A Novel Improved Bat Algorithm in UAV Path Planning

Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.

Comparison of Wide-bandgap Devices in 1 kV,3 kW LLC Converters

Emerging wide-bandgap(WBG)devices,such as silicon carbide(SiC)MOSFETs and gallium nitride(GaN)high-electron-mobility transistors(HEMTs)provide new opportunities to realize high efficiency,high power density,and high reliability in several kHz,1 kV input,and several kW output applications.However,the performance comparison between SiC MOSFETs and GaN HEMTs in high-voltage,high-frequency,medium-high-power DC conversion applications have not yet been investigated thoroughly.Two 1 kV,3 kW LLC prototypes with GaN and SiC devices are built to perform a careful comparison of the prototypes in terms of parameters,power density,zero voltage switch realization,and overall efficiency.This provides guidance for the appropriate evaluation of WBG devices in high-voltage,high-frequency,and medium-high-power applications.

Multi-objective vehicle rebalancing for ridehailing system using a reinforcement learning approach

The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric(unbalanced)demand is considered here.We pose the rebalancing problem within a semi Markov decision problem(SMDP)framework with closed queues of vehicles serving stationary,but asymmetric demand,over a large city with multiple stations(representing neighborhoods).We assume that the passengers queue up at every station until they are matched with a vehicle.The goal of the SMDP is to minimize a convex combination of the waiting time of the passengers and the total empty vehicle miles traveled.The resulting SMDP appears to be difficult to solve yielding closed-form expression for the optimal rebalancing strategy.Consequently,we use a deep reinforcement learning algorithm to determine the approximately optimal solution to the SMDP.We show through extensive Monte Carlo simulations that the trained policy outperforms other well-known state-dependent rebalancing strategies.