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A Modified PSO Algorithm for Numerical Optimization Problems |
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PP: 1229-1234 |
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Author(s) |
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Hsin-Chuan Kuo,
Jeun-Len Wu,
Ching-Hai Lin,
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Abstract |
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| By successively employing the interval search method, we developed the proposed algorithm MPSO, introducing three
creative position vectors to replace the three worst fitness particles among the population in the PSO, to overcome the premature
convergence situation that occurs when a problem with a large number of variables and (or) multiple optima is solved.
The results obtained by applying the MPSO and the PSO on 6 benchmark functions show that, except for the randomly shifted
Rosenbrock functions, the MPSO can successfully secure a solution that is close to the exact solution for each of the remaining
five functions.We also showed that all benchmark functions are solvable by the MPSO if the maximum number of generations is raised
to be as high as possible. With regard to the PSO′s performance for the three different numbers of variables, it fails to obtain a solution
that is close to the exact solution for all of the tested functions except for the Sphere function with 30 variables. |
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