| 000 | 01752naa a2200241 a 4500 | ||
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| 003 | AR-LpUFIB | ||
| 005 | 20250311170419.0 | ||
| 008 | 230201s2009 xx o 000 0 eng d | ||
| 024 | 8 |
_aDIF-M6599 _b6738 _zDIF006017 |
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| 040 |
_aAR-LpUFIB _bspa _cAR-LpUFIB |
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| 100 | 1 | _aLópez, Javier | |
| 245 | 1 | 0 | _aParticle swarm optimization with oscillation control |
| 300 | _a1 archivo (438,3 kB) | ||
| 500 | _aFormato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) | ||
| 520 | _aParticle Swarm Optimization (PSO) is a metaheuristic that has been successfully applied to linear and non-linear optimization problems in functions with discrete and continuous domains. This paper presents a new variation of this algorithm - called oscPSO - that improves the inherent search capacity of the original (canonical) version of the PSO algorithm. This version uses a deterministic local search method whose use depends on the movement patterns of the particles in each dimension of the problem. The method proposed was assessed by means of a set of complex test functions, and the performance of this version was compared with that of the original version of the PSO algorithm. In all cases, the oscPSO variation equaled or surpassed the performance of the canonical version of the algorithm. | ||
| 534 | _aAnnual conference on genetic and evolutionary computation GECCO 09 (11º : 2009 : Montreal, Canadá) Proccedings ACM, Nueva York, 2009, pp.1751-1752. | ||
| 650 | 4 | _aCOMPUTACIÓN EVOLUTIVA | |
| 650 | 4 | _aOPTIMIZACIÓN | |
| 700 | 1 | _aLanzarini, Laura Cristina | |
| 700 | 1 | _aDe Giusti, Armando Eduardo | |
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1145/1569901.1570141 |
| 942 | _cCP | ||
| 999 |
_c55799 _d55799 |
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