Abstract
Dynamic optimization problem (DOP) is a kind of optimization problem in the real world, and its optimization target or constraint may change over time. In order to solve the dynamic optimization problem effectively, a hybrid algorithm is proposed based on artificial immune network algorithm. On the one hand, update operator in particle swarm algorithm is introduced to realize a supplement to artificial immune network algorithm and improve its convergence speed. On the other hand, due to its local search capability, chaotic variables are used to avoid premature convergence in the late optimization of artificial immune network algorithm and improve search precision. At the same time, a Cauchy mutation operator is added to improve the global search capability. Simulation experiments are conducted on GDBG. The experimental results show that the algorithm can not only effectively improve the global search ability, convergence speed and search precision, but also keep the population diversity. Compared with similar optimization algorithms, the algorithm has obvious advantages in error and convergence indexes.
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