Translated Title

An application of simulated annealing to collaborative optimization

Translated Abstract

In　order　to　deal　with　the　shortcoming　of　traditional　collaborative　optimization,　for　example　the　results　are　always　not　converging　and　local　optimal　solution;an　improved　collaborative　optimization　based　on　simulated　annealing　is　presented.　Under　the　premise　of　the　advantages　of　the　parallel　and　autonomy　of　the　standard　collaborative　optimization　algorithm,　the　ASA-MMFD-CO　replaces　the　single　optimization　algorithm　by　the　hybrid　optimization　strategy　based　on　the　global　algorithm　ASA　and　the　gradient　algorithm　MMFD　in　the　system　level.　Meanwhile,　dynamic　relaxation　factor　is　used　in　the　optimization.　The　global　optimization　and　accuracy　can　be　guaranteed,　and　the　shortcoming　of　tradi-tional　collaborative　optimization　can　be　resolved.　Two　typical　MDO　examples　are　adopted　to　test　the　improved　CO.　The　results　show　that　it　has　better　accuracy,　convergence　rate　and　stability.

Translated Keyword

collaborate optimization
dynamic relaxation
hybrid optimization
simulated annealing

Classification

TH122

Type

Project Name

Project No.

Access Number

WF:perioarticalfzdxxb201803014

Language

Chinese

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