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 optimiz ation 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 algo rithm indicates that it can quickly converge to optimal solutions of the optimiz ation problems. Hence this algorithm is more reliable and stable than traditional evolutionary algorithms.