Abstract
Magnetic Optimization Algorithm (MOA) is a recently novel optimization algorithm inspired by the principles of magnetic field theory whose possible solutions are magnetic particles scattered in the search space. In order improve the performance of MOA, a Functional Size population MOA (FSMOA) is proposed here. To find the best function for the size of the population, several functions for MOA are considered and investigated and the best parameters for the functions will be derived. In order to test the proposed algorithm and operators, the proposed algorithm will be compared with GA, PSO, QEA and saw-tooth GA on 14 numerical benchmark functions. Experimental results show that the proposed algorithm consistently has a better performance than those of other algorithms in most benchmark function.
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