Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Soft Computing
Erschienen in: Soft Computing 19/2017

07.01.2017 | Focus

Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness

verfasst von: Carlos García-Martínez, Pablo D. Gutiérrez, Daniel Molina, Manuel Lozano, Francisco Herrera

Erschienen in: Soft Computing | Ausgabe 19/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Real-parameter optimisation is a prolific research line with hundreds of publications per year. There exists an impressive number of alternatives in both algorithm families and enhancements over their respective original proposals. In this work, we analyse if this growth in the number of publications is correlated with a real progress in the field. We have selected five approaches from one of the most significant journals in the field and compared them with the winner of the competition celebrated within the IEEE Congress on Evolutionary Computation 2005. We observe that not only these methods are unable to get the good results of the winner of the competition, published several years before, but that they often avoid this type of comparison. Instead, they usually compare with other approaches from the same family. We conclude that the comparison with the state-of-the-art of the field should be mandatory to promote a real progress and to prevent that the area becomes obfuscated for outsiders.
Vorheriger Artikel Optimizing a parameterized message-passing metaheuristic scheme on a heterogeneous cluster
Nächster Artikel Swarmic approach for symmetry detection of cellular automata behaviour

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
Auger A, Hansen N (2005a) A restart CMA evolution strategy with increasing population size. In: IEEE Congress on Evolutionary Computation (CEC’05), vol 2, pp 1769–1776
Auger A, Hansen N (2005b) Performance evaluation of an advanced local search evolutionary algorithm. In: IEEE Congress on Evolutionary Computation (CEC’05), pp 1777–1784
Auger A, Hansen N, Schoenauer M (2012) Benchmarking of continuous black box optimization algorithms. Evol Comput 20(4):481–481CrossRef
Awad NH, Ali MZ, Suganthan, PN, Reynolds RG (2016) An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC 2014 problems. In: IEEE Congress on Evolutionary Computation (CEC’16), pp 2958–2965
Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23CrossRef
Bersini H, Dorigo M, Langerman S, Seront G, Gambardella L (1996) Results of the first international contest on evolutionary optimisation (1st ICEO), In: IEEE Congress on Evolutionary Computation (CEC’96), pp 611–615
Box G (1957) Evolutionary operation: a method for increasing industrial productivity. Appl Stat 6:639–641CrossRef
Bremermann H (1962) Optimization through evolutiona dn recombination. Spartan Books, Washington, pp 93–106
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution. A comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657CrossRef
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRef
Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287MathSciNetCrossRefMATH
Das S, Abraham A, Chakraborty U, Konar A (2009) Differential evolution using a neighbourhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553CrossRef
de Oca MM, Stützle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evol Comput 13(5):1120–1132CrossRef
Deb K, Anand A, Joshi D (2001) A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput 9(2):159–195CrossRef
Demsar J (2006) Statistical comparisons of classifers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, CambridgeMATH
Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29–41CrossRef
Eberhart R, Shi Y (2001) Tracking and optimizing dynamic systems with particle swarms. In: IEEE Congress on Evolutionary Computation (CEC’01), pp 94–100
Esbensen H, Mazumder P (1994) SAGA: a unification of the genetic algorithm with simulated annealing and its application to macro-cell placement. In: IEEE Int. Conf. VLSI Des., pp 211–214
Eshelman L, Schaffer J (1993) Real-coded genetic algorithms and interval schemata. In: Foundation of Genetic Algorithm-2. Morgan Kaufmann
Fogel L (1962) Autonomous automata. Ind Res 4:14–19
Fogel DB (2000) Evolutionary computation. Toward a new philosophy of machine intelligence. IEEE Press, PiscatawayMATH
Fogel L, Owens A, Walsh M (1966) Artificial intelligence through simulated evolution. Wiley, New YorkMATH
Garcia S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644CrossRefMATH
García-Martínez C, Rodriguez FJ, Lozano M (2012) Arbitrary function optimisation with metaheuristics. No free lunch and real-world problems. Soft Comput 16(12):2115–2133CrossRef
