CAJ | 학술논문

离散粒子群算法能充分利用粒子的局部极值和全局极值信息,但收敛速度慢、精度低;Inver-Over算子收敛速度快、精度高,但学习具有盲目性.结合二者优点,文中提出一种基于Inver-Over算子的改进离散粒子群优化算法.为防止早熟收敛,引入局部最优子群的概念,使粒子向局部最优子群中粒子学习而不是向个体局部最优学习.引入3个参数:学习选择概率用以确定粒子的学习对象,代数阈值确定何时向全局最优粒子学习,局部最优子群比决定最优子群的规模.讨论这些参数的选择原则,并给出相应参考选择范围.研究表明,文中算法与普通离散粒子群优化算法和郭涛算法相比,收敛速度和求解精度都有较大提高.
리산입자군산법능충분이용입자적국부겁치화전국겁치신식,단수렴속도만、정도저;Inver-Over산자수렴속도쾌、정도고,단학습구유맹목성.결합이자우점,문중제출일충기우Inver-Over산자적개진리산입자군우화산법.위방지조숙수렴,인입국부최우자군적개념,사입자향국부최우자군중입자학습이불시향개체국부최우학습.인입3개삼수:학습선택개솔용이학정입자적학습대상,대수역치학정하시향전국최우입자학습,국부최우자군비결정최우자군적규모.토론저사삼수적선택원칙,병급출상응삼고선택범위.연구표명,문중산법여보통리산입자군우화산법화곽도산법상비,수렴속도화구해정도도유교대제고.
Though the discrete particle swarnl optimization(DPSO)can make the best of the local and global optima of particles,it converges slowly with low precision.The Guo Tao algorithm converges with fast high precision,but it is blindfold to learn from the other particles.A modified discrete particle swarmoptimization algorithm is presented based on the inver-over operator(IDPSO).To prevent premature convergence,the local sub-optimum particle swarm is introduced into IDPSO.Particles learn from the particles in the local sub-optimum particle swarm instead of their local optima.Three new parameters are introduced into IDPSO.Learning selection probability is introduced to select the particle to be learned.A generation threshold is introduced to define when to learn from the global particle.Local sub-optimum particle swarm ratio is introduced to define the size of the sub-optimum particle swarm.Selecting principles of these parameters is detailed discussed and the general reference scopes ale given.Experiments are carried out on the traveling salesman problem and the results show that the modifiedIDPSO achieves good results compared with the Guo Tao algorithm and the general DPSO.The proposed algorithm improves both the convergence speed and solution precision.