilmaz S, Kucuksille E U. Improved bat algorithm (IBA) on continuous optimization problems, Lecture Notes on Software Engineering, 2013, 1(3): 279.
Mirjalili S, Mirjalili S M, Yang X S. Binary bat algorithm, Neural Computing and Applications, 2014, 25(3–4): 663–681.
Chen H, Xie J, Zhou Y. A novel bat algorithm based on differential operator and Levy flights trajectory, Computational intelligence and neuroscience (2013).
Afrabandpey H, Ghaffari M, Mirzaei A, et al. A novel bat algorithm based on chaos for optimization tasks, Intelligent Systems (ICIS), 2014 Iranian Conference on. IEEE, 2014: 1–6.
Tan Y, Zhu Y. Fireworks algorithm for optimization, International Conference in Swarm Intelligence. Springer, Berlin, Heidelberg, 2010: 355–364.
Janecek A, Tan Y, Zheng S. Enhanced fireworks algorithm, Evolutionary Computation, 2013.
Tizhoosh H R. Opposition-based learning: a new scheme for machine intelligence, Computational intelligence for modelling, control and automation, 2005 and international conference on intelligent agents, web technologies and internet commerce, international conference on. IEEE, 2005, 1: 695–701.
Ding L X, Xie D T, Wang S W, et al. Group search optimizer applying opposition-based learning, Computer Science, 2012, 39(9): 183–187.
Wu Z J, Wang H, Zhou X Y, et al. Elite opposition-based particle swarm optimization, Acta Electronica Sinica, 2013, 41(8): 1647–1652.
Rahnamayan S, Tizhoosh H R, Salama M M A. Opposition-based differential evolution, IEEE Transactions on Evolutionary computation, 2008, 12(1): 64–79.
Hou N, Yan X, He F. A Survey on Partitioning Models, Solution Algorithms and Algorithm Parallelization for Hardware/Software Co-design, Design Automation for Embedded Systems, 2019, 23(1–2): 57–77.
Yong J, He F, Li H, et al. A Novel Bat Algorithm based on Collaborative and Dynamic Learning of Opposite Population, 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, 2018: 541–546.
Yilmaz S, Kucuksille E U, Cengiz Y. Modified bat algorithm, Elektronika ir Elektrotechnika, 2014, 20(2): 71–79.
Wu Y, He F, Zhang D, Li X. Service-Oriented Feature-Based Data Exchange for Cloud-Based Design and Manufacturing, IEEE Transactions on Services Computing, 2018, 11(2): 341–353.
Li K, He F, Yu H, Chen X. A Parallel and Robust Object Tracking Approach Synthesizing Adaptive Bayesian Learning and Improved Incremental Subspace Learning, Frontiers of Computer Science, 2019, 13(5): 1116–1135.
Zhang Q, Nie, Y, Zhan, L, Xiao C. Underexposed video enhancement via perception-driven progressive fusion, IEEE Transactions on Visualization and Computer Graphics, 2015, 22(6): 1773–1785.
Zhang S, He F, Ren W, Yao J. Joint learning of image detail and transmission map for single image dehazing, The Visual Computer, DOI: 10.1007/s00371-018-1612-9.
Yu H, He F, Pan Y. A Novel Segmentation Model for Medical Images with Intensity Inhomogeneity Based on Adaptive Perturbation, Multimedia Tools and Applications, 2019, 78(9): 11779–11798
Liotta G, Stecca G, Kaihara T. Optimisation of freight flows and sourcing in sustainable production and transportation networks, International Journal of Production Economics, 2015, 164: 351–365.
Yu H, He F, Pan Y. A novel region-based active contour model via local patch similarity measure for image segmentation, Multimedia Tools and Applications, 2018, 77(18): 24097–24119.
Liotta G, Kaihara T, Stecca G. Optimization and simulation of collaborative networks for sustainable production and transportation, IEEE Transactions on Industrial Informatics, 2016, 12(1): 417–424.
Zhang J, He F, Chen Y. A new haze removal approach for sky/river alike scenes based on external and internal clues, Multimedia Tools and Applications, DOI: 10.1007/sll042-019-08399-y.
Zhu H. Role-Based Collaboration and the E-CARGO: Revisiting the Developments of the Last Decade, IEEE Systems, Man, and Cybernetics Magazine, 2015, 1(3): 27–35.
Ni B, He F, Pan Y, Yuan Z. Using Shapes Correlation for Active Contour Segmentation of Uterine Fibroid Ultrasound Images in Computer-Aided Therapy, Applied Mathematics-A Journal of Chinese Universities, 2016, 31(1): 37–52.
Lv X, He F, Cai W, Cheng Y. An optimized RCA supporting selective undo for collaborative text editing systems, Journal of Parallel and Distributed Computing, 2019, 132: 310–330.
Pan YT, He FZ, Yu HP. A Correlative Denoising Autoencoder to Model Social Influence for Top-N Recommender System, Frontiers of Computer Science, DOI: 10.1007/sll704-019-8123-3.
Li K, He F, Yu H, Chen X. A Correlative Classifiers Approach based on Particle Filter and Sample Set for Tracking Occluded Target, Applied Mathematics-A Journal of Chinese Universities, 2017, 32(3): 294–312.
Zhu, H. Avoiding Critical Members in a Team by Redundant Assignment, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 99: 1–12.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Natural Science Foundation of China (61472289) and the Open Project Program of the State Key Laboratory of Digital Manufacturing Equipment and Technology (DMETKF2017016).
The original version of this article was revised due to a retrospective Open Access order.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Yong, Js., He, Fz., Li, Hr. et al. A Novel Bat Algorithm based on Cross Boundary Learning and Uniform Explosion Strategy. Appl. Math. J. Chin. Univ. 34, 480–502 (2019). https://doi.org/10.1007/s11766-019-3714-1
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11766-019-3714-1