Particle Swarm Optimization with Independent Adaptive Parameter Adjustment

Abstract

Aiming at the problems that traditional particle swarm optimization (PSO) algorithm is prone to fall into local optimum and depends on the value of parameters when solving complex optimization problems, an independent adaptive parameter adjustment particle swarm optimization algorithm (IAP-PSO) is proposed. The evolutionary ability of particle, the evolutionary ability of population and evolutionary rate are redefined. On this basis, the indep-endent adjustment strategy of inertia weight and learning factor of PSO is given, which effectively balances the ability of local search and global search. In order to maintain the diversity of population and improve the speed of particle convergence to the global optimal position, a new particle reconstruction strategy is proposed in the iteration process of the algorithm. The particles with weak evolutionary ability learn from those with strong evolutionary ability and generate new particles. Finally, 10 benchmark functions in CEC 2013 are compared with four improved particle swarm optimization algorithms in different dimensions. The experimental results show that IAP-PSO algori-thm has high efficiency in solving complex functions. Convergence analysis shows the effectiveness of the algorithm.

Keywords

• particle swarm optimization (pso)
• inertia weight
• learning factor
• particle reconstruction