Adaptable Evolutionary Particle Swarm Optimization

In this study we describe a method for extending particle swarm optimization. We have presented a novel approach for avoiding premature convergence to local minima by the introduction of diversity in the swarm. The swarm is made more diverse and is encouraged to explore by employing a mechanism which allows each particle to use a different equation to update its velocity. This equation is also continuously evolved through the use of genetic programming to ensure adaptability. Results from experimentation show that the modified PSO performs exceptionally well and is very good at finding the exact optimum.