The Particle Swarm Multi-subspace Augmentation Optimization

In this article, the proposed Particle Swarm Optimization (PSO) variant uses a search space with distinct subspaces for each particle in the population respectively in the exploration of the optimum solution. What is normally done for a reduction in swarm size and achieving a much quicker response in PSO is to manually set the swarm size and other auxiliary constants through trial and error. An algorithm is proposed which assigns a particle to a discrete subspace and combines each particles position to form the solution at every functional evaluation. This assignment of the particle to a discrete subspace is suitable for swarm size reduction and quick convergence with less iteration. The theoretical basis is provided for the proposed algorithm and empirical studies are conducted to compare the proposed algorithm with PSO and some selected optimization algorithms on reference benchmark test functions. Based on the experimental results, the proposed algorithm has a higher accuracy than the other optimization algorithms.