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  • 1 Institute of Energy Engineering and Chemical Machinery, Faculty of Mechanical Engineering, University of Miskolc, Miskolc, H-3515, Miskolc-Egyetemváros, Hungary
  • | 2 Department of Mechanical Engineering, Faculty of Engineering, University of Kufa, Iraq
  • | 3 Department of Structures and Water Resources, Faculty of Engineering, University of Kufa, Iraq
Open access

Abstract

This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.

Abstract

This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.

1 Introduction

During its history, optimization algorithms have been inspired by natural or human-made phenomena to introduce mathematical formulation that can solve problems in different fields of sciences. Specifically, optimization algorithms used to find the maximum or minimum of a function, and they have a wide range of applications in the industry [1] and engineering problems like as robotic [2] and structures [3]. Developers are more interested in phenomena that could inspire them to develop a new method that can solve new problems or find the best solutions for the existing ones. One of the inspiration engines is flock of animal, birds, and insects that lead to developing swarm intelligence [4, 5] methods; this term can be defined as accumulative and shared knowledge among a group of individuals, and this kind of intelligence cannot be reached by one of them alone. Examples of swarm intelligence Particle Swarm Optimization (PSO) [6], Artificial Bee Colony (ABC) [7], and Grey Wolf Optimization (GWO) [8]. Not all the biologically inspired algorithms are swarm intelligence; bacteria and invasive weeds optimization do not follow the rules of a swarm. In this article, a biologically inspired algorithm from the fertilization process in the reproductive tract of mammal animals during reproduction is presented. The new algorithm is called Fertilization Optimization (FO) algorithm. Computationally expensive benchmarks CEC2015 [9] are employed during experiments. On these mathematical optimization problems, FO was compared with other meta heuristics. Remarkably, FO has shown great performance and overcome many other algorithms in many cases. The variety and difficulty of the mathematical optimization problems that FO could pass through successfully have proved the reliability of the fertilization algorithm for mathematical optimization. In brief, the FO algorithms can be described as follows.

Each solution have a position (X) and velocity (v) in the search space. For each iteration, the velocity decreased by some value δ
vt+1=δvt,0<δ<1,
Vit+1=Vite1vt+1,
where t is the number of iteration in the optimization process. The solutions move in the search space using levy flight L and the solution is updated by the following equation:
Xit+1=L(XitVit),(i=1,2,n),
where i the index of solution components, and n is the total number of variables in the solution (3). The average value of the best Xfirstt, medium best Xmiddlet, and worst solutions Xendt can also have effect on the update solution process:
Xit+1=Xfirstt+Xmiddlet+Xendt3.
The combination of equations (1)−(4) give the search engine of the F algorithm:
Xijt+1=XijtVijte1vt+1+L(XitVijt)Xfirstt+Xmiddlet+Xendt3,(j=1,2,m),
where m is the number of variables in the proposed solution, and the pseudocode can be seen in Code 1.

The pseudocode

Define problem parameters (No. of variables, objective, limits)

Define algorithm parameters (population size, max iteration, velocity reduction coefficient, damping)

Initialize random positions and velocities for the population

Initialize best cost

Repeat from 1 to max iteration

Define new solution

Repeat from 1 to the number of population

Use equation (26) to calculate new position of the new solution

Stop when the maximum number of population is reached

Merge the old solution with the new solution

Sort solutions

Choose the first solution in the population

Choose the solution in the middle of population

Choose the last solution in the population

The first solution in the sorted group is the best solution

The cost of the best solution is the best cost

Update best cost

Stop when the maximum number of iterations is reached

2 Results and discussion

CEC2015 benchmark functions, which are described in Tables 1 and 2, are used in this study to examine the performance of the FO algorithm. The run conditions on CEC2015 experiment are: variable dimensions 10, population size 10, maximum number of iterations 1,000, and 20 independent runs. Firstly, FO algorithm is compared with Hybrid Particle Swarm Optimization algorithm and FireFly algorithm (HPSOFF) [10], and Hybrid Firefly and Particle Optimization (HFPO) algorithm [11]. Tables 3 and 4 show the results of comparison on mean solutions and standard deviation among FO, HPSOFF, and HFPSO.

