Papers by Keyword: Ant Colony Optimization (ACO)

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Authors: Xian Yi Chen, Zhi Gang Jin, Xiong Yang
Abstract: As ant colony optimization algorithm and clustering routing algorithm were discussed deeply, a clustering routing algorithm based ant colony optimization (CRAACO) for wireless sensor networks has been put forward. To test the performance of CRAACO, simulations have been done from information fusion rates, remaining energy and network lifetime. The experiment results show that the CRAACO can work effectively and may be used in wireless sensor networks.
Authors: Wen Xin Hu, Jun Zheng, Xia Yu Hua, Ya Qian Yang
Abstract: For several special features in the environment of cloud computing, which may be quite different from the centralized computing infrastructure currently available, the existed method of resource allocation used in the grid computing environment may not be suitable for these changes. In our paper, a new allocation algorithm based on Ant Colony Optimization (ACO) is proposed to satisfy the needs of Infrastructure as a Service (IaaS) supported by the cloud computing environment. When started, this algorithm first predicts the capability of the potentially available resource nodes; then, it analyzes some factors such as network qualities and response times to acquire a set of optimal compute nodes; finally, the tasks would be allocated to these suitable nodes. This algorithm has shorter response time and better performance than some of other algorithms which are based on Grid environment when running in the simulate cloud environment. This result is verified by the simulation in the Gridsim environment elaborated in the following section.
Authors: Ji Ung Sun, Don Ki Baek
Abstract: In this paper we consider a capacitated single allocation p-hub median problem with direct shipment (CSApHMPwD). We determine the location of p hubs, the allocation of non-hub nodes to hubs, and direct shipment paths in the network. This problem is formulated as 0-1 integer programming model with the objective of the minimum total transportation cost and the fixed cost associated with the establishment of hubs. An optimal solution is found using CPLEX for the small sized problems. Since the CSApHMPwD is NP-hard, it is difficult to obtain optimal solution within a reasonable computational time. Therefore, an ant colony optimization algorithm is developed which solves hub selection and node allocation problem hierarchically. Its performance is examined through a comparative study. The experimental results show that the proposed ant colony optimization algorithm can be a viable solution method for the capacitated hub and spoke network design problem.
Authors: Jing Chen, Xiao Xia Zhang, Yun Yong Ma
Abstract: This paper presents a novel hybrid ant colony optimization approach (ACO&VNS) to solve the permutation flow-shop scheduling problem (PFS) in manufacturing systems and industrial process. The main feature of this hybrid algorithm is to hybridize the solution construction mechanism of the ant colony optimization (ACO) with variable neighborhood search (VNS) which can also be embedded into the ACO algorithm as neighborhood search to improve solutions. Moreover, the hybrid algorithm considers both solution diversification and solution quality. Finally, the experimental results for benchmark PFS instances have shown that the hybrid algorithm is very efficient to solve the permutation flow-shop scheduling in manufacturing engineering compared with the best existing methods in terms of solution quality.
Authors: Y.H. Gai, Gang Yu
Abstract: This paper presents a novel hybrid feature selection algorithm based on Ant Colony Optimization (ACO) and Probabilistic Neural Networks (PNN). The wavelet packet transform (WPT) was used to process the bearing vibration signals and to generate vibration signal features. Then the hybrid feature selection algorithm was used to select the most relevant features for diagnostic purpose. Experimental results for bearing fault diagnosis have shown that the proposed hybrid feature selection method has greatly improved the diagnostic performance.
Authors: Yi Zhang, Meng Zhang
Abstract: In this paper, we introduce a hybrid optimization algorithm with the Branch-and-Bound Method and the Ant Colony Optimization to solve the multi-chromosomal reversal median problem. We convert the large-scale genome into TSP maps at first. Then we use a hybrid optimization algorithm with the Branch-and-Bound Method and the Ant Colony Optimization to solve the problem. In our improved algorithm, we increase the search speed by implement multi-branch parallel search of ACO. Our extensive experiments on simulated datasets show that this median solver is efficient.
Authors: Hai Ning Wang, Shou Qian Sun, Bo Liu
Abstract: In this paper, for the problems of low convergence rate and getting trapped in local optima easily, the average path similarity (APS) was proposed to present the optimization maturity by analyzing the relationship between parameters of local pheromone updating and global pheromone updating, as well as the optimizing capacity and convergence rate. Furthermore, the coefficients of pheromone updating adaptively were adjusted to improve the convergence rate and prevent the algorithm from getting stuck in local optima. The adaptive ACS has been applied to optimize several benchmark TSP instances. The solution quality and convergence rate of the algorithm were compared comprehensively with conventional ACS to verify the validity and the effectiveness.
Authors: Zong Li Liu, Jie Cao, Zhan Ting Yuan
Abstract: This paper proposes a new approach to determining the complex system design for a product mix comprising complex hierarchies of subassembly and components. Pareto Ant Colony Optimisation as an especially effective meta-heuristic for solving the problem of complex system design was introduced in this paper. A Pareto Optimal Set of complex system in which only the non dominated solutions allow ants to deposit pheromones over the time and cost pheromone matrices after certain generation runs. Simulation results show that the model for complex system and the hybrid algorithms are effective to the design of complex system.
Authors: Jing Chun Lin, Ling Yu
Abstract: A new MRACO identification algorithm is proposed for structural multi-damage detection through combining MapReduce procedure and ACO method in this paper. Four classical benchmark functions are first employed to evaluate convergent performance of the MRACO algorithm, which pursues a global solution to combination optimal problem with constrained conditions. Then, a series of numerical simulations on constrained optimal problem about structural multi-damage detection of a two-story rigid frame have been conducted for assessing the applicability of the new MRACO algorithm applied to the structural damage detection field. Finally, some illustrated numerical results show that the MRACO algorithm can not only locate the structural multiple damages but also effectively quantify the severity of damages with higher accuracy and good noise immunity.
Authors: Lu Lin
Abstract: To solve the deficiency of ant colony optimization as falling into local optimal solution easily, the paper proposes a dynamic globe pheromone ant system which based on the small world network phenomenon of information exchange in ant colony system and simulates this mechanism by meanings of the wave equation of volatilization pheromone, and then constructs the particle wave function of diffuse pheromone as well as the corresponding condition shift formula. Through dynamic surveying proliferation wave information, the ant is able to effectively absorb the effective information containing in the inferior solutions during the process of seeking superior solution, and can carry on the condition shift using the globe distributed pheromone information, thus enhance the quality of solution. Taking vehicle routing problem as example, the computed result shows that compared the basis colony optimization DGPAS has higher globe search ability.
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