OCDE: Adaptive Differential Evolution Algorithm in Distributed Intelligent Systems

In autonomous driving, the optimization theory and algorithms for distributed intelligent systems are essential for enhancing vehicle decision-making capabilities, path planning, and environmental perception. Evolutionary algorithms, as a global optimization method inspired by biological evolution, is widely used in these scenarios. Unfortunately, the performance of evolutionary algorithms relies on the properties of the solution space, the optimization strategy, and parameter settings, and their efficiency also requires more rigorous evaluation. This study focuses on differential evolution algorithm, examining the properties of the solution space for optimization problems, the preferences of strategies and parameters in different optimization scenarios, and setting new standards for efficiency evaluation. Based on the experimental conclusions, modifications are made to our algorithm to further verify the correctness of our findings. All experimental results are deduced based on the CEC2017 benchmark problem suite from the IEEE Congress on Evolutionary Computation (CEC).