Table 1.

CEC2015 expensive benchmark problems F1 to F9

CEC2015
TypeNo.Descriptionfmin
Unimodal functionsF1Rotated Bent Cigar Function100
F2Rotated Discus Function200
Simple Multimodal FunctionsF3Shifted and Rotated Weierstrass Function300
F4Shifted and Rotated Schwefel's Function400
F5Shifted and Rotated Katsuura Function500
F6Shifted and Rotated HappyCat Function600
F7Shifted and Rotated HGBat Function700
F8Shifted and Rotated Expanded Griewank's plus Rosenbrock's Function800
F9Shifted and Rotated Expanded Scaffer's F6 Function900
Table 2.

CEC2015 expensive benchmark problems F10 to F15

CEC2015
TypeNo.Descriptionfmin
Hybrid functionsF10Hybrid Function 1 (N = 3)1,000
F11Hybrid Function 2 (N = 4)1,100
F12Hybrid Function 3 (N = 5)1,200
Composition FunctionsF13Composition Function 1 (N = 5)1,300
F14Composition Function 2 (N = 3)1,400
F15Composition Function 3 (N = 5)1,500
Table 3.

Standard deviation results of the FO algorithm vs. HPSOFF and HFPSO on CEC2015

HPSOFFHFPSOFO
F13.4292E+076.6375E+060E+00
F21.2383E+041.5696E+041.9569E−06
F31.5636E+001.4189E+007.0195E−02
F43.0718E+023.9950E+021.8913E+00
F58.2275E−015.7466E−012.0303E+02
F61.4097E−011.4584E−017.1663E−10
F79.3694E−012.5433E−016.1138E+00
F82.8927E+004.0866E+004.7333E+04
F92.3372E−012.6387E−014.6656E−13
F102.9730E+053.3036E+058.8424E+04
F111.9652E+002.6814E+000E+00
F129.5565E+011.0221E+022.9857E−01
F132.5959E+012.8341E+013.3013E+01
F145.0554E+005.8221E+002.8957E+02
F151.8650E+021.0398E+026.8567E+00
Table 4.

Average solutions results of the FO algorithm vs. HPSOFF and HFPSO on CEC2015

HPSOFFHFPSOFO
F14.8387E+071.3768E+077.0974E+07
F23.8331E+043.8542E+041.1254E+10
F33.0845E+023.0671E+023.2049E+02
F41.7084E+031.3159E+034.8685E+02
F55.0273E+025.0250E+022.1652E+03
F66.0063E+026.0054E+021.6116E+06
F77.0087E+027.0060E+027.5666E+02
F88.0740E+028.0773E+021.6292E+05
F99.0388E+029.0393E+021.0413E+03
F103.5402E+053.3099E+056.8481E+04
F111.1067E+031.1074E+031.4195E+03
F121.4517E+031.3983E+031.3391E+03
F131.6333E+031.6452E+031.3908E+03
F141.6053E+031.6021E+031.5594E+04
F151.8365E+031.9233E+032.0528E+03

Table 5 reveals the comparison on standard deviation results among FO, PSO, FFPSO algorithm [12], and FireFly (FF) algorithm while Table 6 reveals the comparison on mean solutions results among the same algorithms in Table 5.

Table 5.

Standard deviation results of the FO algorithm vs. PSO, FF, and FFPSO on CEC2015

PSOFFFFPSOFO
F11.3549E+082.8945E+084.9786E+090E+00
F21.5114E+049.7404E+034.6261E+081.9569E−06
F31.3259E+001.2487E+001.6124E+007.0195E−02
F43.5521E+023.2112E+022.6203E+021.8913E+00
F56.3611E−015.9796E−019.3430E−012.0303E+02
F62.8490E−015.8361E−011.3580E+007.1663E−10
F71.8947E+005.8077E+003.3329E+016.1138E+00
F82.7690E+011.7256E+022.7423E+054.7333E+04
F93.2749E−012.3842E−011.7883E−014.6656E−13
F101.9786E+056.5054E+057.7896E+078.8424E+04
F112.9153E+002.4020E+006.7179E+010E+00
F121.1574E+029.1615E+014.3894E+022.9857E−01
F131.9141E+012.9519E+019.8971E+023.3013E+01
F144.5254E+003.5980E+004.2292E+012.8957E+02
F151.4570E+027.4514E+011.0567E+026.8567E+00
Table 6.

