  In this paper we propose a Particle Swarm Optimization algorithm combined
with Novelty Search. Novelty Search finds novel place to search in the search
domain and then Particle Swarm Optimization rigorously searches that area for
global optimum solution. This method is never blocked in local optima because
it is controlled by Novelty Search which is objective free. For those functions
where there are many more local optima and second global optimum is far from
true optimum, the present method works successfully. The present algorithm
never stops until it searches entire search area. A series of experimental
trials prove the robustness and effectiveness of the present algorithm on
complex optimization test functions.

Neural and Evolutionary Computing;Artificial Intelligence