LAUSR

Particle Swarm Optimization (PSO) is a metaheuristic evolutionary computation technique inspired by the social behavior of birds and fish flock. The classical PSO has limitations of slow convergence rate and trapping in local minima, as the dimensions of the data increase. Moreover, majority of improvements made by the researchers in optimization techniques have focused on the accuracy of solution and have overlooked the convergence speed of an algorithm. Keeping in view the need of an optimization algorithm with fast convergence speed, suitable for high dimensional data space, this article proposes a novel concept of Multi-Cluster Jumping PSO. In the proposed method, the particles in the swarm are divided in different clusters to search for the global optimum solution. Each cluster in the swarm has its own cluster best position which is the best position within a cluster and the global best position is located by clusters communication. In order to avoid trapping in the local optima, a jumping strategy is incorporated for stuck particles through relocation of particles to a random new position. Instead of random initialization of the particles, a semi-random initialization is opted by dividing the entire search space and the distribution of particles over a search space is done in independent slots. The proposed approach has the ability to overcome the limitations of classical evolutionary computation methods and is suitable for high dimensional dynamic data. An extensive experimentation is carried out to optimize twelve benchmark functions using the proposed Multi-Cluster Jumping PSO and a significant difference is observed in the convergence speed of the proposed method over the existing state-of-the-art approaches.