Average solutions results of the FO algorithm vs. PSO, FF, and FFPSO on CEC2015

PSOFFFFPSOFO
F12.4553E+084.3059E+081.6287E+107.0974E+07
F23.8112E+043.3304E+041.4957E+081.1254E+10
F33.0779E+023.0773E+023.1455E+023.2049E+02
F42.2534E+031.5473E+033.1120E+034.8685E+02
F55.0277E+025.0293E+025.0350E+022.1652E+03
F66.0089E+026.0092E+026.0673E+021.6116E+06
F77.0193E+027.0586E+028.0586E+027.5666E+02
F88.1583E+028.6344E+022.7632E+051.6292E+05
F99.0391E+029.0395E+029.0451E+021.0413E+03
F102.9540E+055.3162E+055.1186E+076.8481E+04
F111.1088E+031.1080E+031.2198E+031.4195E+03
F121.4620E+031.3995E+032.1953E+031.3391E+03
F131.6415E+031.6437E+033.0005E+031.3908E+03
F141.6076E+031.6111E+031.6770E+031.5594E+04
F151.9149E+031.9269E+032.1840E+032.0528E+03

Another experiment has been done to compare the performance of the FO algorithm on large scale optimization problems against Ant Lion Optimizer ALO [13], Butterfly Optimization Algorithm (BOA) [14], GWO [7], PSO, Sine Cosine Algorithm (SCA) optimization [15], Dynamic Differential Annealed Optimization (DDAO) [16], Bat Algorithm (BA) [17], and Tree-Seed Algorithm (TSA) [18]. Tables 7 and 8 illustrate the statistical results for this test in terms of best solution (Best), worst solution (Worst), mean solution (Mean), and STandard Deviation (STD). Four large scale optimization problems are chosen in these experiments, and the run conditions are: variable dimensions 1,000, population size 25, number of iterations 100, and 51 independent runs. The description and formulation of the large scale problems can be written as follows:

  • F16: Rastrigin: f(x)=10n+i=1n[xi210cos(2πxi)], Range = [−5.12, 5.12], Fmin = 0,

  • F17: f(x)=i=1n1[100(xi+1xi2)2+(1xi)2], Range = [−2.048, 2.048], Fmin = 0,

  • F18: f(x)=aexp(b1di=1dxi2)exp(b1di=1dcos(cxi))+a+exp(1), Range = [−32.768, 32.768], Fmin = 0,

  • F19: f(x)=i=1dxi24000i=1dcos(xii)+1, Range = [−600, 600], Fmin = 0.

Table 7.

Results for large scale optimization on F16 and F17

FunctionF16F17
ALOBest1.2463E+049.6145E+04
Worst1.5088E+042.6251E+05
Mean1.3352E+041.2072E+05
STD5.7343E+022.5922E+04
BOABest0.0000E+009.9873E+02
Worst1.1460E−109.9894E+02
Mean3.3526E−129.9885E+02
STD1.5967E−114.6350E−02
GWOBest6.3429E+033.3616E+03
Worst7.4800E+037.2573E+03
Mean6.9049E+034.8800E+03
STD2.6947E+028.9692E+02
PSOBest1.4774E+044.2109E+05
Worst1.7302E+044.6887E+05
Mean1.6192E+044.5065E+05
STD6.4158E+021.0330E+04
SCABest5.1110E+021.2307E+05
Worst4.6834E+033.0827E+05
Mean1.7431E+032.3237E+05
STD9.1819E+023.8524E+04
DDAOBest2.2573E−019.9897E+02
Worst6.4277E+031.4620E+03
Mean5.7423E+021.0382E+03
STD1.0834E+038.9853E+01
BABest1.2667E+045.4067E+04
Worst1.7947E+044.3425E+05
Mean1.4585E+041.8689E+05
STD1.3142E+037.7892E+04
TSABest6.3653E+031.2015E+04
Worst1.4767E+045.2552E+04
Mean1.0217E+042.7948E+04
STD2.1618E+039.2913E+03
FOBest0.0000E+009.9890E+02
Worst0.0000E+009.9899E+02
Mean0.0000E+009.9896E+02
STD0.0000E+002.2892E−02
Table 8.

