nach oben

Soft Computing

Erschienen in:

01.03.2014 | Methodologies and Application

A cooperative group optimization system

verfasst von: Xiao-Feng Xie, Jiming Liu, Zun-Jing Wang

Erschienen in: Soft Computing | Ausgabe 3/2014

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

A cooperative group optimization (CGO) system is presented to implement CGO cases by integrating the advantages of the cooperative group and low-level algorithm portfolio design. Following the nature-inspired paradigm of a cooperative group, the agents not only explore in a parallel way with their individual memory, but also cooperate with their peers through the group memory. Each agent holds a portfolio of (heterogeneous) embedded search heuristics (ESHs), in which each ESH can drive the group into a stand-alone CGO case, and hybrid CGO cases in an algorithmic space can be defined by low-level cooperative search among a portfolio of ESHs through customized memory sharing. The optimization process might also be facilitated by a passive group leader through encoding knowledge in the search landscape. Based on a concrete framework, CGO cases are defined by a script assembling over instances of algorithmic components in a toolbox. A multilayer design of the script, with the support of the inherent updatable graph in the memory protocol, enables a simple way to address the challenge of accumulating heterogeneous ESHs and defining customized portfolios without any additional code. The CGO system is implemented for solving the constrained optimization problem with some generic components and only a few domain-specific components. Guided by the insights from algorithm portfolio design, customized CGO cases based on basic search operators can achieve competitive performance over existing algorithms as compared on a set of commonly-used benchmark instances. This work might provide a basic step toward a user-oriented development framework, since the algorithmic space might be easily evolved by accumulating competent ESHs.

Vorheriger Artikel Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm

Nächster Artikel Financial time series prediction by a random data-time effective RBF neural network

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

Note that the same symbol no longer means a general type if it appears at other places. Taking “M _S” as an example (“M” is its general type), “S” means a key variant rather than the general type of a space of states.

Anderson JR (2005) Human symbol manipulation within an integrated cognitive architecture. Cognit Sci 29(3):313–341CrossRef

Barkat Ullah ASSM, Sarker R, Cornforth D, Lokan C (2009) AMA: a new approach for solving constrained real-valued optimization problems. Soft Comput 13(8-9):741–762CrossRef

Becerra RL, Coello CAC (2006) Cultured differential evolution for constrained optimization. Comput Methods Appl Mech Eng 195(33–36):4303–4322MATHCrossRef

Beyer HG (2001) On the performance of (1, λ)-evolution strategies for the ridge function class. IEEE Trans Evol Comput 5(3):218–235CrossRef

Birattari M, Stützle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. In: Genetic and evolutionary computation conference. Morgan Kaufmann, New York, pp 11–18

Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11:4135–4151CrossRef

Boyd R, Richerson PJ, Henrich J (2011) The cultural niche: Why social learning is essential for human adaptation. Proc Natl Acad Sci 108:10918–10925CrossRef

Cahon S, Melab N, Talbi EG (2004) ParadisEO: a framework for the reusable design of parallel and distributed metaheuristics. J Heurist 10:357–380CrossRef

Chen X, Ong YS, Lim MH, Tan KC (2012) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 15(5):591–607CrossRef

Curran D, O’Riordan C (2006) Increasing population diversity through cultural learning. Adapt Behav 14(4):315–338CrossRef

Danchin É, Giraldeau LA, Valone T, Wagner R (2004) Public information: from nosy neighbors to cultural evolution. Science 305(5683):487–491

Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338MATHCrossRef

Dennis A, Valacich J (1993) Computer brainstorms: more heads are better than one. J Appl Psychol 78(4):531–537CrossRef

Edgington T, Choi B, Henson K, Raghu T, Vinze A (2004) Adopting ontology to facilitate knowledge sharing. Communi ACM 47(11):85–90CrossRef

Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141CrossRef

Elsayed S, Sarker RA, Essam DL (2011) Multi-operator based evolutionary algorithms for solving constrained optimization problems. Comput Oper Res 38:1877–1896

Elsayed S, Sarker RA, Essam DL (2012) On an evolutionary approach for constrained optimization problem solving,. Appl Soft Comput 12(10):3208–3227CrossRef

Elsayed S, Sarker RA, Essam DL (2013) An improved self-adaptive differential evolution algorithm for optimization problems. IEEE Trans Ind Inf 9(1):89–99

