Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 1
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature.
Go to article

Bibliography

  1.  M.J. Mahmoodabadi, R.A. Maafi, and M. Taherkhorsandi, “An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems”, Appl. Soft Comput. 52(1), 1191‒1199 (2017).
  2.  C. Lorenzini, A.S. Bazanella, L.F. Alves Pereira, and G.R. Gonçalves da Silva, “The generalized forced oscillation method for tuning PID controllers”, ISA Trans. 87(1), 68‒87 (2019).
  3.  S.K. Valluru and M. Singh, “Performance investigations of APSO tuned linear and nonlinear PID controllers for a nonlinear dynamical system”, J. Electr. Syst. Inf. Technol. 5(3), 442‒452 (2018).
  4.  M. Kumar, D. Prasad, B.S. Giri, and R.S. Singh, “Temperature control of fermentation bioreactor for ethanol production using IMC-PID controller”, Biotechnol. Rep. 22(1), e00319 (2019).
  5.  D.B. Santosh Kumar and R. Padma Sree, “Tuning of IMC based PID controllers for integrating systems with time delay”, ISA Trans. 63(1), 242‒255 (2016).
  6.  J. Prakash and K. Srinivasan, “Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor”, ISA Trans. 48(3), 273‒282 (2009).
  7.  M. Hamdy and I. Hamdan, “Robust fuzzy output feedback controller for affine nonlinear systems via T–S fuzzy bilinear model: CSTR benchmark”, ISA Trans. 57(1), 85‒92 (2015).
  8.  V. Ghaffari, S. VahidNaghavi, and A.A. Safavi, “Robust model predictive control of a class of uncertain nonlinear systems with application to typical CSTR problems”, J. Process Control. 23(4), 493‒499 (2013).
  9.  W.-D. Chang, “Nonlinear CSTR control system design using an artificial bee colony algorithm”, Simul. Modell. Pract. Theory 31(1), 1‒9 (2013)
  10.  Y.P. Wang, N.R. Watson, and H.H. Chong, “Modified genetic algorithm approach to design of an optimal PID controller for AC–DC transmission systems”, Int. J. Electr. Power Energy Syst. 24(1), 59‒69 (2002).
  11.  S.S. Jadon, R. Tiwari, H. Sharma, and J.C. Bansal, “Hybrid Artificial Bee Colony algorithm with Differential Evolution”, Appl. Soft Comput. 58(1), 11‒24 (2017).
  12.  D. Karaboga, “An Idea Based on Honey Bee Swarm for Numerical Optimization”, Technical Report-TR06, Department of Computer Engineering, Engineering Faculty, Erciyes University (2005).
  13.  J. Zhou, X.Yao, F.T.S. Chan, Y. Lin, H. Jin, L. Gao, X. Wang, “An individual dependent multi-colony artificial bee colony algorithm”, Inf. Sci. 485(1), 114‒140 (2019).
  14.  X. Chen, H. Tianfield, and K. Li, “Self-adaptive differential artificial bee colony algorithm for global optimization problems”, Swarm Evol. Comput. 45(1), 70‒91 (2019).
  15.  Y. Zhang, S. Cheng, Y. Shi, D.-Wei Gong, and X. Zhao, “Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm”, Expert Syst. Appl. 137(1), 46‒58 (2019).
  16.  R. Szczepanski, T. Tarczewski, and L.M. Grzesiak, “Adaptive state feedback speed controller for PMSM based on Artificial Bee Colony algorithm”, Appl. Soft Comput. 83(1), 105644 (2019).
  17.  Q. Wei, Z. Guo, H.C. Lau, and Z. He, “An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern”, Appl. Soft Comput. 76(1), 629‒637 (2019).
  18.  T. Sen and H.D. Mathur, “A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm”, Electr. Power Energy Syst. 78(1), 735–744 (2017).
