Skip to main content
SpringerLink
Log in

Adaptive parameter control of search group algorithm using fuzzy logic applied to networked control systems

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Search group algorithm (SGA) is one of the newest nature-inspired heuristics for solving different engineering optimization problems. Like other evolutionary algorithms, SGA suffers from the parameters tuning, which is considerably dependent on the problem. The purpose of this paper is to introduce an adaptive parameter control using fuzzy logic, namely fuzzy SGA (FSGA), for enhancing the solution quality of the basic SGA. In FSGA, a fuzzy system is incorporated to dynamically adjust the control parameter value with respect to normalized iteration and normalized error value, which are the inputs of the system. To evaluate the performance of FSGA, firstly, it is compared against those of state-of-the-art algorithms over the well-known benchmark functions. Null hypothesis significance testing is then applied to make algorithm ranking. Finally, in order to demonstrate the potential applicability of FSGA in the field of control, it is adopted to design of robust proportional-integral-derivative controller for the network-based control system dealing with time delays existed in the communication channel. The results verify the feasibility of the proposed FSGA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Abedi Pahnehkolaei SM, Alfi A, Sadollah A, Kim JH (2017) Gradient-based Water cycle algorithm with evaporation rate applied to chaos suppression. Appl Soft Comput 53:420–440

    Article  Google Scholar 

  • Ahmadizar F, Soltanian K, AkhlaghianTab F, Tsoulos I (2015) Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm. Eng Appl Artif Intell 39:1–13

    Article  Google Scholar 

  • Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014

    Article  Google Scholar 

  • Alfi A (2011) PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems. Acta Autom Sin 37(5):541–549

    MATH  Google Scholar 

  • Alfi A, Farrokhi M (2008) Force reflecting bilateral control of master–slave systems in teleoperation. J Intell Robot Syst 52:209–232

    Article  Google Scholar 

  • Alfi A, Fateh MM (2011) Intelligent identification and control using improved fuzzy particle swarm optimization. Expert Syst Appl 38:12312–12317

    Article  Google Scholar 

  • Alfi A, Bakhshi A, Yousefi M, Talebi HA (2016) Design and implementation of robust-fixed structure controller for telerobotic systems. Intelli Robot Syst. doi:10.1007/s10846-016-0335-2

    Article  Google Scholar 

  • Alikhani Koupaei J, Hosseini SMM, Maalek Ghaini FM (2016) A new optimization algorithm based on chaotic maps and golden section search method. Eng Appl Artif Intell 50:201–214

    Article  Google Scholar 

  • Amarjeet et al (2017) Harmony search based remodularization for object-oriented software systems. Comput Lang Syst Struct 47(Part 2):153–169

    Google Scholar 

  • Ameli K, Alfi A, Aghaebrahimi M (2015) A fuzzy discrete harmony search algorithm applied to annual cost reduction in radial distribution systems. Eng Optim. doi:10.1080/0305215X.2015.1120299

    Article  Google Scholar 

  • Arab A, Alfi A (2015) An adaptive gradient descent-based local search in memetic algorithm applied to optimal controller design. Inf Sci 299:117–142

    Article  MathSciNet  Google Scholar 

  • Arora V et al (2017) Synthesizing test scenarios in UML activity diagram using a bio-inspired approach. Comput Lang Syst Struct 50:1–19

    Google Scholar 

  • Årzén K (1999) A simple event-based PID controller. In: Proceedings of 14th IFAC world congress, pp 423–428 (1999)

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, CEC 2007, pp 4661–4667

  • Bhuvana J, Aravindan C (2016) Memetic algorithm with preferential local search using adaptive weights for multi-objective optimization problems. Soft Comput 20(4):1365–1388

    Article  Google Scholar 

  • Birattari M, Stützle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. In: GECCO 2002 Proceedings of the genetic and evolutionary computation conference, pp 11–18

  • Biswas A, Das S, Abraham A, Dasgupta S (2009) Design of fractional-order PI\(\lambda \)D\(\mu \) controllers with an improved differential evolution. Eng Appl Artif Intell 22(2):343–350

    Article  Google Scholar 

  • Črepinšek M, Mernik M, Liu SH (2011) Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees. Int J Innov Comput Appl 3:11–19

    Article  Google Scholar 

  • Črepinšek M, Liu SH, Mernik M (2012) A note on teaching-learning-based optimization algorithm. Inf Sci 212:79–93

