| 000 | 03486nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-70851-5 | ||
| 003 | DE-He213 | ||
| 005 | 20220801221155.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180314s2018 sz | s |||| 0|eng d | ||
| 020 |
_a9783319708515 _9978-3-319-70851-5 |
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| 024 | 7 |
_a10.1007/978-3-319-70851-5 _2doi |
|
| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
|
| 072 | 7 |
_aTEC009000 _2bisacsh |
|
| 072 | 7 |
_aUYQ _2thema |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aOlivas, Frumen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _954514 |
|
| 245 | 1 | 0 |
_aDynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic _h[electronic resource] / _cby Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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| 300 |
_aVII, 105 p. 25 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aSpringerBriefs in Computational Intelligence, _x2625-3712 |
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| 505 | 0 | _aIntroduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results. | |
| 520 | _aIn this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the optimization method, because with the modification of parameters, these will affect directly the way in which the global or local search are performed. Three different optimization methods were used to verify the improve-ment of the proposed methodology. In this case the optimization methods are: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), where some parameters are se-lected to be dynamically adjusted, and these parameters have the most im-pact in the behavior of each optimization method. Simulation results show that the proposed methodology helps to each optimization method in obtaining better results than the results obtained by the original method without parameter adjustment. | ||
| 650 | 0 |
_aComputational intelligence. _97716 |
|
| 650 | 0 |
_aArtificial intelligence. _93407 |
|
| 650 | 1 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 700 | 1 |
_aValdez, Fevrier. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _954515 |
|
| 700 | 1 |
_aCastillo, Oscar. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _954516 |
|
| 700 | 1 |
_aMelin, Patricia. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _954517 |
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| 710 | 2 |
_aSpringerLink (Online service) _954518 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319708508 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319708522 |
| 830 | 0 |
_aSpringerBriefs in Computational Intelligence, _x2625-3712 _954519 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-70851-5 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c79373 _d79373 |
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