Integrated optimization of structure and control systems based on a modified adaptive multi-population genetic algorithm
A modified adaptive multi-population genetic algorithm(MAMPGA)was proposed to better solve the integrated optimization problem of building structure and active control systems.Specifically,structure parameters,control algorithm parameters and the placement of actuators were optimized simultaneously.The proposed MAMPGA was improved in coding method,initial population generation,selection strategy,adaptive regulation of crossover probability and mutation probability,migration strategy in multi-population co-evolution,etc.The results show that the optimization result of the MAMPGA is generally consistent with that of an improved simple genetic algorithm(ISGA),indicating the correctness and accuracy of the former algorithm.When the optimal solution is obtained for the first time,the average evolutionary generations of the MAMPGA and the ISGA are 320 and 730 respectively,which means that the former algorithm converges faster than the latter.The MAMPGA can reach or approach the optimal solution each time,so it can overcome the shortcoming of strong randomness in optimization results of the ISGA.The active control system optimized by the MAMPGA achieves a good vibration reduction effect.The peak values of inter-story drift angles and floor absolute accelerations of the actively controlled structure under E1 Centro wave are decreased by 54.5% and 46.7% respectively on average compared to those without control.The numerical example demonstrates the effectiveness of the MAMPGA,and the integrated optimization of building structure and active control systems is realized.
active controlstructure and control systemintegrated optimizationadaptive genetic algorithmmulti population
万方数据
维普
