TY  - JOUR
T1  - A New Engineering Optimization Method: African Wild Dog Algorithm
AU - Subramanian, C. AU - , A.S.S. Sekar AU - , K. Subramanian 
JO  - International Journal of Soft Computing
VL  - 8
IS  - 3
SP  - 163
EP  - 170
PY  - 2013
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2013.163.170
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2013.163.170
KW  - Meta-heuristic optimization
KW  -African wild dog algorithm
KW  -engineering optimization
KW  -memory
KW  -parameters
AB  - 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.
ER  -