As a new network addressing and routing scheme, anycast has been defined as a standard communication model in IPv6 The multiple QoS constrained anycast routing problem is a nonlinear combination optimization problem, ...
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ISBN:
(纸本)9781424447541
As a new network addressing and routing scheme, anycast has been defined as a standard communication model in IPv6 The multiple QoS constrained anycast routing problem is a nonlinear combination optimization problem, which is proved to be a NP complete problem This paper studies anycast routing technology with multiple QoS constraints and proposes a multiple QoS anycast routing a*algorithm based adaptive genetic a*algorithm This a*algorithm uses adaptive probabilities of crossover and mutation over and over again in simple genetic a*algorithm Fitness scaling can guarantee the diversity of populations, which is beneficial to find global optimal solution Simulation results show the efficiency of our a*algorithm It can satisfy the constrained condition of multiple QoS, balance network load fairly, and improve the quality of network service
Adaptive a*algorithm is widely applied in the filed of digital signal processing. LMS adaptive a*algorithm is applied in the location system of an autonomous robot to increase the accuracy of the location system according...
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ISBN:
(纸本)9781424421138
Adaptive a*algorithm is widely applied in the filed of digital signal processing. LMS adaptive a*algorithm is applied in the location system of an autonomous robot to increase the accuracy of the location system according to the analysis of the adaptive filter. The autonomous robot is used to dig the hole in the mud underwater along the prearranged trajectory in wreck salvage. The working condition of the robot in the mud is terrible. The location system is designed. Based on the location method, the location errors of the autonomous robot are analyzed. The LMS adaptive a*algorithm of location system is simulated in the computer. The simulation results show that LMS adaptive a*algorithm can decrease the errors of location system.
Unmanned Aerial Vehicles (UAVs), a new emerging form of Internet of Things (IoT), is a promising technology to be widely used in both civil and military applications. On the fly, the UAVs need to find an efficient and...
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ISBN:
(纸本)9781728189642
Unmanned Aerial Vehicles (UAVs), a new emerging form of Internet of Things (IoT), is a promising technology to be widely used in both civil and military applications. On the fly, the UAVs need to find an efficient and safe path by avoiding both static and dynamic obstacles to carry out any mission successfully. The Artificial Potential Field (APF) a*algorithm is one of the conventional catalysts in UAV path planning. However, APF-aided UAVs can be easily trapped into a local minimum solution before reaching the destination. Therefore, this paper proposes an efficient APF a*algorithm for Collision-free Path Planning (eAPF-CPP) in UAVs. In eAPF-CPP, the attractive and repulsive potentials evaluate the quadratic distance to the destination and the obstacle respectively. The evaluation aids the UAV to select the optimal path in navigation. The eAPF-CPP mechanism is simulated in the Software-In-The-Loop (SITL) setup, and the experimental results show that the eAPF-CPP mechanism utilizes an average of 24.4 seconds to track a safe path and has a lower collision rate of 8.56% compared with Artifical Potential Field Approach (APFA).
Multicast routing is to find the paths from a service node re ail multicast destinations. In this paper;based on mrt a*algorithm;we propose a distributed multicast routing scheme with delay-bounded and load-balancing tr...
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ISBN:
(纸本)0769509126
Multicast routing is to find the paths from a service node re ail multicast destinations. In this paper;based on mrt a*algorithm;we propose a distributed multicast routing scheme with delay-bounded and load-balancing traffic in real-time communications. We first describe ant a*algorithm model and give ant-network model, then present art approach using ant a*algorithm to optimize the multicast routes with delay-bounded and load-balancing traffic. Finally simulation has been done to show the efficiency of the a*algorithm in the environment of OPNET simulation software, and the simulation results show that the proposed approach can find the best optimal multicast routes which can satisfy the delay-bounded requirement avoid to congested nodes.
In order to enhance the performance of Chinese question answering (QA) system, this paper presented a model for Chinese question answering system based on multi-agent. Optimal coalition was formatted by using improved...
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ISBN:
(纸本)9780769539485
In order to enhance the performance of Chinese question answering (QA) system, this paper presented a model for Chinese question answering system based on multi-agent. Optimal coalition was formatted by using improved ant colony a*algorithm. This paper described the problem, introduced solving steps based on ant colony a*algorithm (ACA), and improved ACA considering the characteristics of Chinese QA. The capability of agents and users' satisfaction were best shown in the process of optimization. The experiment demonstrated that the improved a*algorithm could avoid falling into local optimums,accelerate the convergence rate and improve the ability of searching an optimum solution.
In the Multicast k-Tree Routing Problem, a data copy is sent from the source node to at most k destination nodes in every transmission. The goal is to minimize the total cost of sending data to all destination nodes, ...
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ISBN:
(纸本)9783642020254
In the Multicast k-Tree Routing Problem, a data copy is sent from the source node to at most k destination nodes in every transmission. The goal is to minimize the total cost of sending data to all destination nodes, which is measured as the sum of the costs of all routing trees. This problem was formulated Out of optical networking and has applications in general multicasting. Several approximation a*algorithms, with increasing performance, have been proposed in the last several years;The most recent ones are heavily relied on a tree partitioning technique. In this paper, we present a further improved approximation a*algorithm along the line. The a*algorithm has a worst case performance ratio of 5/4 rho + 3/2, where p denotes the best approximation ratio for the Steiner Minimum Tree problem. The proofs of the technical routing lemmas also provide some insights on why such a performance ratio could be the best possible that one can get using this tree partitioning technique.
Aiming at the characteristics of multi-constraints and multi-optimization objectives in manipulator truss assembly tasks, a truss assembly sequence planning method based on ant colony a*algorithm was proposed. Firstly, ...
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ISBN:
(纸本)9798350360875;9798350360868
Aiming at the characteristics of multi-constraints and multi-optimization objectives in manipulator truss assembly tasks, a truss assembly sequence planning method based on ant colony a*algorithm was proposed. Firstly, the mathematical representation model of the manipulator truss assembly task is established to describe the truss properties and the task status. Secondly, a comprehensive evaluation index of assembly sequence considering assembly stability and energy consumption of the manipulator was established. Then, the search strategy of truss assembly sequence based on ant colony a*algorithm is designed. Finally, the feasibility and superiority of the proposed method are proved by simulation and comparison.
This paper proposes a new allocation a*algorithm of indivisible goods. We consider the case when the total value of the whole goods is the same for every participant, which models allocation at divorce or inheritance. T...
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ISBN:
(纸本)9784885522987
This paper proposes a new allocation a*algorithm of indivisible goods. We consider the case when the total value of the whole goods is the same for every participant, which models allocation at divorce or inheritance. The worst participant's obtained value must be maximized. There are not good allocation a*algorithms for our rating scale. We show that this problem is NP-complete. Therefore we propose four types of approximation a*algorithms. Among the four a*algorithms, the raising standard a*algorithm has the best ratio that the a*algorithm outputs the optimal solution by a computer simulation.
In this article we propose an original approach that allows the decoding of Automatic Speech Recognition Graphs by using a constructive a*algorithm based on ant colonies. In classical approaches, when a graph is decoded...
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ISBN:
(纸本)9781510817906
In this article we propose an original approach that allows the decoding of Automatic Speech Recognition Graphs by using a constructive a*algorithm based on ant colonies. In classical approaches, when a graph is decoded with higher order language models;the a*algorithm must expand the graph in order to develop each new observed n-gram. This extension process increases the computation time and memory consumption. We propose to use an ant colony a*algorithm in order to explore ASR graphs with a new language model, without the necessity of expanding it. We first present results based on the TED English corpus where 2-grams graph are decoded with a 4-grams language model. Then, we show that our approach performs better than a conventional Viterbi a*algorithm when computing time is constrained and allows a highly threaded decoding process with a single graph and a strict control of computation time and memory consumption.
The ABC a*algorithm is a new meta-heuristic approach, having the advantages of memory, multi-characters, local search, and a solution improvement mechanism. It can be used to identify a high quality optimal solution and...
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ISBN:
(纸本)9781467348041;9781467348065
The ABC a*algorithm is a new meta-heuristic approach, having the advantages of memory, multi-characters, local search, and a solution improvement mechanism. It can be used to identify a high quality optimal solution and offer a balance between complexity and performance, thus optimizing forecasting effectiveness. This paper proposes an efficient prediction model for forecasting of short and long range stock market prices of two well know stock indices, S&P 500 and DJIA using a simple adaptive linear combiner (ALC), whose weights are trained using artificial bee colony (ABC) a*algorithm. The Model is simulated in terms of mean square error (MSE) and extensive simulation study reveals that the performance of the proposed model with the test input patterns is more efficient, accurate than the PSO and GA based trained models.
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