Title :
Stochastic velocity threshold inspired by evolutionary programming
Author :
Cui, Zhihua ; Cai, Xingjuan ; Zeng, Jianchao
Author_Institution :
Complex Syst. & Comput. Intell. Lab., Taiyuan Univ. of Sci. & Technol., Taiyuan, China
Abstract :
Particle swarm optimization (PSO) is a new robust swarm intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the computational efficiency, there are still many works need to do. Inspired by evolutionary programming theory, this paper proposes a self-adaptive particle swarm optimization in which the velocity threshold dynamically changes during the course of a simulation, and two further techniques are designed to avoid badly adjusted by the self-adaption. Six benchmark functions are used to testify the new algorithm, and the results show the new adaptive PSO clearly leads to better performance, although the performance improvements were found to be dependent on problems.
Keywords :
evolutionary computation; particle swarm optimisation; evolutionary programming; robust swarm intelligence; self-adaptive particle swarm optimization; stochastic velocity threshold; Benchmark testing; Computational efficiency; Convergence; Equations; Evolutionary computation; Genetic programming; Particle swarm optimization; Space exploration; Stochastic processes; Stochastic systems;
Conference_Titel :
Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4244-5053-4
DOI :
10.1109/NABIC.2009.5393434