Multi-objective design optimization based on evolutionary algorithms

The scheduling of inbound aircraft includes determining the landing runway, sequencing, and landing time, which is an NP -C combinatorial optimization problem. When the number of targets, runways, and aircraft increases, the complexity of the problem increases sharply. A scientific and reasonable parking allocation plan or optimization of existing plans can effectively impr ove the operational efficiency of the airport and reduce its operating costs. In order to achieve intelligent airp ort parking space allocation, this article establishes a parking space allocation model based on the weighted fraction matrix of flight parking spaces based on the relevant concepts and constraints of parking space allocation. Then, this article improves the mutation strategy of the classic differential evolution (DE) algorithm by adding a scaling factor K, while allowing K and the crossover probabilit y factor (CR) to adaptively change with the change of iteration number, which is conducive to better optimizing the multi-objective allocation of airport parking spaces. Experiments have shown that the approach converges faster in standard test functions and simulation tests for parking allocation, and it can search for better solutions than the original algorithm, confirming that the performance of the improved algorithm has been improved. Finally, thi s study compared the DE algorithm before and after improvement using standard test functions and simulation data of parking space allocation. The results show that the improved differential evolution algorithm has improved various performance indicators, verifying the effectiveness of the proposed algorithm.