Abstract
In this paper a particle gradient evolutionary algorithm is presented for solving complex single-objective optimization problems based on statistical mechanics theory, the principle of gradient descending, and the law of evolving chance ascending of particles. Numerical experiments show that we can easily solve complex single-objective optimization problems that are difficult to solve by using traditional evolutionary algorithms and avoid the premature phenomenon of these problems. In addition, a convergence analysis of the algorithm indicates that it can quickly converge to optimal solutions of the optimization problems. Hence this algorithm is more reliable and stable than traditional evolutionary algorithms.
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Li, K., Li, W., Chen, Z., Wang, F. (2007). A Particle Gradient Evolutionary Algorithm Based on Statistical Mechanics and Convergence Analysis. In: Daydé, M., Palma, J.M.L.M., Coutinho, Á.L.G.A., Pacitti, E., Lopes, J.C. (eds) High Performance Computing for Computational Science - VECPAR 2006. VECPAR 2006. Lecture Notes in Computer Science, vol 4395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71351-7_41
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DOI: https://doi.org/10.1007/978-3-540-71351-7_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71350-0
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