Abstract
In this paper, a new dynamical evolutionary algorithm is presented based on a particle transportation theory according to the principle of energy minimization and the law of entropy increasing in the phase space of particles. In numerical experiments we use this algorithm to solve optimization problems, which are difficult to solve using traditional evolutionary algorithms (e.g., the minimization problem of six-hump camel back functions). Compared with the traditional evolutionary algorithms, this new algorithm not only solves linear and nonlinear optimization problems more quickly, but also more easily finds all the points that reach the global solutions of these problems because it drives almost all the individuals to have chances to participate in crossing and mutating. The results of numerical experiments show that this dynamical evolutionary algorithm obviously improves the computing performance of the traditional evolutionary algorithms in that its convergent speed is faster and it is more reliable.
This work is partly supported by the National Natural Science Key Foundation of China with the Grant No.60133010 and the National Research Foundation for the Doctoral Program of Higher Education of China with the Grant No.20030486049.
This work is partly supported by the National Natural Science Key Foundation of China with the Grant No.60133010 and the National Research Foundation for the Doctoral Program of Higher Education of China with the Grant No.20030486049.
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Li, K., Li, Y., Chen, Z., Wu, Z. (2005). A New Dynamical Evolutionary Algorithm Based on Particle Transportation Theory. In: Zhang, W., Tong, W., Chen, Z., Glowinski, R. (eds) Current Trends in High Performance Computing and Its Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27912-1_8
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DOI: https://doi.org/10.1007/3-540-27912-1_8
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