Abstract:
The global optimization problem has been widely used in various fields, but the traditional method mainly relies on the gradient information of the objective function. The meta heuristic search algorithm has better flexibility and can be used in practical problem. Hence, aiming at the global continuous optimization problem, this paper proposes a continuous crystal energy optimization algorithm based on reinforcement learning and angular penalty distance (APD-CEO), which introduces the probabilistic update strategy based on reinforcement learning and the deviation strategy based on angle penalty distance. Firstly, the ice crystal continuous optimization algorithm is proposed to solve the continuous extremum problem by simulating the freezing process of lake water. Secondly, in order to eliminate the error in calculating the energy from the temporary center of the lake, the Angle penalty distance strategy is introduced to better balance the convergence and diversity. Meanwhile, the probabilistic update strategy based on reinforcement learning can better guide the position of the newly formed crystals, accelerate the freezing process of the lake, and approach the center of lake faster (the global optimum). Finally, in order to verify the validity of probabilistic update strategy and angular penalty distance strategy, these algorithms with and without joining these strategies are compared. It is shown that APD-CEO has better performance than other algorithms in most benchmark functions, and the contrast effect is more obvious in the high dimensions. Moreover, Friedman test also shows that the APD-CEO ranks the best one among five algorithms.