Oscillatory Particle Swarm Optimizer.

The Particle Swarm Optimization (PSO) algorithm is an attractive meta-heuristic approach for difficult optimization problems. It is able to produce satisfactory results when classical analytic methods cannot be applied. However, the design of PSO was usually based on ad-hoc attempts and its behavior could not be exactly specified. In this work, we propose to drive particle into oscillatory trajectories such that the search space can be covered more completely. A difference equation based analysis is conducted to reveal conditions that guarantee trajectory oscillation and solution convergence. The settings of cognitive and social learning factors and the inertia weight are then determined. In addition, a new strategy in directing these parameters to follow a linearly decreasing profile with a perturbation is formulated. Experiments on function optimizations are conducted and compared to currently available methods. Results have confirmed that the proposed Oscillatory Particle Swarm Optimizer (OSC-PSO) outperforms other recent PSO algorithms using adaptive inertia weights.