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A cooperative bat searching algorithm with application to model predictive control

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Abstract

In this paper, a cooperative bat searching algorithm (CBA) is proposed by using a communication topology to share information among all the bats in bat algorithm (BA). Inspired by the cooperation mechanism in the distributed control theory, a cooperative term is added to the original BA to accelerate the searching process. The convergence issue is rigorously studied for CBA by using the Jury’s test. Moreover, numerical evaluation is conducted to compare CBA with other variants of BA by solving fifteen benchmark functions from IEEE congress on evolutionary computation. The results are provided to demonstrate the effectiveness of the proposed CBA. As an application, CBA and binary CBA are equipped as the real-time optimizers in a networked model predictive control strategy to solve a balanced coordination problem. The proposed CBA showed competitive performance.

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References

  • Balamurugan K, Muthukumar K (2019) Differential Evolution algorithm for contingency analysis-based optimal location of FACTS controllers in deregulated electricity market. Soft Comput 1–17

  • Banerjee A, Mukherjee V, Ghoshal S (2013) Modeling and seeker optimization based simulation for intelligent reactive power control of an isolated hybrid power system. Swarm Evol Comput 13:85–100

    Article  Google Scholar 

  • Borrelli F, Falcone P, Keviczky T, Asgari J (2005) MPC-based approach to active steering for autonomous vehicle systems. Int J Veh Auton Syst 3(2):265–291

    Article  Google Scholar 

  • Cai X, Gao X-Z, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio Inspired Comput 8(4):205–214

    Article  Google Scholar 

  • Camacho EF, Alba CB (2013) Model predictive control. Springer, Berlin

    Google Scholar 

  • Changdar C, Pal RK, Mahapatra GS (2017) A genetic ant colony optimization based algorithm for solid multiple travelling salesmen problem in fuzzy rough environment. Soft Comput 21(16):4661–4675

    Article  Google Scholar 

  • Chen G, Guo W, Chen Y (2010) A PSO-based intelligent decision algorithm for VLSI floorplanning. Soft Comput 14(12):1329–1337

    Article  Google Scholar 

  • Chen S, Peng G-H, He X-S, Yang X-S (2018) Global convergence analysis of the bat algorithm using a Markovian framework and dynamical system theory. Expert Syst Appl 114:173–182

    Article  Google Scholar 

  • Cui L, Li G, Zhu Z, Wen Z, Lu N, Lu J (2017) A novel differential evolution algorithm with a self-adaptation parameter control method by differential evolution. Soft Comput 1–20

  • Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  • Dougherty D, Cooper D (2003) A practical multiple model adaptive strategy for single-loop MPC. Control Eng Practice 11(2):141–159

    Article  Google Scholar 

  • Eaton J, Yang S, Mavrovouniotis M (2016) Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays. Soft Comput 20(8):2951–2966

    Article  Google Scholar 

  • Gan C, Cao W, Wu M, Chen X (2018) A new bat algorithm based on iterative local search and stochastic inertia weight. Expert Syst Appl 104:202–212

    Article  Google Scholar 

  • Ganguly S, Mahto T, Mukherjee V (2017) Integrated frequency and power control of an isolated hybrid power system considering scaling factor based fuzzy classical controller. Swarm Evol Comput 32:184–201

    Article  Google Scholar 

  • Garcia CE, Prett DM, Morari M (1989) Model predictive control: theory and practice—a survey. Automatica 25(3):335–348

    Article  Google Scholar 

  • Guedes JJ, Castoldi MF, Goedtel A, Agulhari CM, Sanches DS, (2018) Differential evolution applied to line-connected induction motors stator fault identification. Soft Comput 1–10

  • Guha D, Roy PK, Banerjee S (2016) Load frequency control of interconnected power system using grey wolf optimization. Swarm Evol Comput 27:97–115

    Article  Google Scholar 

  • Hu X, Shi Y, Eberhart R (2004) Recent advances in particle swarm. Proc IEEE Congr Evol Comput 1:90–97

    Google Scholar 

  • Hui Q, Haddad WM (2008) Distributed nonlinear control algorithms for network consensus. Automatica 44:2375–2381

    Article  MathSciNet  Google Scholar 

  • Jaddi NS, Abdullah S, Hamdan AR (2015) Optimization of neural network model using modified bat-inspired algorithm. Appl Soft Comput 37:71–86

    Article  Google Scholar 

  • Kang Q, Zhou M, An J, Wu Q (2013) Swarm intelligence approaches to optimal power flow problem with distributed generator failures in power networks. IEEE Trans Autom Sci Eng 10(2):343–353

    Article  Google Scholar 

  • Kar S, Sharma KD, Maitra M (2015) Gene selection from microarray gene expression data for classification of cancer subgroups employing pso and adaptive k-nearest neighborhood technique. Expert Syst Appl 42(1):612–627

