Particle Swarms: The Second Decade

Particle swarm optimisation (PSO) was born just over ten years ago. The initial ideas on particle swarms of Kennedy and Eberhart were aimed at producing computational intelligence by exploiting simple analogues of social interaction, rather than purely individual cognitive abilities. The first simulations [1] were influenced by Heppner’s and Grenander’s work [2] and involved analogues of bird flocks searching for corn. These soon developed [1, 3, 4] into a powerful optimisation method—the particle swarm optimiser. In PSO, a number of simple entities—the particles—are placed in the search space of some problem or function, and each evaluates the objective function at its current location. Each particle then determines its movement through the search space by combining some aspect of the history of its own current and best (best-fitness) locations with those of one or more members of the swarm, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually, the swarm as a whole, like a flock of birds collectively foraging for food, moves close to an optimum of the fitness function. The particle swarm is more than just a collection of particles. A particle by itself has almost no power to solve any problem; progress occurs only when the particles interact. Problem solving is a population-wide phenomenon, emerging from the individual behaviours of the particles through their interactions. In any case, populations are organised according to some sort of communication structure or topology, often thought of as a social network. Each particle communicates with some other particles and is affected by the best point found by any member of its topological neighbourhood. The potential kinds of topologies of social networks are hugely varied, but in practice certain types, such as rings and fully connected networks, have been used more frequently.