VECPAR 2006: High Performance Computing for Computational Science - VECPAR 2006 pp 530-543 | Cite as
A Particle Gradient Evolutionary Algorithm Based on Statistical Mechanics and Convergence Analysis
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.
Keywords
Evolutionary Algorithm Convergence Analysis Global Optimal Solution Jiangxi Province Helmholtz Free EnergyPreview
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