Results for large scale optimization on F18and F19

FunctionF18F19
ALOBest1.9469E+019.8149E+03
Worst2.0307E+011.4948E+04
Mean1.9750E+011.1345E+04
STD2.4107E−011.4212E+03
BOABest1.2957E−082.0416E−06
Worst6.1917E−075.9821E−04
Mean5.9824E−085.5767E−05
STD9.7536E−081.2535E−04
GWOBest9.1437E+006.8521E+02
Worst1.2941E+011.4847E+03
Mean1.0128E+019.7278E+02
STD9.8562E−011.8400E+02
PSOBest1.7001E+018.1418E+02
Worst1.7812E+011.0013E+03
Mean1.7356E+019.2068E+02
STD1.6680E−014.4017E+01
SCABest7.0434E+002.0794E+03
Worst1.9671E+011.2949E+04
Mean1.6601E+018.3566E+03
STD3.2924E+002.5246E+03
DDAOBest2.4380E−021.3322E+00
Worst9.5690E+006.0214E+02
Mean3.3104E+008.4837E+01
STD2.1546E+001.5768E+02
BABest1.9362E+011.0246E+04
Worst2.1148E+012.8739E+04
Mean2.0312E+011.7242E+04
STD4.7706E−014.9752E+03
TSABest6.4128E+003.1366E+02
Worst1.2601E+013.3672E+03
Mean9.0431E+001.0870E+03
STD1.5954E+005.1949E+02
FOBest8.9617E−130.0000E+00
Worst2.4986E−077.2283E−07
Mean5.0366E−091.4257E−08
STD3.4971E−081.0120E−07

The FO algorithm is less efficient on high-degree multimodal benchmarks, and this behavior can be seen on the statistical results. The experimental results show that the FO algorithm is more effective on large scale optimization than small scale. The behavior on large and small scale problems needs a dedicated study that can be suggested for a future work. In brief, the FO algorithm can be stable and fast convergent on unimodal optimization problems as well as its efficiency on large scale problems.

3 Conclusion

The fertilization optimization algorithm is a powerful biologically inspired algorithm developed for mathematical optimization problems. It mimics the interaction between sperms and uterus in the process of fertilization the egg. The statistical results on 19 test functions; CEC2015 time expensive benchmarks, unimodal, multimodal, small scale, and large scale problems have shown the efficiency of the proposed algorithm compared with many optimization algorithms. During examinations of the FO algorithm, it has been noticed that the performance of the FO algorithm on large scale problems is better than its performance on small scale problems. The statistical results illustrate that FO algorithm is stable with less STD and best solutions than other eight competitive. The FO algorithm has proven its powerful on unimodal functions and it has promising applications on continuous differentiable objective functions and large scale optimization. The FO algorithm is fast and simple and can efficiently skip local points in the search space and go-ahead to the global point.

Acknowledgments

The research was supported by the Hungarian National Research Development and Innovation Office-NKFIH under the project number K 134358.

References

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  • [1]

    H. N. Ghafil and K. Jármai , “Research and application of industrial robot manipulators in vehicle and automotive engineering, a survey,” in Vehicle and Automotive Engineering 2, Lecture Notes in Mechanical Engineering, K. Jármai and B. Bolló , Eds, Springer, 2018, pp. 611623.

    • Search Google Scholar
    • Export Citation
  • [2]

    H. N. Ghafil and A. H. Mohammed , “A virtual reality environment for 5-DOF robot manipulator based on XNA framework,” Int. J. Copluter Appl., vol. 113, no. 3, pp. 3337, 2015.

    • Search Google Scholar
    • Export Citation
  • [3]

    H. N. Ghafil and K. Jármai , “Kinematic-based structural optimization of robots,” Pollack Period., vol. 14, no. 3, pp. 213222, 2019.

