Particle Swarm Optimization based on Novelty Search
This addresses optimization challenges for complex functions where traditional methods fail, but it appears incremental as it hybridizes existing techniques.
The paper tackles optimization problems with many local optima by combining Particle Swarm Optimization with Novelty Search to avoid getting stuck, resulting in a robust algorithm that successfully searches entire domains.
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.