nach oben

Soft Computing

Erschienen in:

11.07.2015 | Focus

Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

verfasst von: Wei Jer Lim, Asral Bahari Jambek, Siew Chin Neoh

Erschienen in: Soft Computing | Ausgabe 12/2015

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The effectiveness of HMGA in Pareto optimal convergence was investigated with two performance indicators (i.e. generational distance and spacing). To measure HMGA’s performance, a comparison study was conducted between HMGA and MGA. In this work, HMGA is outperformed MGA in the search for Pareto optimal front and capable of solving different difficulty of MOPs.

Vorheriger Artikel DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm

Nächster Artikel A hybrid version of invasive weed optimization with quadratic approximation

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

Baesler F, Palma C (2014) Multiobjective parallel machine scheduling in the sawmill industry using memetic algorithms. Int J Adv Manuf Technol 74(5–8):757–768CrossRef

Banzhaf W et al (1998) Genetic programming: an introduction, vol 1. Morgan Kaufmann, San FranciscoMATHCrossRef

Bartz-Beielstein T, Limbourg P, Mehnen J, Schmitt K, Parsopoulos, KE, Vrahatis MN (2003) Particle swarm optimizers for Pareto optimization with enhanced archiving techniques. In: Evolutionary computation, 2003. CEC’03. The 2003 Congress on, IEEE

Bastos-Filho CJA, Chaves DAR, e Silva FSF, Pereira HA, Martins-Filho JF (2011) Wavelength assignment for physical-layer-impaired optical networks using evolutionary computation. Opt Commun Netw IEEE/OSA J 3(3):178–188CrossRef

Beyer H-G (2001) The theory of evolution strategies. Springer, HeidelbergCrossRef

Carcangiu S, Fanni A, Montisci A (2008) Multiobjective tabu search algorithms for optimal design of electromagnetic devices. Magn IEEE Trans 44(6):970–973CrossRef

Coello CC, Lamont GB (2005) An introduction to multi-objective evolutionary algorithms and their applications. Appl Multi-Object Evol Algorithms 1:1–28CrossRef

Coello CAC, Pulido G (2005) Multiobjective structural optimization using a microgenetic algorithm. Struct Multidiscip Optim 30:388–403CrossRef

Dasheng L, Tan KC, Goh CK, Ho WK (2007) A multiobjective memetic algorithm based on particle swarm optimization. Syst Man Cybern Part B: Cybern IEEE Trans 37(1):42–50CrossRef

Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. Evol Comput IEEE Trans 6(2):182–197CrossRef

Deb K (2008) Introduction to evolutionary multiobjective optimization. In: Branke J (ed) Multiobjective optimization. Springer, Berlin, Heidelberg, pp 59–96CrossRef

Deb K (2010) Recent developments in evolutionary multi-objective optimization. In: Ehrgott M, Figueira JR, Greco S (eds) Trends in multiple criteria decision analysis. Springer, US, pp 339–368CrossRef

Durillo JJ, Nebro AJ (2011) jMetal: a Java framework for multi-objective optimization. Adv Eng Softw 42(10):760–771CrossRef

Eiben AE, Bäck T (1997) Empirical investigation of multiparent recombination operators in evolution strategies. Evol Comput 5(3):347–365CrossRef

Emmerich Michael, Deutz André (2006) Multicriteria optimization and decision making. LIACS, Leiden university, NL

Eskandari H, Geiger C (2008) A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems. J Heuristics 14(3):203–241MATHCrossRef

Fogel DB (1992) Using evolutionary programming for modeling: an ocean acoustic example. Ocean Eng IEEE J 17(4):333–340CrossRef

Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulationDiscussion and generalization. In: ICGA

Ho SL, Yang S, Ni G, Wong HC (2002) A tabu method to find the Pareto solutions of multiobjective optimal design problems in electromagnetics. Magn IEEE Trans 38(2):1013–1016CrossRef

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

Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: IEEE World Congress on Computational Intelligence. Proceedings of the 1st IEEE Conference on Evolutionary Computation, 1994

