Performance Enhancement of Distributed Quasi Steady-State Genetic Algorithm

TL;DR

This paper introduces a novel distributed genetic algorithm scheme that uses adaptive clustering and co-evolution to improve optimization performance on unimodal and multimodal functions.

Contribution

It presents a new population distribution method with adaptive clustering and independent cluster evolution for enhanced genetic algorithm performance.

Findings

Improved convergence on test functions.

Effective cluster merging based on performance.

Enhanced exploration and exploitation balance.

Abstract

This paper proposes a new scheme for performance enhancement of distributed genetic algorithm (DGA). Initial population is divided in two classes i.e. female and male. Simple distance based clustering is used for cluster formation around females. For reclustering self-adaptive K-means is used, which produces well distributed and well separated clusters. The self-adaptive K-means used for reclustering automatically locates initial position of centroids and number of clusters. Four plans of co-evolution are applied on these clusters independently. Clusters evolve separately. Merging of clusters takes place depending on their performance. For experimentation unimodal and multimodal test functions have been used. Test result show that the new scheme of distribution of population has given better performance.