Skip to main content
SpringerLink
Log in

Electron radar search algorithm: a novel developed meta-heuristic algorithm

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

Abstract

This paper introduces a new optimization algorithm called electron radar search algorithm (ERSA) inspired by the electron discharge mechanism. It is based on the natural phenomenon of electric flow as the form of electron discharge through a gas, liquid, or solid environment. When the voltage between separated electrodes (anode and cathode) increases, electrons tendency to emission from a low potential state to the higher potential condition is grown up. However, electrons are trying to find the best path with the least resistance in the medium. At each point, electrons evaluate the surrounding environment with a radar mechanism and least resistance path is selected for the next move. Hence, in this paper, a novel developed meta-heuristic algorithm based on the electrons’ search approach is presented and the algorithm is benchmarked on 20 mathematical functions with four well-known methods for validation and verification tests. Moreover, the algorithm is implemented in two engineering design problems (tension/expression spring and welded beam design optimization) and the results demonstrate that the ERSA performs more efficiently for solving unknown search spaces and the algorithm found best solution in approximately 95% of the reviewed benchmark functions.

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

Similar content being viewed by others

References

  • Abbas NM, Solomon DG, Bahari MF (2007) A review on current research trends in electrical discharge machining (EDM). Int J Mach Tools Manuf 47:1214–1228

    Google Scholar 

  • Abraham A, Das S, Roy S (2008) Swarm intelligence algorithms for data clustering. Springer, Boston, MA, Soft Comput. Knowl. Discov. Data Min., pp 279–313

    MATH  Google Scholar 

  • Arora J (2011) Introduction to optimum design, 3rd Editio edn. Elsevier, Amsterdam

    Google Scholar 

  • Bäck T, Schwefel H-P (1995) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1:1–23

    Google Scholar 

  • Banzhaf W, Nordin P, Keller RE, Francone FD (1997) Genetic programming: an introduction, 1st edn. Morgan Kaufmann, Burlington

    MATH  Google Scholar 

  • Beauchamp K (2001) History of telegraphy, 1st edn. The Institution of Engineering and Technology, London

    Google Scholar 

  • Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization. Part I: theory. Int J Numer Methods Eng 21:1561–1748

    Google Scholar 

  • Byrne C, Tainsky M, Fuchs E (1994) Programming gene expression in developing epidermis. Development 120:2369–2383

    Google Scholar 

  • Cacchiani V, D’Ambrosio C (2017) A branch-and-bound based heuristic algorithm for convex multi-objective MINLPs. Eur J Oper Res 260:920–933

    MathSciNet  MATH  Google Scholar 

  • Cantarella GE, de Luca S, di Pace R, Memoli S (2015) Network signal setting design: meta-heuristic optimisation methods. Transp Res Part C Emerg Technol 55:24–45

    Google Scholar 

  • Chen T, Tsao HL (2009) Using a hybrid meta-evolutionary rule mining approach as a classification response model. Expert Syst Appl 36:1999–2007

    Google Scholar 

  • Christensen J, Bastien C (2015) Seven: heuristic and meta-heuristic optimization algorithms. In: Christensen J (ed) Nonlinear optimization of vehicle safety structures 1st edn. Butterworth-Heinemann, pp 277–314

  • Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127

    Google Scholar 

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

    MATH  Google Scholar 

  • Daneshmand A, Facchinei F, Kungurtsev V, Scutari G (2015) Hybrid random/deterministic parallel algorithms for convex and nonconvex big data optimization. IEEE Trans Signal Process 63:3914–3929. https://doi.org/10.1109/TSP.2015.2436357

    Article  MathSciNet  MATH  Google Scholar 

  • Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15:4–31

    Google Scholar 

  • Deb K (1991) Optimal design of a welded beam via genetic algorithms Read More. AIAA J 29:2013–2015. https://doi.org/10.2514/3.10834

