ANTLION OPTIMIZATION ALGORITHM BASED DATA CLUSTERING

Abstract

Nature is the principal source for proposing new optimization methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution. Many of these methods are inspired by swarm behaviors in nature. In this work we propose a new swarm based clustering algorithm Antlion Optimized Clustering Algorithm. Similar to other population-based algorithms, the Antlion Optimization Algorithm (ALO) starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. At each iteration of the ALO, the best candidate is selected to be the Best Antlion, which then starts hunting the ants. The ALO algorithm mimics the hunting mechanism of antlions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are implemented. Various data cluster centers are initialized in the form of antlions and then these centres are optimized using these five hunting steps.