In this paper, we propose a new method for solving the Loney's solenoid benchmark problem based on stemcellsalgorithm (SCA). The key idea of the algorithm is using a modified version of our proposed stemcells o...
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In this paper, we propose a new method for solving the Loney's solenoid benchmark problem based on stemcellsalgorithm (SCA). The key idea of the algorithm is using a modified version of our proposed stemcells optimization method that can efficiently achieve the best solution for the position and size of two correcting coils in the famous Loney's solenoid problem. The results show the superiority of the proposed method on other previously proposed methods for electromagnetic optimization.
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 Sw...
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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 stemcellsalgorithm (SCA) is an optimization algorithm inspired by the natural behavior of stemcells 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).
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