Abstract

This essay puts forward a cruise pricing model based on improved quantum particle swarm optimization, aiming at optimizing the pricing strategy and realizing the maximum sales income expected. Firstly, we combine the two factors – actual booking records and expected booking records in the process of cruises pricing – and improve the dynamic price-setting model based on demand learning put forward earlier. Then we improve the Dynamically Changing Weight’s Quantum-behaved Particle Swarm Optimization (DCWQPSO) based on multistage punish function, in order to faster the converging speed and avoid the problem of local optimum. Lastly, we use the improved DCWQPSO to find the best expected sales income in the improved pricing model. The instance analysis of cruise pricing shows that the process of constructing this model is reliable and logical. Also this model could better higher the maximum expected sales inc ome and better perform in future application.

This work is licensed under a Creative Commons Attribution 4.0 License.