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As for the tme-efficient A* algorithm significantly reduces the algorithm time, lead to the problem of increase path planning length and path sawtooth. This paper proposed an improved bidirectional time-efficient A* algorithm, which ran the A*algorithm from the start and the end point to find the path and applied the multiple neighbor grid distance computation scheme. At the same time, according to different environment map, this paper compared and analyzed the A* algorithm, time-efficient A*algorithm and bidirectional time-efficient A*algorithm. Finally, it chose the index of algorithm time and path length as two indicators to evaluate the merits and demerits of the algorithm. Experimental results show that compared with traditional A*algorithm, the bidirectional time-efficient A*decreases the time of the algorithm by 76. 8% at the maximum, and reduces the maximum time by 55. 4% compared with time-efficient A*algorithm. It also solves the problem that the path distance of the time-efficient A*algorithm is augmented and the path is not smooth enough. [ABSTRACT FROM AUTHOR]

Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS). As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively. [ABSTRACT FROM AUTHOR]

This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent's fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA). [ABSTRACT FROM AUTHOR]