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With the increasing demand for electrical services, wind farm layout optimization has been one of the biggest challenges that we have to deal with. Despite the promising performance of the heuristic algorithm on the route network design problem, the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored. In this paper, the wind farm layout optimization problem is defined. Then, a multi-objective algorithm based on Graph Neural Network (GNN) and Variable Neighborhood Search (VNS) algorithm is proposed. GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved. The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives. The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy. The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling (PCC) over the current state-of-the-art algorithm, which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1% at the same cost. The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization