CAJ | 학술논문

为了提高粒子群优化算法中粒子搜索最优解的效率,该文在标准粒子群优化算法的基础上,提出一种改进的粒子群优化算法. 该方法通过对粒子飞行轨迹的分析,对种群中每个粒子构建了评价粒子性能差异的等级标准,并对认识系数和社会系数设计了对应的动态变化系数模型. 通过引入迁徙策略,使迁徙行为随机生成的新粒子更有可能接近全局最优解,更加有利于群体搜索跳出局部最优解和寻找全局最优解. 实验结果表明,与其他比较算法相比,该文提出的改进粒子群优化算法具有寻优能力强和搜索精度高等优点,测试准测上的实验数据验证了改进算法的有效性和可行性.
위료제고입자군우화산법중입자수색최우해적효솔,해문재표준입자군우화산법적기출상,제출일충개진적입자군우화산법. 해방법통과대입자비행궤적적분석,대충군중매개입자구건료평개입자성능차이적등급표준,병대인식계수화사회계수설계료대응적동태변화계수모형. 통과인입천사책략,사천사행위수궤생성적신입자경유가능접근전국최우해,경가유리우군체수색도출국부최우해화심조전국최우해. 실험결과표명,여기타비교산법상비,해문제출적개진입자군우화산법구유심우능력강화수색정도고등우점,측시준측상적실험수거험증료개진산법적유효성화가행성.
To improve the efficiency of the particle swarm optimization algorithm for particles searching optimal solutions, an improved particle swarm optimization ( IPSO ) algorithm is proposed based on the standard PSO. Each particle has a corresponding grading standard by trajectory analysis of flight path,and two dynamic models of coefficients are designed for the cognitive coefficient and social one, respectively. In addition, through the introduction of the migration strategy, the newly obtained particles are more likely closer to the global optimal solution to a certain extent,and it is easy to jump out of the local optimal solution to search for the optimal solution. Simulation results show the IPSO algorithm has powerful optimizing ability and higher search veracity. The experimental data on the test criterion verify the effectiveness and the feasibility of the improved algorithm.