    Article  Google Scholar 

  • Deepa O (2016) Swarm intelligence from natural to artificial systems: ant colony optimization. Int J Appl Graph Theory Wirel Ad Hoc Netw Sens Netw 8:9–17

    Google Scholar 

  • Du D-Z, Pardalos PM (1999) Handbook of Combinatorial Optimization. Springer, Boston. https://doi.org/10.1007/978-1-4613-0303-9

    Book  MATH  Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. Micro Mach. Hum. Sci, Nagoya

    Google Scholar 

  • Ebrahimi M, ShafieiBavani E, Wong RK, Fong S, Fiaidhi J (2017) An adaptive meta-heuristic search for the internet of things. Futur Gener Comput Syst 76:486–494

    Google Scholar 

  • Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37:106–111. https://doi.org/10.1016/j.advengsoft.2005.04.005

    Article  Google Scholar 

  • Floudas CA (2000) Deterministic global optimization: theory, methods and applications, 1st edn. Springer, US

    Google Scholar 

  • Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3:1–16

    Google Scholar 

  • Fridman A, Chirokov A, Gutsol A (2005) Non-thermal atmospheric pressure. J Phys D Appl Phys 38:1–24. https://doi.org/10.1088/0022-3727/38/2/R01

    Article  Google Scholar 

  • Gandhi KR, Uma SM, Karnan M (2012) A hybrid meta heuristic algorithm for discovering classification rule in data mining. Int J Comput Sci Netw Secur 12:116–122

    Google Scholar 

  • Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22:1239–1255. https://doi.org/10.1007/s00521-012-1028-9

    Article  Google Scholar 

  • Glover F (1989) Tabu search—part I. ORSA J Comput 1:190–206. https://doi.org/10.1287/ijoc.1.3.190

    Article  MATH  Google Scholar 

  • Goldenberg M (2017) The heuristic search research framework. Knowledge-Based Syst 129:1–3

    Google Scholar 

  • Griffis SE, Bell JE, Closs DJ (2012) Metaheuristics in logistics and supply chain management. J Bus Logist 33:90–106

    Google Scholar 

  • Gutjahr WJ (2010) Stochastic search in metaheuristics. In: Price CC, Zhu J (eds) International series in operations research and management science, 1st edn. Springer, Boston, pp 573–97

    Google Scholar 

  • He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99. https://doi.org/10.1016/j.engappai.2006.03.003

    Article  Google Scholar 

  • Hills TT, Todd PM, Lazer D, Redish AD, Couzin ID (2015) Exploration versus exploitation in space, mind, and society. Trends Cogn Sci. https://doi.org/10.1016/j.tics.2014.10.004

    Article  Google Scholar 

  • Ho K, Newman S (2003) State of the art electrical discharge machining (EDM). Int J Mach Tools Manuf 43:1287–1300

    Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor

    Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press Cambridge, MA

    Google Scholar 

  • Horst R, Tuy H (1996) Global optimization: Deterministic approaches, 3rd edn. Springer-Verlag, Berlin Heidelberg

    MATH  Google Scholar 

  • Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186:340–356

    MathSciNet  MATH  Google Scholar 

  • Junqin XU, Jihui Z (2014) Exploration-exploitation tradeoffs in metaheuristics: survey and analysis. In: Proc. 33rd Chinese control conf, pp 8633–8

  • Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84. https://doi.org/10.1016/j.advengsoft.2017.03.014

    Article  Google Scholar 

  • Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27. https://doi.org/10.1016/j.compstruc.2014.04.005

    Article  Google Scholar 

  • Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system. Acta Mech 213:267–289. https://doi.org/10.1007/s00707-009-0270-4

    Article  MATH  Google Scholar 

  • Kaveh A, Zolghadr A (2016) A novel meta-heuristic algorithm: tug of war optimization. Int J Optim Civ Eng 6:469–492

    Google Scholar 

  • Keidar M, Beilis I (2013) Plasma engineering: applications from aerospace to bio and nanotechnology. Elsevier Inc., Amsterdam

