Asynchronous digenetic Particle Swarm Optimization for global and sustainable search

Particle Swarm Optimization (PSO), which has attracted attention as a global optimization method in recent years, has a drawback in that sustainable search cannot be performed until the end of computation due to its strong convergence trend. In this paper, in order to realize a sustainable search in PSO, the improved PSO using concepts of particle ages and digenesis is proposed. In the new PSO, parameters in the update formula are degenerated and a stagnant particle is erased if it loses activity, and then a new search point in which large parameter values are assigned. In addition, information regarding the elite point of all searching points until the current time is reflected to new points in next generation. The effectiveness of the improved method is confirmed through applications to benchmark problems.