Because the Particle Swarm Optimization (PSO) algorithm is easily trapped into local optimal solutions when solving the bi-level programming problems, an Improved hybrid Cuckoo Search-based Quantum-behaved Particle Swarm Optimization (ICSQPSO) algorithm based on Simulated Annealing (SA) Metropolis criterion was proposed. Firstly, the Metropolis criterion of SA algorithm was introduced into the hybrid algorithm to enhance the global optimization ability by accepting good solutions as well as bad solutions with a probability during solving process. Secondly, a Lévy flight with dynamic step size was designed for cuckoo search algorithm in order to maintain the high diversity of particle swarm during optimization, so as to guarantee search range. Finally, the preference random walk mechanism in the cuckoo algorithm was used to help the particles jump out of local optimal solutions. The numerical results of 13 bi-level programming cases including nonlinear ones, fractional ones, and those with multiple lower levels show that the objective functions optimal values of 12 cases obtained by ICSQPSO algorithm are significantly better than those of the algorithms for comparison in literatures, only the result of 1 case is slightly worse, and the results of half of the 13 cases are 50% better than those of the algorithms to be compared. Therefore, the ICSQPSO algorithm is superior to the algorithms to be compared on the optimization ability for bi-level programming.

Due to the deficiency of ubiquitous charging stations (or stakes) and short driving distances of Electric Vehicle (EV), many people are hesitant to use EV. To reduce users' anxiety about limited battery capacity and lower fees due to frequent charging and making detour to charge, a matching theoretic Traveling Plan-aware Charging Scheduling (TPCS) scheme was proposed. Firstly, preference lists of EV users and charging stations were constructed respectively according to traveling plans of EV and their electricity demand at each charging station. Secondly, a many-to-one matching model was established between EV users and charging stations. Finally, interfaces of charging stations were allocated to optimize the system total utility. Compared with the Random Charging Scheduling (RCS) algorithm and Only utility of Electric Vehicle concerned Scheduling (OEVS) algorithm, the system total utility of TPCS was increased at most by 39.3% and 5% respectively. In addition, TPCS guaranteed the satisfactory ratio of EV users to be above 90% when charging demand of EV users was light, which is higher than that of RCS. The proposed algorithm can effectively improve the system total utility and satisfactory ratio of EV users, and reduce the computational complexity.