CAJ | 학술논문

应用改进的粒子群算法进行桁架结构优化设计. 首先, 在确定初始种群时用随机方向法产生一组适应环境值较高的初始种群, 使算法快速收敛于全局最优解, 降低了算法的时间复杂度; 其次, 将模糊推理应用于算法的参数动态调整中, 提高种群的适应搜索空间环境的能力; 最后, 将改进的粒子群算法应用于桁架结构优化设计中. 算例表明, 改进后算法的搜索性能得到了一定改善,为其应用于大型复杂的工程结构优化设计提供了借鉴.
응용개진적입자군산법진행항가결구우화설계. 수선, 재학정초시충군시용수궤방향법산생일조괄응배경치교고적초시충군, 사산법쾌속수렴우전국최우해, 강저료산법적시간복잡도; 기차, 장모호추리응용우산법적삼수동태조정중, 제고충군적괄응수색공간배경적능력; 최후, 장개진적입자군산법응용우항가결구우화설계중. 산례표명, 개진후산법적수색성능득도료일정개선,위기응용우대형복잡적공정결구우화설계제공료차감.
The purpose of this paper is to improve the search performance of the particle swarm optimization algorithm applying to solve truss structure optimization. The method not only improves its parameters, but can produce the higher quality particles of the initial population than the original algorithm. A group of initial population is produced, which owns the higher capacity of adapting environment values. The algorithm not only convergences to the global optimal solution, but reduces the time complexity of the algorithm. Fuzzy reasoning method is used in the dynamic adjustment of parameters, which can improve the ability to adapting to the search space environment. The example that the improved particle swarm algorithm is applied to the truss structure optimization design shows that the search performance is modified. The conclusion is that the improvement method of this paper improves the search performance of the particle swarm optimization, and it provides reference for the complex engineering structure optimization design.