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

  • Khan K, Nikov A, Sahai A (2011) A fuzzy bat clustering method for ergonomic screening of office workplaces. In: International conference on web services and semantic technology. Springer, pp. 59–66

  • Komarasamy G, Wahi A (2012) An optimized k-means clustering technique using bat algorithm. Eur J Sci Res 84(2):26–273

    Google Scholar 

  • Kumar N, Vidyarthi DP (2016) A model for resource-constrained project scheduling using adaptive PSO. Soft Comput 20(4):1565–1580

    Article  Google Scholar 

  • Lin J-H, Chou C-W, Yang C-H, Tsai H-L et al (2012) A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems. Comput Inf Technol 2(2):56–63

    Google Scholar 

  • Li X, Tang K, Omidvar MN, Yang Z, Qin K, China H (2013) Benchmark functions for the cec 2013 special session and competition on large-scale global optimization, gene

  • Mavrovouniotis M, Yang S (2011) A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15(7):1405–1425

    Article  Google Scholar 

  • Meng X-B, Gao XZ, Liu Y, Zhang H (2015) A novel bat algorithm with habitat selection and doppler effect in echoes for optimization. Expert Syst Appl 42(17–18):6350–6364

    Article  Google Scholar 

  • Mnif S, Elkosantini S, Darmoul S, Said LB (2019) An immune network based distributed architecture to control public bus transportation systems. Swarm Evol Comput 50:100478

    Article  Google Scholar 

  • Ni J, Liu M, Ren L, Yang SX (2014) A multiagent q-learning-based optimal allocation approach for urban water resource management system. IEEE Trans Autom Sci Eng 11(1):204–214

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  • Venkat AN, Hiskens IA, Rawlings JB, Wright SJ (2008) Distributed MPC strategies with application to power system automatic generation control. IEEE Trans Control Syst Technol 16(6):1192–1206

    Article  Google Scholar 

  • Wang J-J (2018) Parameter optimization and speed control of switched reluctance motor based on evolutionary computation methods. Swarm Evol Comput 39:86–98

    Article  Google Scholar 

  • Wang Y, Boyd S (2010) Fast model predictive control using online optimization. IEEE Trans Control Syst Technol 18(2):267–278

    Article  Google Scholar 

  • Wang X, Li S, Chen MZ (2018) Composite backstepping consensus algorithms of leader-follower higher-order nonlinear multiagent systems subject to mismatched disturbances. IEEE Trans Cybern 48(6):1935–1946

    Article  Google Scholar 

  • Wen J, Wu Q, Turner D, Cheng S, Fitch J (2004) Optimal coordinated voltage control for power system voltage stability. IEEE Trans Power Syst 19(2):1115–1122

    Article  Google Scholar 

  • Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature Inspired cooperative strategies optimization. Springer, pp 65–74

  • Zhang H (2020) A binary cooperative bat algorithm based optimal topology design of leader-Follower consensus. ISA Trans 96:51–59

    Article  Google Scholar 

  • Zhang J, Yang S (2016) A novel PSO algorithm based on an incremental-PID-controlled search strategy. Soft Comput 20(3):991–1005

    Article  Google Scholar 

  • Zhang H, Yue D, Zhao W, Hu S, Dou C (2018) Distributed optimal consensus control for multiagent systems with input delay. IEEE Trans Cybern 48(6):1747–1759

  • Zhang H, Hui Q (2013) Binary multiagent coordination optimization with application to formation control design. In: Proceedings of IEEE congress evolution computing, pp 1968–1975

  • Zhang H, Hui Q (2013) Multiagent coordination optimization: a control-theoretic perspective of swarm intelligence algorithms. In: Proceedings of the IEEE congress on evolutionary computation, pp 3339–3346

  • Zhang H, Hui Q (2014) Multiagent coordination optimization based model predictive control strategy with application to balanced resource allocation. In: Proceedings of the 2010 ASME dynamic systems and control conference, p V003T40A00

  • Zhang H, Hui Q (2020) A coupled spring forced bat searching algorithm: design, analysis and evaluation. In: 2020 American control conference, pp 5016–5021

  • Zhang H, Hui Q Parallel multiagent coordination optimization algorithm: implementation, evaluation, and applications. IEEE Trans Autom Sci Eng

  • Zhang H, Liyanage S (2020) Finite-time formation control for multi-agent systems underlying heterogeneous communication topologies. In: 2020 IEEE/ASME international conference on advanced intelligent mechatronics, pp 1441–1446

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  1. Department of Mechanical Engineering, University of Louisville, Louisville, KY, 40292, USA

    Haopeng Zhang

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Correspondence to Haopeng Zhang.

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The material in this paper was partially presented at 13th IEEE Conference on Automation Science and Engineering, August 20–23, 2017, Xian China.

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Zhang, H. A cooperative bat searching algorithm with application to model predictive control. Soft Comput 25, 8325–8335 (2021). https://doi.org/10.1007/s00500-021-05755-9

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