Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

Sen Zhang,Yongquan Zhou

doi:10.1155/2015/481360

Abstract

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

Highlights

Swarm Intelligence (SI) has received much attention
In order to apply the grey wolf optimizer to solve complex optimization problems efficiently, this paper proposed a novel grey wolf optimizer based on Powell local optimization method, namely, PGWO
The proposed algorithm is benchmarked on seven well-known test functions, and the results are comparative study with guided Gravitational Search Algorithm (GGSA), Cuckoo search (CS), artificial bee colony (ABC), particle swarm optimization (PSO), and GWO

Summary

Introduction

Swarm Intelligence (SI) has received much attention. Many SI algorithms have been proposed, such as genetic algorithm (GA) [1], particle swarm optimization (PSO) [2], Differential Evolution (DE) [3, 4], and evolutionary programming (EP) [5]. In order to overcome the shortcomings of GWO algorithm including slow convergence speed, falling into local optimum value, low computation accuracy, and low success rate of convergence [19, 20], GWO algorithm based on Powell local optimization method is proposed. It adopts the powerful local optimization ability of Powell’s method and embeds it into GWO as a local search operator. In this case, the proposed method has potential to provide superior results compared to other state-of-the-art evolutionary algorithms. Various benchmark functions are employed to investigate the efficiency of PGWO algorithm (see Algorithm 3)

Experimental Results and Discussion

F2 F3 F4 F5 F6 F7

Data Clustering

Conclusion and Future Works