Using Artificial Bee Colony Algorithm to Determine the Optimum Stratum Boundaries Depending on Neyman Allocation - Al-Kassab, Mowafaq Muhammed (Journal of Advance Research in Computer Science & Engineering (ISSN 2456-3552), 2016)
In stratified sampling, in order to gain more precision than other methods of sampling, a heterogeneous population is divided into subpopulations, each of which is internally homogeneous. As a result the main problem arising in stratified sampling is to obtain the optimum boundaries. Several numerical and computational methods have been developed for this purpose. Some apply to highly skewed populations and some apply to any kind of populations .This paper proposes an ABC algorithm to solve the problem of stratum boundary while distributing the sample size according to Neyman Allocation method. The ABC algorithm is tested on two groups of populations and a comparative study with Genetic Algorithm (GA) of Keskintürk and Er (2007), Kozak’s (2004), Lavallée and Hidiroglou’s (1988) and Dalenius and Hodges(1959) methods have been implemented. The numerical results show the ability of the proposed algorithm to find the optimal stratified boundaries for a set of standard populations and various standard test functions compared with other algorithms.