{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1363670318918135552.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1155/2008/861275"}},{"identifier":{"@type":"URI","@value":"http://downloads.hindawi.com/archive/2008/861275.pdf"}},{"identifier":{"@type":"URI","@value":"http://downloads.hindawi.com/archive/2008/861275.xml"}},{"identifier":{"@type":"URI","@value":"https://onlinelibrary.wiley.com/doi/pdf/10.1155/2008/861275"}}],"dc:title":[{"@value":"The Generalized PSO: A New Door to PSOEvolution"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p>A generalized form of the particle swarm optimization (PSO) algorithm is\npresented. Generalized PSO (GPSO) is derived from a continuous version of PSO adopting a\ntime step different than the unit.  Generalized continuous particle swarm optimizations are compared in terms of\nattenuation and oscillation. The deterministic and stochastic stability regions and their respective\nasymptotic velocities of convergence are analyzed as a function of the time step and the\nGPSO parameters. The sampling distribution of the GPSO algorithm helps to study the effect\nof stochasticity on the stability of trajectories. The stability regions for the second‐, third‐, and\nfourth‐order moments depend on inertia, local, and global accelerations and the time step and are\ninside of the deterministic stability region for the same time step. We prove that stability regions\nare the same under stagnation and with a moving center of attraction. Properties of the \nsecond‐order moments variance and covariance serve to propose some promising parameter sets. High\nvariance and temporal uncorrelation improve the exploration task while solving ill‐posed inverse\nproblems. Finally, a comparison is made between PSO and GPSO by means of numerical experiments\nusing well‐known benchmark functions with two types of ill‐posedness commonly found in inverse\nproblems: the Rosenbrock and the “elongated” DeJong functions (global minimum located in a\nvery flat area), and the Griewank function (global minimum surrounded by multiple minima). \nNumerical simulations support the results provided by theoretical analysis. Based on these results,\ntwo variants of Generalized PSO algorithm are proposed, improving the convergence and the\nexploration task while solving real applications of inverse problems.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1383670318918135553","@type":"Researcher","foaf:name":[{"@value":"J. L. Fernández Martínez"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670318918135552","@type":"Researcher","foaf:name":[{"@value":"E. García Gonzalo"}]}],"contributor":[{"@id":"https://cir.nii.ac.jp/crid/1380019692241806080","@type":"Researcher","foaf:name":[{"@value":"Riccardo Poli"}],"role":"editor"}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"16876229"},{"@type":"EISSN","@value":"16876237"}],"prism:publicationName":[{"@value":"Journal of Artificial Evolution and Applications"}],"dc:publisher":[{"@value":"Wiley"}],"prism:publicationDate":"2008-01","prism:volume":"2008","prism:number":"1"},"reviewed":"false","dc:rights":["http://creativecommons.org/licenses/by/3.0/"],"url":[{"@id":"http://downloads.hindawi.com/archive/2008/861275.pdf"},{"@id":"http://downloads.hindawi.com/archive/2008/861275.xml"},{"@id":"https://onlinelibrary.wiley.com/doi/pdf/10.1155/2008/861275"}],"createdAt":"2008-05-15","modifiedAt":"2024-07-03","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1390001205350590592","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"ja","@value":"PSOを用いたレイリー波分散曲線のインバージョン"},{"@language":"en","@value":"INVERSION ANALYSIS ON DISPERSION CURVE OF RAYLEIGH WAVE BY USING PARTICLE SWARM OPTIMIZATION"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1155/2008/861275"},{"@type":"CROSSREF","@value":"10.2208/jscejseee.71.i_725_references_DOI_F5Rce9lFR615hL6zCbnycchc2ub"}]}