A Diverse Clustering Particle Swarm Optimizer for Dynamic Environment: To Locate and Track Multiple Optima

TL;DR

This paper introduces a novel particle swarm optimization algorithm designed for dynamic environments that effectively locates and tracks multiple optima while maintaining high diversity and convergence speed, outperforming existing methods.

Contribution

A new diverse clustering particle swarm optimizer is proposed, enhancing particle diversity and optima tracking in dynamic problems, with improved handling of overcrowded particles and exploration of undiscovered search space areas.

Findings

Outperforms state-of-the-art algorithms on Moving Peak Benchmark

Effectively tracks multiple optima in dynamic environments

Improves particle diversity and convergence speed

Abstract

In real life, mostly problems are dynamic. Many algorithms have been proposed to handle the static problems, but these algorithms do not handle or poorly handle the dynamic environment problems. Although, many algorithms have been proposed to handle dynamic problems but still, there are some limitations or drawbacks in every algorithm regarding diversity of particles and tracking of already found optima. To overcome these limitations/drawbacks, we have proposed a new efficient algorithm to handle the dynamic environment effectively by tracking and locating multiple optima and by improving the diversity and convergence speed of algorithm. In this algorithm, a new method has been proposed which explore the undiscovered areas of search space to increase the diversity of algorithm. This algorithm also uses a method to effectively handle the overlapped and overcrowded particles. Branke has proposed moving peak benchmark which is commonly used MBP in literature. We also have performed different experiments on Moving Peak Benchmark. After comparing the experimental results with different state of art algorithms, it was seen that our algorithm performed more efficiently.