An Improved Random Inertia Weighted Particle Swarm Optimization

Interactive cooperation of local best and global best solution encourages particles to move towards them, with a hope that better solution may present in the neighboring positions around local best or global best. However, this encouragement does not guarantees that movements taken by particle will always be the suitable one (comparatively better solution). With the influence of three random parameters in PSO-RANDIW increases exploration power as well as probability of unsuitable movements (move towards comparatively worst solution). These unsuitable movement may delay in convergence. In this paper, we have introduced a noble method to avoid such move with cognition of particle´s own worst solution. Analysis on well known four benchmark functions shows proposed approach performance is comparatively better.