An improved differential evolution algorithm based on reinforcement learning and its application

As a typical swarm intelligence optimization method, the Differential Evolution (DE) algorithm exhibits excellent performance in solving high-dimensional complex problems; however, its parameter sensitivity and premature convergence issues still restrict its practical application effectiveness. Therefore, this paper proposes an improved Differential Evolution algorithm based on reinforcement learning, namely RLDE. First, it adopts the Halton sequence to realize the uniform initialization of the population space, which effectively improves the ergodicity of the initial solution set. Second, it establishes a dynamic parameter adjustment mechanism based on the policy gradient network, and realizes the online adaptive optimization of the scaling factor and crossover probability through the reinforcement learning framework. Furthermore, it classifies the population according to individual fitness values and implements a differentiated mutation strategy. To verify the effectiveness of the proposed algorithm, 26 standard test functions are used for optimization testing, and comparisons are conducted with multiple heuristic optimization algorithms in 10, 30, and 50 dimensions respectively. Experimental results demonstrate that the proposed algorithm significantly enhances the global optimization performance. Furthermore, by modeling and solving the Unmanned Aerial Vehicle (UAV) task assignment problem, the engineering practical value of the algorithm in real-world scenarios is verified from various indicators.