| Optimum design has been the important content and objectives of engineering field,since the1940s, people had been using the inspiration from biological systems, designedand constructed a lot of bionic intelligent optimization algorithms to solve the optimaldesign of engineering problems. This paper uses the improved artificial searching swarmalgorithm to optimize a twin-e AC contactor electromagnetic mechanism and high-voltagevacuum interrupter. Main work as followings:Firstly, the paper proposed chaos artificial searching swarm algorithm throughanalyzing and studying the artificial searching swarm algorithm. The optimizationalgorithm is improved by using the randomicity,ergodicity and regularity of chaos and theimproved algorithm program is wrote by C++6.0. Then select classical test functions to testthe algorithm performance. Test results show that the optimization capability of improvedalgorithm has been effectively improved.Secondly, a parametric finite element model of a twin-e AC contactor electromagneticmechanism is established and the electromagnetic attraction between static and dynamiccore is calculated by using ANSYS software; optimization mathematical model isestablished; the improved artificial searching swarm algorithm combined with ANSYSsoftware is used to optimize the electromagnetic mechanism. The optimization results showthat volume of electromagnetic mechanism is reduced based on meeting the contactorperformance, saving production cost and achieving the optimization purpose.Thirdly, a parametric finite element model of72.5KV high-voltage vacuuminterrupter is established and the internal electric field distribution is calculated by usingANSYS software; optimization mathematical model is established; finally the improvedartificial searching swarm algorithm combined with ANSYS software is used to optimizethe vacuum interrupter. The optimization results show that the electric field distributioninside the vacuum interrupter is more uniform after the optimization, and the peak electricfield decreases, and it proved the feasibility and effectiveness of optimization. |