    Google Scholar 

  • Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220:671–680

    MathSciNet  MATH  Google Scholar 

  • Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933

    MATH  Google Scholar 

  • Loeb LB, Meek JM (1941) The mechanism of the electric spark. Stanford University Press, Palo Alto

    Google Scholar 

  • Maniezzo V, Carbonaro A (2002) Ant colony optimization: an overview. Springer, Boston, MA, Oper. Res. Sci. Interfaces Ser., pp 469–492

    MATH  Google Scholar 

  • Meek JM, Craggs JD (1978) Electrical breakdown of gases. Wiley, Hoboken

    MATH  Google Scholar 

  • Mirjalili S, Mohammad S, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  • Pinebrook WE (1987) The evolution strategy. Int J Model Simul 7:81–84

    Google Scholar 

  • Pishvaee MS, Farahani RZ, Dullaert W (2010) A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput Oper Res 37:1100–1112

    MATH  Google Scholar 

  • Qureshi AS, Khan A, Zameer A, Usman A (2017) Wind power prediction using deep neural network based meta regression and transfer learning. Appl Soft Comput 58:742–755

    Google Scholar 

  • Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. J Eng Ind 98:1021–1025

    Google Scholar 

  • Ranaboldo M, García-Villoria A, Ferrer-Martí L, Moreno RP (2015) A meta-heuristic method to design off-grid community electrification projects with renewable energies. Energy 93:2467–2482

    Google Scholar 

  • Rashedi E, Nezamabadi-pour H, Saryazdi SGSA (2009) A gravitational search algorithm. Inf Sci 179:2232–2248

    MATH  Google Scholar 

  • Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput J 36:315–333. https://doi.org/10.1016/j.asoc.2015.07.028

    Article  Google Scholar 

  • Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proc. 1999 congr. evol. comput. (Cat. No. 99TH8406), IEEE, Washington, pp 1945–50

  • Sorensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22:3–18. https://doi.org/10.1111/itor.12001

    Article  MathSciNet  MATH  Google Scholar 

  • Vidal T, Battarra M, Subramanian A, Erdogan G (2015) Hybrid metaheuristics for the clustered vehicle routing problem. Comput Oper Res 58:87–99

    MathSciNet  MATH  Google Scholar 

  • Walters JP (1969) Historical advances in spark emission spectroscopy. Appl Spectrosc 23:317–331

    Google Scholar 

  • Xhafa F, Abraham A (2008) Metaheuristics for scheduling in industrial and manufacturing applications. Springer-Verlag, Berlin Heidelberg

    MATH  Google Scholar 

  • Xiao D (2016) Gas discharge and gas insulation. Springer-Verlag, Berlin Heidelberg

    Google Scholar 

  • Yang X-S (2009) Firefly algorithms for multimodal optimization. Springer, Berlin, Heidelberg, Stoch. Algorithms Found. Appl., pp 169–178

    MATH  Google Scholar 

  • Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Springer, Berlin, Heidelberg, Nat. Inspired Coop. Strateg. Optim., pp 65–74

    MATH  Google Scholar 

  • Yao X, Liu Y (1996) Fast evolutionary programming. In: Proceedings of the fifth annual conference on evolutionary programming, pp 451–60

  • Zaepffel C, Hong D, Bauchire J-M (2007) Experimental study of an electrical discharge used in reactive media ignition. J Phys D Appl Phys 40:1052–1058

    Google Scholar 

  • Zheng Y-J (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Sajjad Rahmanzadeh & Mir Saman Pishvaee

Authors
  1. Sajjad Rahmanzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mir Saman Pishvaee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mir Saman Pishvaee.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahmanzadeh, S., Pishvaee, M.S. Electron radar search algorithm: a novel developed meta-heuristic algorithm. Soft Comput 24, 8443–8465 (2020). https://doi.org/10.1007/s00500-019-04410-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04410-8

Keywords

Search

Navigation