CAJ | 학술논문

针对粒子群优化算法搜索精度不高、整体上容易陷入局部最小的不足，提出了一种改进的粒子群优化算法。该算法一方面在速度更新式中用粒子群中粒子个体极值的加权值替代粒子的个体极值，另外通过使用两种非线性递减函数对惯性权重进行调整，这种改进有效地提高了粒子群优化算法的收敛速度和全局寻优能力。然后，通过对4个基准函数的仿真，验证了本文改进算法的全局收敛寻优能力。最后，将本文改进算法应用于珠三角地区某市4G网络基站选址优化中。仿真和应用的结果表明，改进后的粒子群优化算法具有更高的收敛速度和全局寻优能力。
침대입자군우화산법수색정도불고、정체상용역함입국부최소적불족，제출료일충개진적입자군우화산법。해산법일방면재속도경신식중용입자군중입자개체겁치적가권치체대입자적개체겁치，령외통과사용량충비선성체감함수대관성권중진행조정，저충개진유효지제고료입자군우화산법적수렴속도화전국심우능력。연후，통과대4개기준함수적방진，험증료본문개진산법적전국수렴심우능력。최후，장본문개진산법응용우주삼각지구모시4G망락기참선지우화중。방진화응용적결과표명，개진후적입자군우화산법구유경고적수렴속도화전국심우능력。
Since the PSO search accuracy is not high and its easy falling into the disadvantage of local minimum as a whole , this article proposes an improved particle swarm optimization algorithm .On the one hand the updated velocity formula using the weighted value of individual extreme of particles in particle swarm replaces the individual extreme of particles in this algorithm .On the other hand this improvement effectively enhances the convergence speed and optimal global searching ability of the particle swarm opti -mization by adjusting the inertia weight of two non-linear decreasing function .Then , the researchers veri-fy the global convergence and optimal searching ability of the improved algorithm by simulating the four reference functions.Finally, the improved algorithm is applied to the optimized location of a 4G network base station in a city of the Pearl River Delta region .The results and applications demonstrate that the im-proved particle swarm optimization possesses greater optimized convergence speed and global searching a -bility.