Papers by Keyword: ACO

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Authors: Yu Cheng Zhang, Xing Guo Qiu, Zhan Jun Hao
Abstract: Since the energy of wireless sensor nodes is limited, the protocols of wireless sensor network (WSN) we design should care about problems of balancing energy of nodes. After analyzing current algorithms, this paper puts forward an hierarchical and clustering strategy for routing based on Ant Colony Optimization (HC-ACO). The protocol divides the nodes into fixed clusters, each cluster selects a Cluster Header (CH) and switches the CH by its energy and other restrictive conditions. And it adopts ACO to search the optimal path between the CHs and Sink node. The simulation indicates the protocol can balance energy consumption of nodes of network.
Authors: Bashra Kadhim Oleiwi, Hubert Roth, Bahaa I. Kazem
Abstract: In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.
Authors: Tao Yan, Xian Min Lin, You Ping Zhong
Abstract: With regard to the slow-convergence disadvantage in the latter generations of GA, ACO is combined with GA in this paper to solve the optimal load allocation problem among thermal power plant units. By comparison with GA method, results show that GA-ACO has faster convergence than the GA method.
Authors: Yan Zhang, Yan Ping Cui, Wen Tao Yang
Abstract: Passenger and freight train scheduling problem on double-track railway line is considered by using Ant Colony Optimization (ACO) algorithm. The aim is to reasonably arrange the dispatch sequence of the trains to minimize the total run time. The constrains in train scheduling problem are considered and the model is established. Due to the complexity of train scheduling problem, this problem is solved by ACO and implemented by programming. A case study is presented to illustrate the solution. The results illustrate that the proposed method is effective to solve the scheduling problem on double-track railway line.
Authors: Li Huo, Bo Jiang, Tao Ning
Abstract: A new algorithm for TSP which is an improved ACO combined with MMAS and CSDT is proposed. MMAS can prevent the search from local optimum and search stagnation. We use candidate set strategy based on the Delaunay triangle (CSDT) in order to reduce serch space and accelerate the speed of the algorithm. Additionally, pheromone update and parameter optimization are detailed in this paper. The comparison analysis of the new algorithm, basic ant colony algorithm and MMAS algorithm is also given by using TSPLIB experimental data. Finally, we give an actual TSP case and compute the optimum solution by our new algorithm.The results show that the new algorithm is validity and effectively.
Authors: Xin Xin Zhou, Yan Zhao
Abstract: Wireless sensor networks (WSNs) is taking an increasing role in our lives. Because the energy of the sensors is limited how to efficiently use the energy to prolong the lifecycle of the sensor networks is very important. In this paper, a novel energy-balanced dynamic routing algorithm based on ACO is proposed. The novel routing algorithm can dynamically choose routing according to the residual energy of the sensors and the sensors with more power is taken more data transfer tasks. The simulation results show that the proposed routing algorithm can effectively balance energy consumption and prolong the lifecycle of the networks.
Authors: Zhong Qun Li, Xin Wang, Ya Feng Dong
Abstract: As a new-type, high efficient hole-making technology, helical milling is widely used in making holes on composites and composite-metal compounds materials in the aircraft industry. In this paper, the problem of machining path optimization using helical milling is converted to a TSP (Traveling Salesman Problem), a mathematical model is established and solved using ACO (Ant Colony Optimization). Simulation results show that with the application of ACO, the traveling efficiency of helical milling has been increased by 41.1%.
Authors: Peng Gao, Yue Jin Tan, Ju Fang Li, Ren Jie He
Abstract: The algorithms for solving the remote satellite scheduling problem are less effective usually in single computing environment. This paper designed a framework of ant colony algorithm for remote satellite and ground integration scheduling problem in the parallel environment, and given the detail of key steps in the algorithm. Experiments are show at the end of this paper to prove effective and validation.
Authors: Xiu Zeng, Qian Li Ma
Abstract: Factory layout is NP problem[1]. There are many methods to solve it ,such as engineering diagram, flow chart method, various heuristic algorithms, SA( simulated annealing) and GA(genetic algorithm) [2].ACO (ant colony optimization) is used to solve it in this paper. The logistics costs exist between two workshops that are treated as pheromone that guides ants to search the best solution. Smaller logistics cost is, stronger the two workshops of relation is. In the process of optimization theworkshop with low logistics cost is more likely to be chosen, which minimizes the system logistics cost. Compared with GA, ACO has the advantage in speed. The mean value of the solution, the best solution, the worst solution is better too. More the number of workshop is, more obvious the superiority is.
Authors: Xue Mei Fan, Shu Jun Zhang, Kevin Hapeshi, Yin Sheng Yang
Abstract: People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and natural-inspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bio-inspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry.
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