A cooperative bat searching algorithm with application to model predictive control
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.
References
[1]
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
[2]
Banerjee A, Mukherjee V, and Ghoshal S Modeling and seeker optimization based simulation for intelligent reactive power control of an isolated hybrid power system Swarm Evol Comput 2013 13 85-100
[3]
Borrelli F, Falcone P, Keviczky T, and Asgari J MPC-based approach to active steering for autonomous vehicle systems Int J Veh Auton Syst 2005 3 2 265-291
[4]
Cai X, Gao X-Z, and Xue Y Improved bat algorithm with optimal forage strategy and random disturbance strategy Int J Bio Inspired Comput 2016 8 4 205-214
[5]
Camacho EF and Alba CB Model predictive control 2013 Berlin Springer
[6]
Changdar C, Pal RK, and Mahapatra GS A genetic ant colony optimization based algorithm for solid multiple travelling salesmen problem in fuzzy rough environment Soft Comput 2017 21 16 4661-4675
[7]
Chen G, Guo W, and Chen Y A PSO-based intelligent decision algorithm for VLSI floorplanning Soft Comput 2010 14 12 1329-1337
[8]
Chen S, Peng G-H, He X-S, and Yang X-S Global convergence analysis of the bat algorithm using a Markovian framework and dynamical system theory Expert Syst Appl 2018 114 173-182
[9]
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
[10]
Dorigo M, Birattari M, and Stützle T Ant colony optimization IEEE Comput Intell Mag 2006 1 4 28-39
[11]
Dougherty D and Cooper D A practical multiple model adaptive strategy for single-loop MPC Control Eng Practice 2003 11 2 141-159
[12]
Eaton J, Yang S, and Mavrovouniotis M Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays Soft Comput 2016 20 8 2951-2966
[13]
Gan C, Cao W, Wu M, and Chen X A new bat algorithm based on iterative local search and stochastic inertia weight Expert Syst Appl 2018 104 202-212
[14]
Ganguly S, Mahto T, and Mukherjee V Integrated frequency and power control of an isolated hybrid power system considering scaling factor based fuzzy classical controller Swarm Evol Comput 2017 32 184-201
[15]
Garcia CE, Prett DM, and Morari M Model predictive control: theory and practice—a survey Automatica 1989 25 3 335-348
[16]
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
[17]
Guha D, Roy PK, and Banerjee S Load frequency control of interconnected power system using grey wolf optimization Swarm Evol Comput 2016 27 97-115
[18]
Hu X, Shi Y, and Eberhart R Recent advances in particle swarm Proc IEEE Congr Evol Comput 2004 1 90-97
[19]
Hui Q and Haddad WM Distributed nonlinear control algorithms for network consensus Automatica 2008 44 2375-2381
[20]
Jaddi NS, Abdullah S, and Hamdan AR Optimization of neural network model using modified bat-inspired algorithm Appl Soft Comput 2015 37 71-86
[21]
Kang Q, Zhou M, An J, and Wu Q Swarm intelligence approaches to optimal power flow problem with distributed generator failures in power networks IEEE Trans Autom Sci Eng 2013 10 2 343-353
[22]
Kar S, Sharma KD, and Maitra M Gene selection from microarray gene expression data for classification of cancer subgroups employing pso and adaptive k-nearest neighborhood technique Expert Syst Appl 2015 42 1 612-627
[23]
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia, pp 1942–1948
[24]
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
[25]
Komarasamy G and Wahi A An optimized k-means clustering technique using bat algorithm Eur J Sci Res 2012 84 2 26-273
[26]
Kumar N and Vidyarthi DP A model for resource-constrained project scheduling using adaptive PSO Soft Comput 2016 20 4 1565-1580
[27]
Lin J-H, Chou C-W, Yang C-H, Tsai H-L, et al. A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems Comput Inf Technol 2012 2 2 56-63
[28]
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
[29]
Mavrovouniotis M and Yang S A memetic ant colony optimization algorithm for the dynamic travelling salesman problem Soft Comput 2011 15 7 1405-1425
[30]
Meng X-B, Gao XZ, Liu Y, and Zhang H A novel bat algorithm with habitat selection and doppler effect in echoes for optimization Expert Syst Appl 2015 42 17–18 6350-6364
[31]
Mnif S, Elkosantini S, Darmoul S, and Said LB An immune network based distributed architecture to control public bus transportation systems Swarm Evol Comput 2019 50 100478
[32]
Ni J, Liu M, Ren L, and Yang SX A multiagent q-learning-based optimal allocation approach for urban water resource management system IEEE Trans Autom Sci Eng 2014 11 1 204-214
[33]
Storn R and Price K Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces J Glob Optim 1997 11 4 341-359
[34]
Venkat AN, Hiskens IA, Rawlings JB, and Wright SJ Distributed MPC strategies with application to power system automatic generation control IEEE Trans Control Syst Technol 2008 16 6 1192-1206
[35]
Wang J-J Parameter optimization and speed control of switched reluctance motor based on evolutionary computation methods Swarm Evol Comput 2018 39 86-98
[36]
Wang Y and Boyd S Fast model predictive control using online optimization IEEE Trans Control Syst Technol 2010 18 2 267-278
[37]
Wang X, Li S, and Chen MZ Composite backstepping consensus algorithms of leader-follower higher-order nonlinear multiagent systems subject to mismatched disturbances IEEE Trans Cybern 2018 48 6 1935-1946
[38]
Wen J, Wu Q, Turner D, Cheng S, and Fitch J Optimal coordinated voltage control for power system voltage stability IEEE Trans Power Syst 2004 19 2 1115-1122
[39]
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature Inspired cooperative strategies optimization. Springer, pp 65–74
[40]
Zhang H A binary cooperative bat algorithm based optimal topology design of leader-Follower consensus ISA Trans 2020 96 51-59
[41]
Zhang J and Yang S A novel PSO algorithm based on an incremental-PID-controlled search strategy Soft Comput 2016 20 3 991-1005
[42]
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
[43]
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
[44]
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
[45]
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
[46]
Zhang H, Hui Q (2020) A coupled spring forced bat searching algorithm: design, analysis and evaluation. In: 2020 American control conference, pp 5016–5021
[47]
Zhang H, Hui Q Parallel multiagent coordination optimization algorithm: implementation, evaluation, and applications. IEEE Trans Autom Sci Eng
[48]
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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Publication History
Published: 01 July 2021
Accepted: 15 March 2021
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