Ericsson KA, Kintsch W (1995) Long-term working memory. Psychol Rev 102(2):211–245CrossRef

Farmani R, Wright J (2003) Self-adaptive fitness formulation for constrained optimization. IEEE Trans Evol Comput 7(5):445–455CrossRef

Galef BG (1995) Why behaviour patterns that animals learn socially are locally adaptive. Anim Behav 49(5):1325–1334CrossRef

Gigerenzer G, Selten R (2001) Bounded rationality: the adaptive toolbox. MIT Press, Cambridge

Glenberg AM (1997) What memory is for. Behav Brain Sci 20(1):1–55

Goncalo JA, Staw BM (2006) Individualism–collectivism and group creativity. Org Behav Human Decis Process 100:96–109CrossRef

Hamida SB, Schoenauer M (2002) ASCHEA: new results using adaptive segregational constraint handling. In: Congress on evolutionary computation. IEEE, Honolulu, pp 884–889

He S, Wu Q, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990CrossRef

Hinton GE, Nowlan SJ (1987) How learning can guide evolution. Complex Syst 1:495–502

Hoos HH, Stutzle T (2004) Stochastic local search: foundations and applications. Elsevier, Burlington

Huberman BA, Lukose RM, Hogg T (1997) An economics approach to hard computational problems. Science 275(5296):51–54CrossRef

Jin Y, Olhofer M, Sendhoff B (2002) A framework for evolutionary optimization with approximate fitness functions. IEEE Trans Evol Comput 6(5):481–494CrossRef

Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmann, San Mateo

Kohn NW, Smith SM (2011) Collaborative fixation: effects of others’ ideas on brainstorming. Appl Cognit Psychol 25(3):359–371CrossRef

Laland KN (2004) Social learning strategies. Learn Behav 32(1):4–14CrossRef

Lau HC, Wan WC, Halim S, Toh K (2007) A software framework for fast prototyping of meta-heuristics hybridization. Int Trans Oper Res 14(2):123–141MATHCrossRef

Leonard NE, Shen T, Nabet B, Scardovi L, Couzin ID, Levin SA (2012) Decision versus compromise for animal groups in motion. Proc Natl Acad Sci 109(1):227–232CrossRef

Liang JJ, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan PN, Coello CAC, Deb K (2006) Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization. Tech. rep., Nanyang Technological University, Singapore

Liu J, Han J, Tang YY (2002) Multi-agent oriented constraint satisfaction. Artif Intell 136(1):101–144MATHMathSciNetCrossRef

Liu J, Tsui KC (2006) Toward nature-inspired computing. Commun ACM 49(10):59–64CrossRef

Liu J, Zhong W, Hao L (2007) An organizational evolutionary algorithm for numerical optimization. IEEE Trans Syst Man Cybern Part B 37(4):1052–1064CrossRef

Lu H, Chen W (2008) Self-adaptive velocity particle swarm optimization for solving constrained optimization problems. J Global Optim 41(3):427–445MATHMathSciNetCrossRef

Mallipeddi R, Mallipeddi S, Suganthan PN (2010a) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696CrossRef

Mallipeddi R, Mallipeddi S, Suganthan PN (2010b) Ensemble strategies with adaptive evolutionary programming. Inf Sci 180(2):1571–1581CrossRef

Mallipeddi R, Suganthan PN (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14(4):561–579CrossRef

Mezura-Montes E, Coello CAC (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9(1):1–17CrossRef

Milano M, Poli A (2004) MAGMA: a multiagent architecture for metaheuristics. IEEE Trans Syst Man Cybern Part B 34(2):925–941CrossRef

Nemeth CJ (1986) Differential contributions of majority and minority influence. Psychol Rev 93(1):23–32CrossRef

Omran MGH, Engelbrecht AP (2009) Free search differential evolution. In: IEEE congress on evolutionary computation. IEEE, Trondheim, pp 110–117

Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B: Cybern 36(1):141–152CrossRef

Parejo JA, Ruiz-Cortes A, Lozano S, Fernandez P (2012) Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput 16(3):527–561CrossRef

Paulus PB (2000) Groups, teams, and creativity: the creative potential of idea-generating groups. Appl Psychol 49(2):237–262MathSciNetCrossRef

