A Particle Gradient Evolutionary Algorithm Based on Statistical Mechanics and Convergence Analysis
Kangshun Li (Jiangxi University of Science and Technology)Wei Li (Jiangxi University of Science and Technology)
Zhangxin Chen (Southern Methodist University)
Zhijian Wu (Wuhan University)
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:
Parallel and Distributed Computing
