Zhang Kai

Particle swarm optimization (PSO) is well known for dealing with complex nonlinear problems. In recent years, many researchers developed improved PSO algorithms to enhance the search and convergence ability. However, when dealing with the engineering

To solve the engineering control system problems, a novel algorithm is proposed, which uses the method by changing chaotic intervals, called hybrid variable intervals piece-wise chaotic particle swarm optimization algorithm (HVIPCPSO). The numerical experiments illustrate this algorithm have a better performance comparing with other improved particle swarm algorithms. Two examples on PID optimizer and sliding mode control system have better results, which proves this algorithm’s application value.

In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles’ diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle’s historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO’s comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence.