nach oben

Soft Computing

Erschienen in:

01.04.2014 | Methodologies and Application

Improving a multi-objective differential evolution optimizer using fuzzy adaptation and \(K\)-medoids clustering

verfasst von: Miltiadis Kotinis

Erschienen in: Soft Computing | Ausgabe 4/2014

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

The research presented in this article focuses on the development of a multi-objective optimization algorithm based on the differential evolution (DE) concept combined with Mamdani-type fuzzy logic controllers (FLCs) and \(K\)-medoids clustering. The FLCs are used for adaptive control of the DE parameters; \(K\)-medoids clustering enables the algorithm to perform a more guided search by evolving neighboring vectors, i.e., vectors that belong to the same cluster. A modified version of the \(DE/best/1/bin\) algorithm is adopted as the core search component of the multi-objective optimizer. The FLCs utilize Pareto dominance and cluster-related information as input in order to adapt the algorithmic parameters dynamically. The proposed optimization algorithm is tested using a number of problems from the multi-objective optimization literature in order to investigate the effect of clustering and parameter adaptation on the algorithmic performance under various conditions, e.g., problems of high dimensionality, problems with non-convex Pareto fronts, and problems with discontinuous Pareto fronts. A detailed performance comparison between the proposed algorithm with state-of-the-art multi-objective optimizers is also presented.

Vorheriger Artikel Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

Nächster Artikel MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

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

http://www.iitk.ac.in/kangal/codes.shtml (last accessed on July 4, 2013).

Abbass HA (2002) The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the IEEE congress on evolutionary computation, pp 831–836

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

Coello Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, New York, pp 189–191MATH

Coello Coello CA, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE 2002 Congress on Evolutionary Computation, pp 1051–1056

Deb K (2001) Multi-objective optimization using evolutionary algorithms, Wiley, Chichester, UK, pp 28–33, 290–291 and 301–304

Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the parallel problem solving from nature VI, pp 849–858

Deb K, Tiwari S (2008) Omni-Optimizer, a generic evolutionary algorithm for single and multi-objective optimization. Eur J Oper Res 185(3):1062–1087CrossRefMATHMathSciNet

Durillo JJ, Nebro AJ (2011) jMetal: a Java framework for multi-objective optimization. Adv Eng Softw 42:760–771CrossRef

Durillo JJ, Nebro AJ, Luna F, Alba E (2008) Solving three-objective optimization problems using a new hybrid cellular genetic algorithm. In: Proceedings of the parallel problem solving from nature X, pp 661–670

Fonseca CMM (1995) Multiobjective genetic algorithms with application to control engineering problems. University of Sheffield, Ph.D. dissertation

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York, pp 501–528

Huang VL, Zhao SZ, Mallipeddi R, Suganthan PN (2009) Multi-objective optimization using self-adaptive differential evolution algorithm. In: Proceedings of the 2009 IEEE congress on evolutionary computation, pp 190–194

Iorio AW, Li X (2006) Incorporating directional information within a differential evolution algorithm for multi-objective optimization. In: Proceedings of the 2006 genetic and evolutionary computation conference, pp 691–697

Kaufman L, Rousseeuw R (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkCrossRef

Knowles JD, Corne DW (2000) Approximating the non-dominated front using the Pareto archived evolution strategy. Evol Comput J 8(2):149–172CrossRef

Kukkonen S, Lampinen J (2005) GDE3: the third evolution step of generalized differential evolution. In: Proceedings of the IEEE 2005 Congress on evolutionary computation, pp 443–450

Kursawe F (1995) A variant of evolution strategies for vector optimization. In: Proceedings of the parallel problem solving from nature I, pp 193–197

Li Z, Zhang Q (2009) Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans Evolut Comput 13(2):229–242CrossRef

Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462CrossRefMATH

Mamdani E (1974) Applications of fuzzy algorithms for control of a simple dynamic plant. Proc IEE 121(12):1585–1588

Mamdani E, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7(1): 1–13

Mann HB, Whitney DR (1947) On a test of whether one of 2 random variables is stochastically larger than the other. Ann Math Stat 18:50–60CrossRefMATHMathSciNet

Mezura-Montes E, Reyes-Sierra M, Coello Coello CA (2008) Multi-objective optimization using differential evolution: a survey of the state-of-the-art. In: Chakraborty UK (ed) Advances in differential evolution. Springer, Berlin, pp 173–196

Nebro AJ, Durillo JJ, Luna F, Dorronsoro B, Alba E (2007) Design issues in a multiobjective cellular genetic algorithm. In: Proceedings of the fourth international conference on evolutionary multi-criterion optimization. LNCS 4403, Springer, Berlin, pp 126–140

Osyczka A, Kundu S (1995) A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. Struct Optim 10(2):94–99CrossRef

Price KV, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin, pp 44–47, 74–91, and 112–117

Reyes M, Coello Coello CA (2005) Improving PSO-based multi-objective optimization using crowding, mutation and \(\varepsilon \)-dominance. In: Proceedings of the third international conference on evolutionary multi-criterion optimization. LNCS 3410, Springer, Berlin, pp 509–519

Schwefel HP (1987) Collective intelligence in evolving systems. In: Wolff W, Soeder CJ, Drepper F (eds) Ecodynamics: contributions to theoretical ecology. Springer, Berlin, pp 95–100

Storn R, Price KV (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, ICSI

Storn R, Price KV (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359CrossRefMATHMathSciNet

Streichert F, Ulmer H, Zell A (2005) Parallelization of multi-objective evolutionary algorithms using clustering algorithms. In: Proceedings of the third international conference on evolutionary multi-criterion optimization, LNCS 3410, Springer, Berlin, pp 92–107

Sugeno M (1985) An introductory survey on fuzzy control. Inform Sci 36:59–83CrossRefMATHMathSciNet

Tanaka M, Watanabe H, Furukawa Y, Tanino T (1995) GA-based decision support system for multi-criteria optimization. In: Proceedings of the international conference on systems, man and cybernetics, pp 1556–1561

Tiwari S, Fadel G, Deb K (2011) AMGA2: improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization. Eng Optim 43(4):377–401CrossRef

Van Veldhuizen DA (1999) Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Ph.D. dissertation, Air Force Institute of Technology

Viennet R (1996) Multicriteria optimization using a genetic algorithm for determining the Pareto set. Int J Syst Sci 27(2):255–260CrossRefMATH

Von Altrock C (1997) Fuzzy logic and neurofuzzy applications in business and finance. Prentice Hall, Upper Saddle River

Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1:80–83

Xue F, Sanderson AC, Bonissone PP, Graves RJ (2005) Fuzzy logic controlled multi-objective differential evolution. In: Proceedings of the IEEE international conference on fuzzy systems, pp 720–725

Zade AH, Mohammadi SMA, Gharaveisi AA (2011) Fuzzy logic controlled differential evolution to solve economic load dispatch problems. J Adv Comput Res 2(4):29–40

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

Zhang J, Chung HS-H, Lo W-L (2007) Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans Evol Comput 11(3):326–335CrossRef

Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput J 8(2):125–148CrossRef

Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271CrossRef

Titel: Improving a multi-objective differential evolution optimizer using fuzzy adaptation and -medoids clustering
verfasst von: Miltiadis Kotinis
Publikationsdatum: 01.04.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1086-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2014

Particle swarm optimization algorithm driven by multichaotic number generator

Using self-organizing maps with complex network topologies and coalitions for time series prediction

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

Preface

Application of predictive control methods for Radio telescope disk rotation control

Behaviour of pseudo-random and chaotic sources of stochasticity in nature-inspired optimization methods

Premium Partner