Particle Swarm Optimization algorithm based on adaptive inertia weight

Inertia weight can significantly impact the effectiveness of the Particle Swarm Optimization (PSO) algorithm. This paper presents a method to improve performance of PSO optimization algorithm by changing the inertia weight dynamically and adaptively. According to the optimization requirement of PSO algorithm in different situations, the inertia weight in the proposed method can be adjusted automatically. Experimental results from numeral simulation on the standard functions are presented to demonstrate that the proposed method achieves good accuracy and precision in the presence of both two dimension and high dimension. Especially for high dimension, performance of the modified algorithm shows better advantage.