Schwarmintelligenz Teil 3 – Metaheuristiken auf der Grundlage des Verhaltens von Ameise & Co.

2. aktualisierte Fassung von Mai 2012

Tiere in Schwärmen haben für ihr Zusammenleben Verhaltensweisen entwickelt, die Wissenschaftler als Inspiration für verschiedene Lösungsansätze nutzen.

Teil 3: Im Operations Research erfordern komplexe Optimierungsprobleme, wie zum Beispiel Routenplanung, Fertigungssteuerung und Personaleinsatzplanung, kreative Lösungsansätze. Erkenntnisse aus dem Verhalten von Ameisen, Bienen und anderen Tierschwärmen haben Informatiker zu speziellen Algorithmen für die mathematische Optimierung inspiriert.

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Konzepte

Wesentliches Schlagwort: Ameisenalgorithmus

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Wesentliches Schlagwort: Particle Swarm Optimization

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Wesentliches Schlagwort: Scheduling-Verfahren

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Literaturliste

Cost optimisation of supply chain networks using Ant Colony Optimisation / P. S. S. Prasad; C. Shankhar.

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A hybrid particel swarm and ant colony optimizer for multi-attribute partnership selection in virtual enterprises / S. H. Niu, S. K. Ong and A. Y. C. Nee.

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An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times / Timur Keskinturk; Mehmet B. Yildirim; Mehmet Barut.

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Process planning through ant colony optimization / Puneet Bhardwaj and M. K. Tiwari.

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A statistical analysis of parameter values for the rank-based ant colony optimization algorithm for the traveling salesperson problem / Francis J. Vasko, J. D. Bobeck, M. A. Governale, D. J. Rieksts and J. D. Keffer.

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An ant colony optimization model: The period vehicle routing problem with time windows / Bin Yu; Zhong Zhen Yang.

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Flow shop scheduling to minimize the total completion time with a permanently present operator : models and ant colony optimization metaheuristic / Xiangyong Li; M. F. Baki; Y. P. Aneja.

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Swarm intelligence : application of the ant colony optimization algorithm to logistics-oriented vehicle routing problems / by John E. Bell and Stanley E. Griffis.

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Ant colony algorithm for multiple-depot vehicle routing problem with shortest finish time / Jianhua Ma and Jie Yuan.

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An ant colony optimization metaheuristic for single-path multicommodity network flow problems / X.-Y. Li, Y. P. Aneja and F. Baki.

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Credit rating prediction using Ant Colony optimization / D. Martens; T. van Gestel; M. De Backer; R. Haesen; J. Vanthienen ….

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Ant colony optimisation with parameterised search space for the job shop scheduling problem / Minseok Seo and Daecheol Kim.

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A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior / Wei Bin; Peng Qinke; Zhao Jing; Chen Xiao.

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Integrating AGVs and production planning with memetic particle swarm optimization / Sri Krishna Kumar, M. K. Tiwari and J. Harding.

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A pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search / Ghasem Moslehi; Mehdi Mahnam.

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Particle swarm optimization for scheduling problems / Jens Czogalla.

Cyber swarm algorithms : improving particle swarm optimization using adaptive memory strategies / Peng-Yeng Yin; Fred Glover; Manuel Laguna; Jia-Xian Zhu.

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Intelligent business process execution using Particle Swarm Optimization / Markus Kress; Sanaz Mostaghim; Detlef Seese.

  • Intelligent business process execution using Particle Swarm Optimization / Markus Kress; Sanaz Mostaghim; Detlef Seese.
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Intelligent business process optimization for the service industry / by Markus Kress.

A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design / C. K. Goh; K. C. Tan; D. S. Liu; S. C. Chiam.

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A modified artificial bee colony algorithm / Wei-feng Gao; San-yang Liu.

  • A modified artificial bee colony algorithm / Wei-feng Gao; San-yang Liu.
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An artificial bee colony algorithm for the capacitated vehicle routing problem / W. Y. Szeto; Yongzhong Wu; Sin C. Ho.

  • An artificial bee colony algorithm for the capacitated vehicle routing problem / W. Y. Szeto; Yongzhong Wu; Sin C. Ho.
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Solving reliability redundancy allocation problems using an artificial bee colony algorithm / Wei-Chang Yeh; Tsung-Jung Hsieh.

  • Solving reliability redundancy allocation problems using an artificial bee colony algorithm / Wei-Chang Yeh; Tsung-Jung Hsieh.
    (2011) In: Computers & operations research. – Bd. 38.2011, 11, (Nov.2011) S. 1465-1473.

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  • Solving reliability redundancy allocation problems using an artificial bee colony algorithm / Wei-chang Yeh; Tsung-jung Hsieh.
    (2011) In: Computers & operations research. – Bd. 38.2011, 11, (Nov.2011) S. 1465-1473.

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Bee colony optimization for the p-center problem / Tatjana Davidovi´c; Dusan Ramljak; Milica Selmi´c; Dusan Teodorovi´c.

  • Bee colony optimization for the p-center problem / Tatjana Davidovi´c; Dusan Ramljak; Milica Selmi´c; Dusan Teodorovi´c.
    (2011) In: Computers & operations research. – Oxford [u.a.]. – Bd. 38.2011, 10, (Okt.2011) S. 1367-1376.

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Der Bee Colony-Inspired Algorithm (BCIA) zur Lösung des Standardproblems der Tourenplanung mit Zeitfensterrestriktionen / Sascha Häckel; Patrick Dippold; Sascha Lemke.

  • Der Bee Colony-Inspired Algorithm (BCIA) zur Lösung des Standardproblems der Tourenplanung mit Zeitfensterrestriktionen / Sascha Häckel; Patrick Dippold; Sascha Lemke.
    (2009) In: Logistics and supply chain management. – Göttingen. – 2009, S. 272-278.

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