Particle swarm optimization
-“utilizes a ‘population’ of candidate solutions to evolve to an optimal or near-optimal solution to a problem.
-“The degree of optimality is measured by a fitness function defined by the user.”
-“differs from evolutionary computation methods in the the population members, called particles, are flown through the problem hyperspace.”
-initialized with random values and “stochastically assigned velocities.”
-“each iteration, each particle’s velocity is stochastically accelerated toward its previous best position (where it had its highest fitness value) and toward a neighborhood best position (the position of highest fitness by any particle in its neighborhood).”
-“powerful, easy to understand, easy to implement, and computationally intensive.”
-“central algorithm comprises just two lines of computer code”
-“often at least an order of magnitude faster than other evolutionary algorithms on benchmark functions.”
-“extremely resistant to being trapped in local optima.”
-“applied to fields as diverse as electric/hybrid vehicle battery pack state of charge, human performance assessment, and human tremor diagnosis.”
-“provides evidence for theoretical perspectives on mind, consciousness, and intelligence.” – intelligence is at least partly attributed to the ability to interact with others and base our behaviors on those interactions.