Garden RW, Engelbrecht AP (2014) Analysis and classification of optimisation benchmark functions and benchmark suites. In: IEEE Congress on Evolutionary Computation (CEC’2014), pp 1664–1669
Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68CrossRef
Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8:156–166CrossRef
Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingMATH
Guo SM, Yang CC (2015) Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Trans Evol Comput 19(1):31–49MathSciNetCrossRef
Guo SM, Tsai JSH, Yang CC, Hsu PH (2015) A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: IEEE Congress on Evolutionary Computation (CEC’2015), pp 1003–1010
Hansen N (2005) Compilation of results on the CEC benchmark function set. Tech. rep., Institute of Computational Science, ETH Zurich, Switzerland
Hansen N (2009) Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed. In: Genetic and Evolutionary Computation Conference (GECCO’09), pp 2389–2396
Hansen N, Auger A, Mersmann O, Tuv̀ar T, Brockhoff D (2016) COCO: a platform for comparing continuous optimizers in a black-box setting. In: ArXiv e-prints, arXiv:​1603.​08785
Herrera F, Lozano M, Sánchez A (2003) A taxonomy for the crossover operator for real-coded genetic algorithms. An experimental study. Int J Intell Syst 18(3):309–338CrossRefMATH
Ho SY, Lin HS, Liauh WH, Ho SJ (2008) OPSO: orthogonal particle swarm optimization and its application to task assignment problems. IEEE Trans Syst Man CybernPart A 38(2):288–298
Holland J (1962) Outline for a logical theory of adaptive systems. J Assoc Comput Mach 3:297–314CrossRefMATH
Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194MATH
Jamil M, Yang X-S, Zepernick H-JD (2013) Test functions for global optimization: a comprehensive survey. In: Swarm Intelligence and Bio-Inspired Computation, pp 193–222
Janson S, Middendorf M (2005) A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans Syst Man Cybern Part B Cybern 35(6):1272–1282CrossRef
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471MathSciNetCrossRefMATH
Keenedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: IEEE Congress on Evolutionary Computation (CEC’99), vol 3, pp 1931–1938
Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Conf Neural Netw 4:1942–1947
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: IEEE Congress on Evolutionary Computation (CEC’02), pp 1671–1676
KrishnaKumar K, Narayanaswamy S, Garg S (1995) Solving large parameter optimization problems using a genetic algorithm with stochastic coding. In: Genetic Algorithms in Engineering and Computer Science, pp 287–303. Wiley
Lee C, Yao X (2004) Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans Evol Comput 8(1):1–13CrossRef
Leung YW, Wang Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput 5(1):41–53CrossRef
Li Z (2015) Genetic algorithm that considers scattering for THz quantitative analysis. IEEE Trans Terahertz Sci Technol 5(6):1062–1067CrossRef
Liang J, Suganthan P (2005) Dynamic multi-swarm particle swarm optimizer. In: Swarm Intell. Symposium, pp 124–129
Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: IEEE Swarm Intelligence Symposium, pp 68–75
Liang J, Qin A, Suganthan P, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRef
Liao T, Molina D, de Oca M, Stützle T (2014) A note on bound constraints handling for the IEEE CEC’05 benchmark function suite. Evol Comput 22(2):351–359CrossRef
Liao T, Molina D, Sttzle T (2015) Performance evaluation of automatically tuned continuous optimizers on different benchmark sets. Soft Comput J 27:490–503CrossRef
Liu J, Lampinen (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462CrossRefMATH
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210CrossRef
Omidvar MN, Li X, Tang K (2015) Designing benchmark problems for large-scale continuous optimization. Inf Sci 2015:419–436CrossRef
Omran M, Salman A, Engelbrecht A (2005) Self-adaptive differential evolution. In: Computational Intelligence and Security (LNCS 3801), pp 192–199. Springer
Ong YS, Keane A (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110CrossRef
Parsopoulos K, Vrahatis M (2004) UPSO A unified particle swarm optimization scheme. In: Lecture Series on Computational Sciences, pp 868–873
Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67CrossRef
Peram T, Veeramachaneni K, Mohan C (2003) Fitness-distance-ration based particle swarm optimization. In: Swarm Intelligence Symposium, pp 174–181
Piotrowski AP (2015) Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions. Inf Sci 297:191–201CrossRef
Pošic P, Kubalík J (2012) Experimental comparison of six population-based algorithms for continuous black box optimization. Evol Comput 20(4):483–508CrossRef
Pošic P, Huyer W, Pál L (2012) A comparison of global search algorithms for continuous black box optimization. Evol Comput 20(4):509–541CrossRef
