A Multi-Center PSO Algorithm with Memory Ability and Its Application to the Online Modelling of an Underwater Vehicle Thruster
To improve the performance of Particle swarm optimization (PSO) in online optimization problems, a multi-center PSO algorithm with memory ability was proposed. Main strategies of the proposed algorithm include the initial population optimization based on historical optimal solution and improved chaos mapping and the multi-center collaborative search. To verify online optimization performance, the proposed algorithm is applied to the online modelling process of an underwater vehicle thruster to optimize the modeling parameters. Result proves the superiority of the proposed algorithm in online optimization problem.
KeywordsOnline optimization PSO Tent mapping Multi-center collaborative search Online modeling Underwater vehicles
This work is supported by the National key research and development program (2017YFC0305901), National Natural Science Foundation of China (91648204).
- 7.Wang, F., Zhang, H., Li, K., et al.: A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf. Sci. 436 (2018)Google Scholar
- 12.Nie, R., Zhang, W.G., Li, G.W., et al.: Adaptive chaos hybrid multi-objective genetic algorithm based on the tent map. J. Beijing Univ. Aeronaut. Astronaut. 38(8), 1010–1016 (2012)Google Scholar
- 13.Zambrano-Bigiarini, M., Clerc, M., Rojas, R.: Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: 2013 IEEE Congress on Evolutionary Computation, pp. 2337–2344. IEEE Press, New York (2013)Google Scholar