Title :
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
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;
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-7834-7
DOI :
10.1109/CEC.2011.5949694