Qin A, Huang V, Suganthan P (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417CrossRef
Qu BY, Liang JJ, Wang ZY, Chen Q, Suganthan PN (2016) Novel benchmark functions for continuous multimodal optimization with comparative results. Swarm Evol Comput 26:23–34CrossRef
Ratnaweera A, Halgamuge S, Watson H (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255CrossRef
Rechenberg I (1965) Cybernetic solution path of an experimental problem. Royal Aircraft Establishment Translation, 1122
Rönkkönen J, Li X, Kyrki V, Lampinen J (2011) A framework for generating tunable test functions for multimodal optimization. Soft Comput 15(9):1689–1706CrossRef
Schwefel HP (1968) Experimemelle Optimierung einer Zweiphasend. Tech. Rep. 35, Project MHD_Staustrahirohr. 11.034/68
Schwefel HP (1975) Evolutionsstrategie und numerische Optimierung. Ph.D. thesis, Technische Universität Berlin
Schwefel H-P (1981) Numerical optimization of computer models. Wiley, ChichesterMATH
Shi Y, Eberhart R (1998a) A modified particle swarm optimizer. In: IEEE Congress on Evolutionary Computation (CEC’98), pp 69–73
Shi Y, Eberhart R (1998b) Parameter selection in particle swarm optimization. In: International Conference on Evolutionary Programming (LNCS 1447), pp 591–600
Shi Y, Eberhart R (1999) Empirical study of particle swarm optimization. In: IEEE Congress on Evolutionary Computation (CEC’99), pp 1945–1950
Srinivas M, Patnaik L (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667CrossRef
Storn R, Price K (1997) Differential Evolution. A simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetCrossRefMATH
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization. Tech. report, Nanyang Technological University
Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: IEEE Congress on Evolutionary Computation (CEC’13), pp 71–78
Tanabe R, Fukunaga A (2014) Improving the search performance of SHADE using linear population size reduction. In: IEEE Congress on Evolutionary Computation (CEC’14), pp 1658–1665
Trelea I (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325MathSciNetCrossRefMATH
van den Bergh F, Engelbrecht A (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRef
Weyland D (2010) A rigorous analysis of the harmony search algorithm: How the research community can be misled by a novel methodology. Int J Appl Metaheuristic Comput 1(2):50–60CrossRef
Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef
Xiong N, Molina D, Ortiz ML, Herrera F (2015) A walk into metaheuristics for engineering optimization: principles, methods and recent trends. Int J Comput Intell Syst 8(4):606–636CrossRef
Yang Z, He J, Yao X (2007) Making a difference to differential evolution. In: Advances Metaheuristics for Hard Optimization, pp 397–414. Springer
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102CrossRef
Zaharie D (2003) Control of population diversity and adaptation in differential evolution algorithms. In: Mendel 9th Int. Conf. Soft Computing, pp 41–46
Zhan ZH, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847CrossRef
Zhang J, Sanderson A (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRef
Zhang Q, Sun J, Tsang E, Ford J (2004) Hybrid estimation of distribution algorithm for global optimization. Eng Comput 21(1):91–107CrossRefMATH
Zheng YL, Ma LH, Zhang LY, Qian JX (2003a) Empirical study of particle swarm optimizer with an increasing inertia weight. In: IEEE Congress on Evolutionary Computation (CEC’03), pp 221–226
Zheng YL, Ma LH, Zhang LY, Qian JX (2003b) On the convergence analysis and parameter selection in particle swarm optimization. In: IEEE International Conference on Machine Learning and Cybernetics, pp 1802–1807
Metadaten
Titel
Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness
verfasst von
Carlos García-Martínez
Pablo D. Gutiérrez
Daniel Molina
Manuel Lozano
Francisco Herrera
Publikationsdatum
07.01.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 19/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2471-9

Weitere Artikel der Ausgabe 19/2017

Soft Computing 19/2017 Zur Ausgabe

Focus

Swarmic approach for symmetry detection of cellular automata behaviour

Focus

Extrapolated quantum states, void states and a huge novel class of distillable entangled states

Methodologies and Application

An improved ant colony algorithm for robot path planning

Methodologies and Application

Chaos-assisted multiobjective evolutionary algorithm to the design of transformer

Foundations

Convex congruences

Methodologies and Application

Interval-valued intuitionistic fuzzy programming technique for multicriteria group decision making based on Shapley values and incomplete preference information