Abstract
In the field of swarm intelligence algorithms, particle swarm optimization algorithms have the ability of fast search and are widely used in lots of application scenarios because they are easy to implement. Therefore, different improvements have been made for their various properties. In this paper, aiming to accelerate its convergence speed, we further improve the existing hybrid particle swarm algorithm by using the idea of self adaptive and simulated annealing, examining its performance by solving Travelling salesman problem. It demonstrate that the relevant improvements can optimize the algorithm performance and have better convergence speed when iteration value was set 30, 50, or 70 by making comparions between the experiment results of both algorithm. The code used in this article is available on github. The experimental results show that the optimization proposed in this paper can optimize the algorithm to a certain extent at 30, 50, and 70 iterations, mainly because the improved algorithm can keep the convergence speed stable in more iterations.
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