  • [4]

    E. Figueiredo , M. Macedo , H. V. Siqueira , C. J. Santana Jr , A. Gokhale , and C. J. A. Bastos-Filho , “Swarm intelligence for clustering-A systematic review with new perspectives on data mining,” Eng. Appl. Artif. Intell., vol. 82, pp. 313329, 2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [5]

    P. Šulek and T. Kinczer , “Expert control system of shipping operation on the Gabcikovo project,” Pollack Period., vol. 14, no. 1, pp. 139150, 2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [6]

    S. Alsamia , D. S. Ibrahim , and H. N. Ghafil , “Optimization of drilling performance using various metaheuristics,” Pollack Period., vol. 16, no. 2, pp. 8085, 2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [7]

    H. Ghafil and K. Jármai , “Comparative study of particles warm optimization and artificial bee colony algorithms,” in Multiscience XXXII. MicroCAD International Multidisciplinary Scientific Conference, Miskolc-Egyetemváros, Hungary, Sep. 5–6, 2018, pp. 16.

    • Search Google Scholar
    • Export Citation
  • [8]

    S. Mirjalili , S. M. Mirjalili , and A. Lewis , “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 4661, 2014.

  • [9]

    J. J. Liang , B. Y. Qu , P. N. Suganthan , and Q. Chen , “Problem definitions and evaluation criteria for the CEC2015 competition on learning-based real-parameters in gle objective optimization,” Tech. Rep. 201411A, Comput. Intell. Lab. Zhengzhou Univ. Zhengzhou China Nanyang Technol. Univ. Singapore, vol. 29, pp. 625640, 2014.

    • Search Google Scholar
    • Export Citation
  • [10]

    S. Arunachalam , T. A. Bhomila , and M. R. Babu , “Hybrid particles warm optimization algorithm and firefly algorithm based combined economic and emission dispatch including valve point effect,” in International Conference on Swarm, Evolutionary, and Memetic Computing, Swarm, volutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science, vol. 8947, B. Panigrahi , P. Suganthan , and S. Das , Eds, Springer, 2014, pp. 647660.

    • Search Google Scholar
    • Export Citation
  • [11]

    P. Kora and K. S. R. Krishna , “Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block,” Int. J. Cardiovasc. Acad., vol. 2, no. 1, pp. 4448, 2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [12]

    I. B. Aydilek , “A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems,” Appl. Soft Comput., vol. 66, pp. 232249, 2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]

    S. Mirjalili , “The ant lion optimizer,” Adv. Eng. Softw., vol. 83, pp. 8098, 2015.

  • [14]

    M. Pelikan , D. E. Goldberg , and E. Cantú-Paz , “BOA: The Bayesian optimization algorithm,” in Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, Orlando Florida, Jul. 13, 1999, pp. 525532.

    • Search Google Scholar
    • Export Citation
  • [15]

    S. Mirjalili , “SCA: a sine cosine algorithm for solving optimization problems,” Knowledge-Based Syst., vol. 96, pp. 120133, 2016.

  • [16]

    H. N. Ghafil and K. Jármai , “Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications,” Appl. Soft Comput., 2020, Paper no. 106392.

    • Crossref
    • Search Google Scholar
    • Export Citation
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    X. S. Yang , “A new metaheuristic bat-inspired algorithm,” in Nature Inspired Cooperative Strategies for Optimization Studies in Computational Intelligence, vol. 284, J. R. González , D. A. Pelta , C. Cruz , G. Terrazas , and N. Krasnogor , Eds, Berlin, Heidelberg: Springer, 2010, pp. 6574.

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    S. Kaur , L. K. Awasthi , A. L. Sangal , and G. Dhiman , “Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization,” Eng. Appl. Artif. Intell., vol. 90, 2020, Paper no. 103541.

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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

Editorial Board

  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

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Indexing and Abstracting Services:

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2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
12
Scimago
Journal Rank
0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score
1,5
Scopus
CIte Score Rank
Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP
0,613

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
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Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

Monthly Content Usage

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Mar 2022 0 7 5
Apr 2022 0 39 22
May 2022 0 32 29
Jun 2022 0 31 17
Jul 2022 0 4 5