Self adaptive cluster based and weed inspired differential evolution algorithm for real world optimization

Author

Haider, Udit ; Das, Swagatam ; Maity, Dipankar ; Abraham, Ajith ; Dasgupta, Preetam

Author_Institution

Dept. of Electron. & Telecommun. Eng., Jadavpur Univ., Kolkata, India

fYear

2011

fDate

5-8 June 2011

Firstpage

750

Lastpage

756

Abstract

In this paper we propose a Self Adaptive Cluster based and Weed Inspired Differential Evolution algorithm (SACWIDE), the total population is divided into several clusters based on the positions of the individuals and the cluster number is dynamically changed by the suitable learning strategy during evolution. Here we incorporate a modified version of the Invasive Weed Optimization (IWO) algorithm as a local search technique. The algorithm strategically determines whether a particular cluster will perform Differential Evolution (DE) or the IWO algorithm (modified). The number of clusters in a particular iteration is set by the algorithm itself self-adaptively. The performance of SACWIDE is reported on the set of 22 benchmark problems of CEC-2011.

Keywords

evolutionary computation; iterative methods; learning (artificial intelligence); pattern clustering; search problems; invasive weed optimization algorithm; iteration method; learning strategy; local search technique; real world optimization; self adaptive cluster based differential evolution algorithm; weed inspired differential evolution algorithm; Algorithm design and analysis; Clustering algorithms; Convergence; Heuristic algorithms; Iron; Optimization; Performance evaluation; Differential Evolution; Evolutionary Algorithm; real world optimization; self-adaptive optimization algorithm; weed colony optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2011 IEEE Congress on

Conference_Location

New Orleans, LA

ISSN

Pending

Print_ISBN

978-1-4244-7834-7

Type

conf

DOI

10.1109/CEC.2011.5949694

Filename

5949694