    Article  Google Scholar 

  • Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45:1–33

    Article  Google Scholar 

  • Črepinšek M, Liu SH, Mernik M (2014) Replication and comparison of computational experiments in applied evolutionary computing: common pitfalls and guidelines to avoid them. Appl Soft Comput J 19:161–170

    Article  Google Scholar 

  • Črepinšek M, Liu SH, Mernik L, Mernik M (2016) Is a comparison of results meaningful from the inexact replications of computational experiments. Soft Comput 20(1):223–235

    Article  Google Scholar 

  • Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  • Dias JC, Machado P, Silva DC, Abreu PH (2014) An inverted ant colony optimization approach to traffic. Eng Appl Artif Intell 36:122–133

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. MHS’95. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39–43

  • Erol OK, Eksin I (2006) A new optimization method: Big Bang–Big Crunch. Adv Eng Softw 37:106–111

    Article  Google Scholar 

  • Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    Article  Google Scholar 

  • Fu CM et al (2017) An adaptive differential evolution algorithm with an aging leader and challengers mechanism. Appl Soft Comput 57:60–73

    Article  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68

    Article  Google Scholar 

  • Gonçalves MS, Lopez RH, Miguel LFF (2015) Search group algorithm: a new metaheuristic method for the optimization of truss structures. Comput Struct 153:165–184

    Article  Google Scholar 

  • González B, Valdez F, Melin P, Prado-Arechiga G (2015) Fuzzy logic in the gravitational search algorithm enhanced using fuzzy logic with dynamic alpha parameter value adaptation for the optimization of modular neural networks in echocardiogram recognition. Appl Soft Comput J 37:245–254

    Article  Google Scholar 

  • Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Prentice

    Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  Google Scholar 

  • Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  • Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks. IEEE, vol 4, pp 1942–1948

  • Kumar EV et al (2016) Adaptive PSO for optimal LQR tracking control of 2 DoF laboratory helicopter. Appl Soft Comput 41:77–90

    Article  Google Scholar 

  • Li C, Wu T (2011) Adaptive fuzzy approach to function approximation with PSO and RLSE. Expert Syst Appl 38(10):13266–13273

    Article  Google Scholar 

  • Liu SH, Mernik M, Hrnčič D, Črepinšek M (2013) A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova’s mass transfer model. Appl Soft Comput J 13(9):3792–3805

    Article  Google Scholar 

  • Merkle D, Middendorf M, Schmeck H (2002) Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evol Comput 6(4):333–346

    Article  Google Scholar 

  • Mernik M, Liu SH, Karaboga D, Črepinšek M (2015) On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation. Inf Sci 291(C):115–127

    Article  MathSciNet  Google Scholar 

  • Modares H, Alfi A, Sistani MBN (2010) Parameter estimation of bilinear systems based on an adaptive particle swarm optimization. Eng Appl Artif Intell 23:1105–1111

    Article  Google Scholar 

  • Montalvo I, Izquierdo J, Pérez-García R, Herrera M (2010) Improved performance of PSO with self-adaptive parameters for computing the optimal design of water supply systems. Eng Appl Artif Intell 23:727–735

    Article  Google Scholar 

  • Mousavi Y, Alfi A (2015) A memetic algorithm applied to trajectory control by tuning of fractional order proportional-integral-derivative controllers. Appl Soft Comput J 36:599–617

    Article  Google Scholar 

  • Muthukaruppan S, Er MJ (2012) A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Syst Appl 39(14):11657–11665

    Article  Google Scholar 

  • Nannen V, Eiben AE (2007) Relevance estimation and value calibration of evolutionary algorithm parameters. In: IJCAI international joint conference on artificial intelligence, pp 975–980

  • Nezamabadi-pour H (2015) A quantum-inspired gravitational search algorithm for binary encoded optimization problems. Eng Appl Artif Intell 40:62–75

    Article  Google Scholar 

  • Ochoa P, Castillo O, Soria J (2015) Differential evolution with dynamic adaptation of parameters for the optimization of fuzzy controllers. In: Castillo O, Melin P, Pedrycz W, Kacprzyk J (eds) Recent advances on hybrid approaches for designing intelligent systems. Studies in computational intelligence, vol 547. Springer, Cham, pp 573–592

  • Pan I, Das S, Gupta A (2011) Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. ISA Trans 50(1):28–36

    Article  Google Scholar 

  • Pazhoohesh F, Hasanvand S, Mousavi Y (2015) Optimal harmonic reduction approach for PWM AC-AC converter using nested memetic algorithm. Soft Comput. doi:10.1007/s00500-015-1979-8