  19.  X. Li , Z. Peng , B. Dub, J. Guo, W. Xu, and K. Zhuang, “Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems”, Comput. Ind. Eng. 113(1), 10–26 (2017).
  20.  S. Lua, X. Liua, J. Peia, M.T. Thai, and P.M. Pardalos, “A hybrid ABC-TS algorithm for the unrelated parallel-batchingmachines scheduling problem with deteriorating jobs and maintenance activity”, Appl. Soft Comput. 66(1), 168–182 (2018).
  21.  S. Goudarzi et.al., “ABC-PSO for vertical handover in heterogeneous wireless networks”, Neurocomputing 256(1), 63–81 (2017).
  22.  M.A. Awadallah, A.L. Bolaji, and M.A. Al-Betar, “A hybrid artificial bee colony for a nurse rostering problem”, Appl. Soft Comput. 35(1), 726‒739 (2015).
  23.  X. Yan, Y. Zhu, W. Zou, and L. Wang, “A new approach for data clustering using hybrid artificial bee colony algorithm”, Neurocomputing, 97(1), 241‒250 (2012).
  24.  W.-F. Gao and S.-Y. Liu, “A modified artificial bee colony algorithm”, Eng. Appl. Artif. Intell. 39(1), 3, 687‒697 (2012).
  25.  P. Pramanik and M.K. Maiti, “An inventory model for deteriorating items with inflation induced variable demand under two level partial trade credit: A hybrid ABC-GA approach”, Biotechnol. Rep. 85(1), 194–207 (2019).
  26.  V. Hajisalem and S.Babaie, “A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection”, Comput. Networks 136(1), 37–50 (2018).
  27.  W. Chmiel, P. Kadłuczka, J. Kwiecień, and B. Filipowicz, “A comparison of nature inspired algorithms for the quadratic assignment problem”, Bull. Pol. Acad. Sci. Tech. Sci. 65(4), 513‒522 (2017).
  28.  Y. Li and X. Wang, “Improved dolphin swarm optimization algorithm based on information entropy”, Bull. Pol. Acad. Sci. Tech. Sci. 67(4), 679‒685 (2019).
  29.  R. Gao, A. O’dywer, and E. Coyle, “A Nonlinear PID control for CSTR using local model networks”, Proceedings of 4th World Congress on Intelligent Control and Automation, Shanghai, China, 2002.
  30.  K. Vijayakumar and M. Thathan, “Enhanced ABC Based PID Controller for Nonlinear Control Systems”, Indian J. Sci. Technol. 8(1), 1‒9 (2015).
  31.  D. Ustuna and A. Akdagli, “Design of band–notched UWB antenna using a hybrid optimization based on ABC and DE algorithms”, Int. J. Electron. Commun. 87(1), 10–21 (2018).
  32.  D. Zhang, R. Dong, Y.-W. Si, F. Ye, Q. Cai, “A hybrid swarm algorithm based on ABC and AIS for 2L-HFCVRP”, Appl. Soft Comput. 64(1), 468–479 (2018).
  33.  S. Surjanovic and D. Bingham, “Virtual Library of Simulation Experiments: Test Functions and Datasets” [Online]. Available: http:// www.sfu.ca/~ssurjano [Accessed: January 21, 2021].
  34.  D. T. Pham and M. Castellani, “Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms”, Soft Comput. (18), 871–903 (2014).
  35.  Y. Zhang, P. Wang, L. Yang, Y. Liu, Y. Lu, and X. Zhu, “Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer”, Appl. Sci. 10(14), 4821, 1‒29 (2020).
  36.  K. Anbarasan and K. Srinivasan, “Design of RTDA controller for industrial process using SOPDT model with minimum or non-minimum zero”, ISA Trans. 57, 231–244 (2015).
Go to article

Authors and Affiliations

Nedumal Pugazhenthi P
1
S. Selvaperumal
1
ORCID: ORCID
K. Vijayakumar
2

  1. Department of EEE, Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India
  2. Department of electronics and instrumentation, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India

This page uses 'cookies'. Learn more