Papers by Keyword: ACO

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Authors: S. Sudhagar, V. Srinivasa Raman
Abstract: Gears are the most common of machine elements and due to that many studies have been conducted on optimum gear design. Gear optimization can be divided into two categories, namely, single gear pair or Gear train optimization. The problem of gear pairs design optimization is difficult to solve because it involves multiple objectives and large number of variables. Hence a trustworthy and resilient optimization technique will be more useful in obtaining an optimal solution for the problems. In the proposed work an effort has been made to optimize spur and helical gear pair design using LINGO and Meta heuristics algorithms like Real Coded Genetic Algorithm (RCGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).On applying the combined objective function factors like Power, Efficiency is maximized and the overall Weight, Centre distance has been minimized in the model. The performance of the proposed algorithms is validated through test problems and the comparative results are reported.
Authors: Nan Jiang, Yuan Zhi He, Lei Guo
Abstract: The architecture of distributed satellite cluster network (DSCN) is presented and the characteristics of DSCN topology change are illustrated. On the basis of analyzing the acquisition method of network status and route calculation, we proposed a heuristic algorithm Ant Colony Optimization (ACO) based traffic classified routing (ATCR) algorithm for DSCN. Simulation results shows that, ATCR algorithm can balance network traffic effectively, and the end-to-end delay of every traffic class is less than TCD algorithm. The end-to-end delay of traffic class A and class B is less than ACO algorithm which does not use traffic classification. ATCR has a better performance on packet delivery ratio than ACO and TCD because ATCR reduces the number of heavy load link as well as packet loss caused by congestion.
Authors: Xu Feng Hua, Yun Chen Tian, Cheng Xun Chen, Ke Zhi Xing
Abstract: Intensive Aquaculture Wireless Sensor Network (WSN) for water quality monitoring has specific requirements such as limited energy availability, low memory. Ant Colony Optimization (ACO) routing algorithm can maximize the lifetime of the network. A modified routing algorithm for Intensive Aquaculture WSN based on the ACO meta-heuristic is presented in this paper. Functions of basic ACO-based algorithm are modified to update the pheromone trail. The performance of Intensive Aquaculture WSN is improved on reducing memory used in monitor nodes and energy spent with communications. The modified routing algorithm was simulated for Intensive Aquaculture WSN scenarios and the results show that it maximizes energy savings.
Authors: Zheng He, Fan Wei, Xiao Hong Huang, Yan Ma
Abstract: Overlay networks have emerged as a promising paradigm for providing customizable and reliable services at the application layer, such as fault-resilient routing, multicast, and content delivery. Among the overlay network architecture, overlay routing is an important aspect of the overlay network design. In this paper, we develop a one-hop source routing, called heuristic-K algorithm using Ant Colony Optimization, to allow individual nodes to optimize route selection based on specific metrics like delay, loss rate, or throughput. Moreover, due to the selfish operating manner of overlay routing, we also take the traffic engineering element into consideration in the design process of our proposal. The experimental results demonstrate the effectiveness of the routing algorithm.
Authors: Tsai Duan Lin, Chiun Chieh Hsu, Li Fu Hsu
Abstract: The on-line Class Constrained Bin Packing problem (CCBP) is one of variant version of the Bin Packing Problem (BPP). The BPP is to find the minimum numbers of bins needed to pack a given set of items of known sizes so that they do not exceed the capacity B of each bin. In the CCBP, we are given bins of capacity B with C compartments and n items of Q different classes, each item i is belong to 1,2,…,n with class qi and si. The CCBP is to pack the items into bins, where each bin contains at most Q different classes and has total items size at most B. This CCBP is known to be NP-hard combinatorial optimization problems. In this paper, we used an ant colony optimization (ACO) approach with a simple but very effective local search algorithm to resolve this NP-hard problem. After the experimental design, limited computational results show the efficiency of this scheme. It is also shown that the ACO approach can outperform some existing methods, whereas the hybrid approach can compete with the known solution methods.
Authors: Tie Liu Wang, Xian Ming Chen, Shui Bin Chen
Abstract: For predicting the tool life combine the ant colony optimization(ACO) with the back propagation (BP) neural networks, use the the ACO to train BP neural network, build the prediction model based ACO-BP neural network. Some disadvantages are overcame in the BP algorithm, such as the low convergence speed, easily falling into local minimum point and weak global search capablity in the prediction process. Satisfies the requirement of global search capability and the robustness of the model. The experiment results show the prediction model has high precision in predicting the tool life. By the prediction model can provide a reasonable basis for planing production schedule and cutting tool requirement, calculating the cost, selecting the machining parameters,etc.
Authors: Yong Jun Feng, Xu Zhang, Xiao Hua Liu
Abstract: In order to improve the efficiency of enterprise logistics scheduling and save transport costs, the paper uses ACO to complete the path planning of enterprise logistics scheduling distribution, the paper first illustrates the present situation of the uneven geographical distribution of our country enterprise logistics resource and the different urban demands, and then states in detail the basic principle of ACO, finally carries out the enterprise logistics scheduling example simulation, the experimental results show that ACO shows a fast convergence, a high efficiency, and which can conveniently realize the path planning for the optimal solution, the experiment proves that the ACO has certain advantages of applying into enterprise logistics scheduling distribution, and which has a high research value.
Authors: Quan Gan, Qiong Qiong Sun
Abstract: Wireless multimedia sensor network routing protocols based on ant colony optimization algorithm (ACOQoS) is proposed. Based on the existing routing protocol design concept, and the actual needs of QoS from each network layer of wireless multimedia sensor networks, the protocols use the Agent collection method to select cluster head node, from the perspective of the balance network load. And maximize the network lifetime by balanced use of energy of the whole network.
Authors: Jia Hai Wang, Rui Heng Xiao, Yun Lei Ma
Abstract: The path planning is one core aspect of the research of robotics. In order to solve the problem that the welding robot path planning in the production line of BIW is inefficient, this paper analyzes the problem of the welding robot path planning and abstracts it into the TSP model. Finally, the optimal welding path is calculated using Ant Colony Optimization (ACO) based on the MATLAB7.0 environment. The result shows that this algorithm is an accurate and effective tool for welding path planning.
Authors: Yi Qiang Wang, Chao Fu, Ming Yang Ma, Lin Bin Wang
Abstract: In order to improve the operation efficiency of order picking trucks in the warehouse, the mathematical model of the routing optimization problem is established. Then PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) are used to solve the model. Experimental results show that both of them have good overall search ability and astringency. The operation efficiency is improved to a great extent by using swarm intelligent algorithms.
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