Hybrid nature-inspired computation methods for optimization

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorWang, Xiaolei
dc.contributor.departmentSähkötekniikan laitosfi
dc.date.accessioned2012-08-23T05:21:58Z
dc.date.available2012-08-23T05:21:58Z
dc.date.issued2009
dc.description.abstractThe focus of this work is on the exploration of the hybrid Nature-Inspired Computation (NIC) methods with application in optimization. In the dissertation, we first study various types of the NIC algorithms including the Clonal Selection Algorithm (CSA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), Harmony Search (HS), Differential Evolution (DE), and Mind Evolution Computing (MEC), and propose several new fusions of the NIC techniques, such as CSA-DE, HS-DE, and CSA-SA. Their working principles, structures, and algorithms are analyzed and discussed in details. We next investigate the performances of our hybrid NIC methods in handling nonlinear, multi-modal, and dynamical optimization problems, e.g., nonlinear function optimization, optimal LC passive power filter design, and optimization of neural networks and fuzzy classification systems. The hybridization of these NIC methods can overcome the shortcomings of standalone algorithms while still retaining all the advantages. It has been demonstrated using computer simulations that the proposed hybrid NIC approaches are capable of yielding superior optimization performances over the individual NIC methods as well as conventional methodologies with regard to the search efficiency, convergence speed, and quantity and quality of the optimal solutions achieved.en
dc.format.extentVerkkokirja (1030 KB, 66 s.)
dc.format.mimetypeapplication/pdf
dc.identifier.isbn978-951-22-9859-4
dc.identifier.isbn978-951-22-9858-7 (printed)#8195;
dc.identifier.issn1795-4584
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/4626
dc.identifier.urnURN:ISBN:978-951-22-9859-4
dc.language.isoenen
dc.publisherTeknillinen korkeakouluen
dc.relation.haspart[Publication 1]: X. Wang. 2005. Clonal Selection Algorithm in power filter optimization. In: Jarno Martikainen (editor). Proceedings of the 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications (SMCia 2005). Espoo, Finland. 28-30 June 2005, pages 122-127. © 2005 IEEE. By permission.en
dc.relation.haspart[Publication 2]: X. Wang, X. Z. Gao, and S. J. Ovaska. 2005. A hybrid optimization algorithm in power filter design. In: Leopoldo G. Franquelo, Alexander Malinowski, Mo-Yuen Chow, and Herbert L. Hess (editors). Proceedings of the 31st Annual Conference of the IEEE Industrial Electronics Society (IECON 2005). Raleigh, NC, USA. 6-10 November 2005, pages 1335-1340. © 2005 IEEE. By permission.en
dc.relation.haspart[Publication 3]: X. Wang, X. Z. Gao, and S. J. Ovaska. 2008. A novel particle swarm-based method for nonlinear function optimization. International Journal of Computational Intelligence Research, volume 4, number 3, pages 281-289. © 2008 Machine Intelligence Research Laboratories (MIR Labs). By permission.en
dc.relation.haspart[Publication 4]: Xiao-Zhi Gao, Xiaolei Wang, and Seppo Jari Ovaska. Uni-modal and multi-modal optimization using modified harmony search methods. International Journal of Innovative Computing, Information and Control, in press. © 2009 by authors and © 2009 ICIC International. By permission.en
dc.relation.haspart[Publication 5]: Xiaolei Wang, Xiao-Zhi Gao, and Seppo J. Ovaska. 2009. Fusion of clonal selection algorithm and harmony search method in optimisation of fuzzy classification systems. International Journal of Bio-Inspired Computation, volume 1, numbers 1-2, pages 80-88. © 2009 Inderscience Enterprises. By permission.en
dc.relation.haspart[Publication 6]: X. Z. Gao, X. Wang, and S. J. Ovaska. 2009. Fusion of clonal selection algorithm and differential evolution method in training cascade–correlation neural network. Neurocomputing, volume 72, numbers 10-12, pages 2483-2490. © 2008 Elsevier Science. By permission.en
dc.relation.haspart[Publication 7]: X. Wang, X. Z. Gao, and S. J. Ovaska. 2008. A simulated annealing-based immune optimization method. In: Proceedings of the 2nd International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2008). Porvoo, Finland. 17-19 September 2008, pages 41-47. © 2008 by authors.en
dc.relation.haspart[Publication 8]: X. Wang, X. Z. Gao, and S. J. Ovaska. 2007. A hybrid optimization algorithm based on ant colony and immune principles. International Journal of Computer Science & Applications, volume 4, number 3, pages 30-44. © 2007 Technomathematics Research Foundation (TMRF). By permission.en
dc.relation.ispartofseriesTKK dissertations, 161en
dc.subject.keywordNature-Inspired Computation (NIC)en
dc.subject.keywordhybrid algorithmsen
dc.subject.keywordoptimizationen
dc.subject.otherElectrical engineeringen
dc.titleHybrid nature-inspired computation methods for optimizationen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotVäitöskirja (artikkeli)fi
dc.type.ontasotDoctoral dissertation (article-based)en
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local.aalto.digifolderAalto_68098
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