Particle Swarm Optimization based on Novelty Search

TL;DR

This paper introduces a hybrid Particle Swarm Optimization algorithm guided by Novelty Search, enabling effective global optimization in complex landscapes by avoiding local optima and exploring the entire search space.

Contribution

It combines Novelty Search with Particle Swarm Optimization to improve global search capabilities and avoid local optima in complex optimization problems.

Findings

Proves robustness on complex test functions

Effectively finds global optima in multi-modal landscapes

Never gets trapped in local optima

Abstract

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.