Vincent Roberge

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.

Strategies to Accelerate Harmonic Minimization in Multilevel Inverters Using a Parallel Genetic Algorithm on Graphical Processing Unit

Multilevel inverters form a popular class of high-power inverters due to their high-voltage operation, high efficiency, low switching losses, and low electromagnetic interference. Metaheuristics, such as the genetic algorithm (GA), have been used with success to compute optimal switching angles for multilevel inverters with many dc sources while minimizing several harmonics. However, these methods are computationally demanding and cannot easily be used for real-time control. In this letter, a parallel implementation of the GA on graphical processing unit (GPU) is proposed in order to accelerate the computation of the optimal switching angles for multilevel inverters with varying dc sources. Four approaches to parallelize and speed up the computation of the total harmonic distortion are presented and compared. By exploiting the massively parallel architecture of GPUs, the computation of optimal angles is accelerated by a factor of 469× compared to a sequential execution on CPU. The proposed solution optimizes multilevel inverters with 100 variable dc sources while minimizing the first 100 harmonics in 164 ms.

Efficient parallel Particle Swarm Optimizers on GPU for real-time harmonic minimization in multilevel inverters

In this paper, we present a parallel implementation of the Particle Swarm Optimization (PSO) on graphical processing units (GPU) using CUDA. By fully utilizing the processing power of graphic processors, we show how our parallel CUDA-PSO can be used to minimize harmonics in multi-level inverters. The computing power of the GPU coupled with the parallelism of our algorithm allows for real-time computation of optimal switching angles for multilevel inverters with several DC inputs while minimizing the 50th first harmonics. Compared to other solutions, our CUDA-PSO offers superior converging rate by running hundreds of independent swarms in parallel without increasing the computation time.

Parallel Power Flow on Graphics Processing Units for Concurrent Evaluation of Many Networks

The power flow (PF) analysis provides the steady state of the power system and is key to the simulation of transmission networks. It is a tool commonly used by system operators to visualize the effect of generator settings on the network prior to making a change. In situations involving large networks, hundreds or even thousands of PF analysis may have to be run on the network before finding the optimal power dispatch. This process requires significant computation time and does not allow for rapid control of the network. To address this problem, this paper presents two parallel PF solvers that exploit the massively parallel architecture of graphics processing units (GPU) in a hybrid GPU-central processing unit (CPU) computing environment using compute unified device architecture and OpenMP in order to significantly speedup the concurrent analysis of many instances of a network. Both implementations use sparse matrices, double precision operations, and enforce the reactive power limit of generators. The parallel Gauss-Seidel (G-S) and Newton-Raphson (N-R) PF algorithms are tested on networks ranging from 4 to 2383 buses. The accuracy is validated using MATPOWER and the maximum speedup achieved, compared with a sequential execution on CPU, is

COMPARISON OF PARALLEL PARTICLE SWARM OPTIMIZERS FOR GRAPHICAL PROCESSING UNITS AND MULTICORE PROCESSORS

45.2 \boldsymbol {\times }

Parallel Algorithm on Graphics Processing Unit for Harmonic Minimization in Multilevel Inverters

for G-S and

Distribution System Optimization on Graphics Processing Unit

17.8 \boldsymbol {\times }

Collaborative Parallel Hybrid Metaheuristics on Graphics Processing Unit

for N-R.

Parallel Particle Swarm Optimization on Graphical Processing Unit for Pose Estimation

In this paper, we present a parallel implementation of the particle swarm optimization (PSO) on graphical processing units (GPU) using CUDA. By fully utilizing the processing power of graphic processors, our implementation (CUDA-PSO) provides a speedup of 167× compared to a sequential implementation on CPU. This speedup is significantly superior to what has been reported in recent papers and is achieved by four optimizations we made to better adapt the parallel algorithm to the specific architecture of the NVIDIA GPU. However, because todays personal computers are usually equipped with a multicore CPU, it may be unfair to compare our CUDA implementation to a sequential one. For this reason, we implemented a parallel PSO for multicore CPUs using MPI (MPI-PSO) and compared its performance against our CUDA-PSO. The execution time of our CUDA-PSO remains 15.8× faster than our MPI-PSO which ran on a high-end 12-core workstation. Moreover, we show with statistical significance that the results obtained using our CUDA-PSO are of equal quality as the results obtained by the sequential PSO or the MPI-PSO. Finally, we use our parallel PSO for real-time harmonic minimization of multilevel power inverters with 20 DC sources while considering the first 100 harmonics and show that our CUDA-PSO is 294× faster than the sequential PSO and 32.5× faster than our parallel MPI-PSO.

Optimal power flow based on parallel metaheuristics for graphics processing units

This paper presents the implementation details of a parallel algorithm on graphics processing units (GPUs) to compute the optimal switching angles for the harmonic minimization in multilevel inverters with unequal dc voltage sources. Two algorithms, the Newton-Raphson method and the bisection method, and three different parallel implementations are investigated. Both algorithms considered have a low time complexity and offer a superior converging rate allowing for the real-time control of inverters with a very large number of levels. By exploiting the massively parallel architecture of GPUs, the execution time of the program is reduced significantly. The proposed parallel implementation offers a maximum speedup of 534× compared with a sequential execution on CPU, and allows for the calculation of the optimal switching angles for inverters with up to 1000 dc sources in less than 16.4 μs.