Perfectly convergent particle swarm optimisation in multidimensional space

In this paper a novel evolutionary algorithm, perfectly convergent particle swarm optimisation (PCPSO) has been proposed. This is an intelligent algorithm which does not get trapped in local minima by using personal best value along with new parameters and new velocity update equation for better exploration in the search space. Velocity clamping is used to avoid the particles from escaping the search space by preventing the initial explosion of particle velocity. The velocity clamping effectively help to control the maximum velocity of the particles by limiting the particle step size and hence the particle will have to perform further steps in the search space to travel the same distance which determines its search precision for exploration or exploitation and finally align them towards the true global minimum. Experimental results show that by using perfect convergent particle swarm optimisation (PCPSO) approach computational efficiency is increased as compared to other variants of PSO and finds fast true global minimum.

Automated summary

The PCPSO algorithm avoids getting trapped in local minima by using personal best value, new parameters, and new velocity update equation for better exploration in the search space. Velocity clamping effectively helps to control the maximum velocity of the particles, limiting the particle step size and improving search precision to align particles towards the true global minimum.