Gathering algorithm: A new concept of PSO based metaheuristic with dimensional mutation

In this paper, a novel PSO based metaheuristic is proposed. This described approach is inspired by human gathering mechanisms. Each particle is given a possibility to follow a randomly selected particle from the swarm. When a promising search area is found by the particle, it remains stationary for a given number of iterations improving the chances of other particles following such a stationary particle into that search area. In this novel concept, the location of global best solution is not used as the attraction point for the particles. But the convergence into promising search areas is driven by the snowball effect of increasing number of stationary particles in the particular promising areas. Two different dimensional mutations are applied on stationary particles for the further improvement the performance of the algorithm. The key mechanism of the algorithm is described here in detail. The performance is tested on the CEĆ13 benchmark set with promising results. The results are compared with two current state-of-art PSO based optimization techniques.