Platon E, Mamei M, Sabouret N, Honiden S, Van Parunak H (2007) Mechanisms for environments in multi-agent systems: survey and opportunities. Auton Agents Multi Agent SystAgents and Multi-Agent Systems 14(1):31–47CrossRef

Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, NY

Raidl GR (2006) A unified view on hybrid metaheuristics. In: International conference on hybrid metaheuristics. Gran Canaria, pp 1–12

Reynolds RG, Peng B, Ali MZ (2008) The role of culture in the emergence of decision-making roles: an example using cultural algorithms. Complexity 13(3):27–42CrossRef

Runarsson TP, Yao X (2005) Search biases in constrained evolutionary optimization. IEEE Trans Syst Man Cybern Part C 35(2):233–243CrossRef

Salomon R (1996) Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSystems 39(3):263–278CrossRef

Satzinger JW, Garfield MJ, Nagasundaram M (1999) The creative process: the effects of group memory on individual idea generation. J Manage Inf Syst 15(4):143–160

Smith-Miles K (2008) Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput Surv 41(6), Art. No. 6

Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173MATHMathSciNetCrossRef

Stadler PF, Happel R (1999) Random field models for fitness landscapes. J Math Biol 38(5):435–478MATHMathSciNetCrossRef

Streeter M, Smith SF (2008) New techniques for algorithm portfolio design. In: Conference in Uncertainty in Artificial Intelligence, pp. 519–527. AUAI, Helsinki, Finland

Taillard ED, Gambardella LM, Gendreau M, Potvin JY (2001) Adaptive memory programming: a unified view of metaheuristics. Eur J Oper Res 135(1):1–16MATHMathSciNetCrossRef

Takahama T, Sakai S (2005) Constrained optimization by applying the alpha constrained method to the nonlinear simplex method with mutations. IEEE Trans Evol Comput 9(5):437–451CrossRef

Talbi EG (2002) A taxonomy of hybrid metaheuristics. J Heurist 8(5):541–564CrossRef

Tomasello M, Kruger A, Ratner H (1993) Cultural learning. Behav Brain Sci 16(3):495–511CrossRef

Ventura S, Romero C, Zafra A, Delgado JA, Hervas C (2008) JCLEC: a Java framework for evolutionary computation. Soft Comput 12(4):381–392CrossRef

Vrugt JA, Robinson BA, Hyman JM (2009) Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput 13(2):243–259CrossRef

Wagner S (2009) Heuristic optimization software systems—modeling of heuristic optimization algorithms in the heuristiclab software environment. Phd thesis, Johannes Kepler University, Linz

Wang Y, Cai Z, Zhou Y, Zeng W (2008) An adaptive tradeoff model for constrained evolutionary optimization. IEEE Trans Evol Comput 12(1):80–92CrossRef

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef

Woolley AW, Chabris CF, Pentland A, Hashmi N, Malone TW (2010) Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004):686–688

Xie XF, Liu J (2005) A compact multiagent system based on autonomy oriented computing. In: IEEE/WIC/ACM international conference on intelligent agent technology. IEEE, Compiegne, pp 38–44

Xie XF, Liu J (2009) Multiagent optimization system for solving the traveling salesman problem (TSP). IEEE Trans Syst Man Cybern Part B Cybern 39(2):489–502CrossRef

Xie XF, Zhang WJ (2004) SWAF: swarm algorithm framework for numerical optimization. In: Genetic and evolutionary computation conference (GECCO). Springer, Seattle, pp 238–250

Xie XF, Zhang WJ, Yang ZL (2002) Social cognitive optimization for nonlinear programming problems. In: International conference on machine learning and cybernetics. IEEE, Beijing, pp 779–783

Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRef

Zhang WJ, Xie XF (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: IEEE international conference on systems, man, and cybernetics. IEEE, Washington, DC, pp 3816–3821

Titel: A cooperative group optimization system
verfasst von: Xiao-Feng Xie
Jiming Liu
Zun-Jing Wang
Publikationsdatum: 01.03.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1069-8

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 3/2014

Analysis of stability and robust stability of polynomial fuzzy model-based control systems using a sum-of-squares approach

Financial time series prediction by a random data-time effective RBF neural network

Chain conditions on $$\textit{BL}$$ BL -algebras

Characterization of extended filters in residuated lattices

Performance analysis of modeling framework for prediction in wind farms employing artificial neural networks

A parallel and scalable CAST-based clustering algorithm on GPU