Distributed Particle Swarm Optimization for Structural Bayesian Network Learning

Particle Swarm Optimization (PSO) was first introduced as a concept for a non-linear optimizer by Kennedy and Eberhart in 1995. Their seminal work articulates a technique of evolutionary computation, which has its origin in artificial intelligence and simplified social models such as bird flocking and fish schooling (Kennedy & Eberhart, Nov. 1995; Kennedy & Eberhart, Oct. 1995). Its early appeal lay in its use of only primitive mathematical operators and computational economy with regard to both memory and speed. The authors were influenced by the work of Reynolds, Heppner and Grenander in modeling bird flocking and recognized that the fundamental hypothesis to the development of PSO is that an evolutionary advantage is obtained by the social sharing of information among members of the same species. They stated that the simulation of the graceful but unpredictable choreography of a bird flock by collision-proof agents could be used as an effective optimizer for a wide range of functions.