TY - JOUR
T1 - An efficient algorithm for function optimization
T2 - Modified stem cells algorithm
AU - Taherdangkoo, Mohammad
AU - Paziresh, Mahsa
AU - Yazdi, Mehran
AU - Bagheri, Mohammad Hadi
N1 - Publisher Copyright:
© Versita sp. z o.o.
PY - 2013/3
Y1 - 2013/3
N2 - In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).
AB - In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).
KW - Ant colony optimization
KW - Artificial bee colony algorithm
KW - Genetic algorithm
KW - Modified stem cells algorithm
KW - Optimization algorithm
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=84876949188&partnerID=8YFLogxK
U2 - 10.2478/s13531-012-0047-8
DO - 10.2478/s13531-012-0047-8
M3 - Article
SN - 1896-1541
VL - 3
SP - 36
EP - 50
JO - Central European Journal of Engineering
JF - Central European Journal of Engineering
IS - 1
ER -