计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
9期
98-102
,共5页
粒子群优化(PSO)算法%早熟收敛%向后传播(BP)神经网络%QoS路由
粒子群優化(PSO)算法%早熟收斂%嚮後傳播(BP)神經網絡%QoS路由
입자군우화(PSO)산법%조숙수렴%향후전파(BP)신경망락%QoS로유
Particle Swarm Optimization(PSO)algorithm%premature convergence%Back Propagation(BP)neural net-work%QoS routing
随机优化的粒子群算法（PSO）在解决待优化问题时，仅利用适应度函数对单个粒子所找到解的优劣进行判断，缺乏对种群总体状态的评估，导致算法经过一定次数的迭代后陷入局部收敛。改进算法BPPSO利用BP神经网络对种群进行状态划分，并根据划分结果对种群实施相应的扰动操作，从种群的角度对算法进行改进。仿真实验表明，改进算法能够增加种群多样性，提高优化精度，较好地解决了Ad Hoc网络的QoS路由问题，从而验证了所提算法的可行性和有效性。
隨機優化的粒子群算法（PSO）在解決待優化問題時，僅利用適應度函數對單箇粒子所找到解的優劣進行判斷，缺乏對種群總體狀態的評估，導緻算法經過一定次數的迭代後陷入跼部收斂。改進算法BPPSO利用BP神經網絡對種群進行狀態劃分，併根據劃分結果對種群實施相應的擾動操作，從種群的角度對算法進行改進。倣真實驗錶明，改進算法能夠增加種群多樣性，提高優化精度，較好地解決瞭Ad Hoc網絡的QoS路由問題，從而驗證瞭所提算法的可行性和有效性。
수궤우화적입자군산법（PSO）재해결대우화문제시，부이용괄응도함수대단개입자소조도해적우렬진행판단，결핍대충군총체상태적평고，도치산법경과일정차수적질대후함입국부수렴。개진산법BPPSO이용BP신경망락대충군진행상태화분，병근거화분결과대충군실시상응적우동조작，종충군적각도대산법진행개진。방진실험표명，개진산법능구증가충군다양성，제고우화정도，교호지해결료Ad Hoc망락적QoS로유문제，종이험증료소제산법적가행성화유효성。
The particle swarm algorithm of stochastic optimization(PSO)only uses fitness function to judge metrics of the found solution, but does not evaluate the overall swarm status. This causes local convergence of the algorithm after a certain times of iterations. The optimized algorithm BPPSO uses BP neural network to classify swarm status, and performs different disturbance operations to swarm according to the divide result. It is the algorithm optimization of swarm perspective. Simu-lation experiment results show that the BPPSO algorithm can increase swarm variety, and improve optimization accuracy. It solves the QoS routing problem of Ad Hoc network better, and proves feasibility and validity of the proposed algorithm.
