This article proposes a robust optimization algorithm for wind power bidding in electricity markets under the influence of wind speed uncertainty. An artificial neural network is integrated to enhance the optimization speed of the meta-heuristic algorithm. The accurate prediction capability of the long short-term memory (LSTM) algorithm is blended into the movement process of individuals in the particle swarm optimization (PSO) algorithm. This hybrid LSTM-PSO algorithm is experimented on the IEEE 30-bus standard power system, and results are compared with the original PSO algorithm and Mixed-Integer Linear Programming algorithm. Furthermore, the experiments yield insights for wind power stakeholders to participate in bidding on electricity markets most effectively despite the uncertain wind energy production risks in real-time.