Euclidean Distance Based Particle Swarm Optimization

This paper proposes a technique for improving the convergence speed and the final accuracy of the Particle Swarm Optimization (PSO) by introducing a new adaptive inertia weight strategy based on Euclidean distance. This change does not inflict any major modifications to the basic algorithm. The proposed technique has shown significantly better performance as compared to other PSO variants on a test suite of ten optimization test functions evaluated on following performance metrics: time to locate the solution, scalability, quality of the final solution, and frequency of hitting the optima.