Hwang C-L, Masud ASM (1979) Multiple objective decision making—methods and applications: a state-of-the-art survey. In: Albach H, Balakrishnan AV, Beckmann M, Dhrymes P, Green J, Hildenbrand W, Krelle W, Kunzi HP, Ritter K, Sato R, Schelbert H, Schonfeld P (eds) Lecture notes in economics and mathematical systems. Springer, Berlin, Heidelberg

Iba H, Aranha C (2012) Introduction to genetic algorithms. In: Lim M-H, Ong Y-S (eds) Practical applications of evolutionary computation to financial engineering. Springer, Berlin, Heidelberg, pp 1–17

Knowles J (2006) ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. Evol Comput IEEE Trans 10(1):50–66CrossRef

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

Kursawe F (1991) A variant of evolution strategies for vector optimization. In: Schwefel H-P, Männer R (eds) Parallel problem solving from nature. Springer, Berlin, Heidelberg, pp 193–197CrossRef

Leung M-F, Ng S-C, Cheung C-C, Lui AK (2014) A new strategy for finding good local guides in MOPSO. In: Evolutionary computation (CEC), 2014 IEEE Congress on, IEEE

Lipinski P (2012) Practical applications of evolutionary computation to financial engineering: robust techniques for forecasting, trading, and hedging (Iba, H. and Aranha, C.C.; 2012)[book review]. Comput Intell Mag IEEE 7(2):75–76CrossRef

Lughofer E (2012) A dynamic split-and-merge approach for evolving cluster models. Evol Syst 3(3):135–151CrossRef

Maheta HH, Dabhi VK (2014) An improved SPEA2 multi objective algorithm with non dominated elitism and generational crossover. In: 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

Nebro AJ, Luna F, Alba E, Dorronsoro B, Durillo JJ, Beham A (2008) AbYSS: adapting scatter search to multiobjective optimization. Evol Comput IEEE Trans 12(4):439–457CrossRef

O’Mahony C, Wilson N (2011) Sorted-pareto dominance: an extension to the Pareto Dominance relation and its application in Soft Constraints. 11th Workshop on Preferences and Soft Constraints

Precup R-E, David R-C, Petriu EM, Preitl S, Paul AS (2011) Gravitational search algorithm-based tuning of fuzzy control systems with a reduced parametric sensitivity. In: Gaspar-Cunha A (ed) Soft computing in industrial applications. Springer, Berlin, Heidelberg, pp 141–150

Pulido GT, Coello CAC (2004) Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. In: Deb K (ed) Genetic and evolutionary computation-GECCO 2004. Springer

Raquel CR, Naval PC Jr (2005) An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM

Schott JR (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization. DTIC Document

Smith KI, Everson RM, Fieldsend JE, Murphy C, Misra R (2008) Dominance-based multiobjective simulated annealing. Evol Comput IEEE Trans 12(3):323–342CrossRef

Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248CrossRef

Van Veldhuizen DA, Lamont GB (2000) On measuring multiobjective evolutionary algorithm performance. In: Evolutionary computation, 2000. Proceedings of the 2000 Congress on, IEEE

Van Veldhuizen DA, Lamont GB (1998) Multiobjective evolutionary algorithm research: a history and analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio

Wang J, Liu W, Zhang W, Yang B (2013) Multi-objective particle swarm optimization based on self-update and grid strategy. In: Proceedings of the 2012 International Conference on Information Technology and Software Engineering, Springer

Yang F-C, Ni B (2014) Water flow-like optimization algorithm for multi-objective continuous optimization problem. In: Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013), Springer

Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195CrossRef

Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm. Eidgenössische Technische Hochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze (TIK)

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

Titel: Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)
verfasst von: Wei Jer Lim
Asral Bahari Jambek
Siew Chin Neoh
Publikationsdatum: 11.07.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1767-5

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 12/2015

Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

MODE: multiobjective differential evolution for feature selection and classifier ensemble

Adaptive least square control in discrete time of robotic arms

To combat multi-class imbalanced problems by means of over-sampling and boosting techniques

DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm

A hybrid cascade neural network with an optimized pool in each cascade