    Article  Google Scholar 

  • Pulkkinen J, Koivo HN, Makela K (1993) Tuning of a robust PID controller-application to heating process in extruder. In: Proceedings of IEEE international conference on control and applications, pp 811–816

  • Qi C et al (2016) Smith predictor based delay compensation for a hardware-in-the-loop docking simulator. Mechatronics 36:63–76

    Article  Google Scholar 

  • Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  • Reynoso-Meza G, Blasco X, Sanchis J, Martınez M (2014) Controller tuning using evolutionary multi-objective optimisation: current trends and applications. Control Eng Pract 28(1):58–63

    Article  Google Scholar 

  • Rezaee Jordehi A (2015) Brainstorm optimisation algorithm (BSOA): an efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems. Int J Electr Power Energy Syst 69:48–57

    Article  Google Scholar 

  • Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612

    Article  Google Scholar 

  • Sadollah A, Yoo DG, Kim JH (2016) Improved mine blast algorithm for optimal cost design of water distribution systems. Eng Optim 47(12):1602–1618

    Article  Google Scholar 

  • Saenphon T, Phimoltares S, Lursinsap C (2014) Combining new fast opposite gradient search with ant colony optimization for solving travelling salesman problem. Eng Appl Artif Intell 35:324–334

    Article  Google Scholar 

  • Scrucca L (2013) GA: a package for genetic algorithms in R. J Stat Softw 53(4):1–37

    Article  Google Scholar 

  • Sombra A, Valdez F, Melin P, Castillo O (2013) A new gravitational search algorithm using fuzzy logic to parameter adaptation. In: 2013 IEEE congress on evolutionary computation, CEC 2013, pp 1068–1074

  • Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Nat Comput, pp 1–50

  • Tang K, Peng F, Chen G, Yao X (2014) Population-based algorithm portfolios with automated constituent algorithms selection. Inf Sci 279:94–104

    Article  Google Scholar 

  • Tipsuwan Y, Chow MY (2004) On the gain scheduling for networked PI controller over IP network. IEEE/ASME Trans Mechatron 9(3):491–498

    Article  Google Scholar 

  • Valdez F, Melin P, Castillo O (2010) Fuzzy control of parameters to dynamically adapt the PSO and GA algorithms. In: IEEE World congress on computational intelligence, WCCI 2010

  • Veček N, Mernik M, Črepinšek M (2014) A chess rating system for evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms. Inf Sci 277:656–679

    Article  MathSciNet  Google Scholar 

  • Veček N, Mernik M, Filipič B, Črepinšek M (2016) Parameter tuning with chess rating system (CRS-Tuning) for meta-heuristic algorithms. Inf Sci 372:446–469

    Article  Google Scholar 

  • Veček N, Črepinšek M, Mernik M (2017) On the influence of the number of algorithms, problems, and independent runs in the comparison of evolutionary algorithm. Appl Soft Comput 54:23–45

    Article  Google Scholar 

  • Wang H, Wang D, Yang S (2009) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput 13(8–9):763–780

    Article  Google Scholar 

  • Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Studies in computational intelligence, vol 284, pp 65–74

  • Yang XS, Deb S (2009) Cuckoo search via L’evy flights. In: 2009 world congress on nature and biologically inspired computing, NABIC 2009—Proceedings, pp 210–214

  • Yen J, Langari R (1999) Fuzzy logic: intelligence, control, and information. Prentice Hall, Prentice

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  • Zhuang M, Atherton DP (1993) Automatic tuning of optimum PID controllers. In: IEE proceedings D control theory and applications

Download references

Acknowledgements

The authors would like to express their sincere appreciation to the anonymous reviewers for their insightful comments which greatly improve the quality of this paper.

Author information

Authors and Affiliations

  1. Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, 36199-95161, Iran

    Seyedeh Fa’ezeh Hosseini Noorbin & Alireza Alfi

Authors
  1. Seyedeh Fa’ezeh Hosseini Noorbin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Alfi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Alfi.

Ethics declarations

Conflict of interest

Both authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noorbin, S.F.H., Alfi, A. Adaptive parameter control of search group algorithm using fuzzy logic applied to networked control systems. Soft Comput 22, 7939–7960 (2018). https://doi.org/10.1007/s00500-017-2742-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2742-0

Keywords

Search

Navigation