Abstract:In this paper we have proposed parallel implementation of Ant colony optimization Ant System algorithm on GPU using OpenCL. We have done comparison on different parameters of the ACO which directly or indirectly affect the result. Parallel comparison of speedup between CPU and GPU implementation is done with a speed up of 3.11x in CPU and 7.21x in GPU. The control parameters α, β, ρ is done with a result of best solution at 1, 5 and 0.5 respectively.
A novel efficient downstairs trajectory is proposed for a 9 link biped robot model with toe-foot. Trajectory of hip joint plays a significant role towards stability of the robot reason being the center of mass which lies approximately close to the hip position. Brachistochrone is the fastest descent trajectory for a particle moving only under the influence of gravity. In case of downstairs movement of robot, methodology for brachistochrone and circular arc trajectories for hip joint is developed. Here, an adaptive trajectory planning algorithm is implemented along with unsupervised artificial neural network (UANN) based inverse kinematics solution, so that biped robots of varying link lengths, masses can climb down on varying staircase dimensions. Zero Moment Point (ZMP) based COG trajectory is considered and its stability is ensured. Cycloidal trajectory is considered for ankle of the swing leg. Parameters of both cycloid and brachistochrone depends on dimensions of staircase steps. Hence this paper can be broadly divided into 4 steps 1) Developing ZMP based brachistochrone and circular arc trajectory for hip 2) Cycloidal trajectory planning for ankle by taking proper collision constraints 3) Solving Inverse kinematics using UANN 4) Comparison between both proposed hip trajectories. The proposed algorithms have been implemented using MATLAB®.
In this paper, we have proposed an approach to implement Ant colony optimization algorithm especially Max-Min Ant System for solving Travelling Salesman problem on GPU. GPUs are specially designed microprocessor for graphical operation and can be used for general purpose operations. ACO is a nature based inspired algorithm based on heuristics to find the solution for combinatorial optimization problems such as TSP. In this paper we have discussed many different programming issues of GPUs using OpenCL such synchronized memory access and barriers. We have used a partial solution for the stochastic probability function used in ACO for the tour construction to increase the speed-up. Thus with this implementation we are able to gain a speedup of 4.01x in CPU parallel and up to 11.29x speedup in GPU parallel.
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