International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
This study introduces a new parameter free meta-heuristic optimization algorithm, African Wild Dog Algorithm (AWDA) to solve engineering optimization problems. Meta-heuristic algorithms imitate natural phenomena, e.g., physical annealing in simulated annealing, human memory in a tabu search and evolution in evolutionary algorithms. AWDA mimics the communal hunting behavior of African wild dogs. As the currently available metaheuristic optimization algorithms require a set of algorithmic parameters to be tuned to yield optimal performance, AWDA does not require any parameter except pack size and termination criterion. The AWDA, code was tested in several benchmark engineering optimization problems taken from literature. The optimization results indicate that AWDA may yield better solutions than other Meta-heuristic algorithms.
How to cite this article:
C. Subramanian, A.S.S. Sekar and K. Subramanian . A New Engineering Optimization Method: African Wild Dog Algorithm.
DOI: https://doi.org/10.36478/ijscomp.2013.163.170
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2013.163.170