Catálogo de publicaciones - libros

A novel method of data fitting via chaotic ant swarm (CAS) is presented in this paper. Through the construction of a suitable function, the problem of data fitting can be viewed as that of parameter optimization, and then the CAS is used to search the parameter space so as to find the optimal estimations of the system parameters. To investigate the performances of the CAS, the CAS is compared with the particle swarm optimization (PSO) on two test problems. Simulation results indicate that the CAS achieves better performances.

The discrete particle swarm algorithm for binary constraint satisfaction problems (CSPs) is analyzed in this paper. The analysis denotes that ϕ _1 and ϕ _2 are set to 0 may be a heuristic similar to min-conflict heuristic. The further observation is the impact of local best positions. A control parameter p _ b is introduced to reduce the effect of the local best positions. To improve the performance, simulated annealing algorithm is combined with the discrete particle swarm algorithm, and the neighborhood exploring in simulated annealing is carried out by ERA model. Eliminating repeated particles and Tabu list avoiding cycling are also introduced in this paper. Our hybrid algorithm is tested with random constraint satisfaction problem instances based on phase transition theory. The experimental results indicate that our hybrid discrete particle swarm algorithm is able to solve hard binary CSPs.

Inspired by the searching model proposed by Kleinberg in a small-world network and based on a novel proposed description that an optimization can be described as a process where information transmitted from a candidate solution to the optimal solution in solution space of problems, where the solution space can also be regarded as a small-world network and each solution as a node in the small-world network, a new optimization strategy with small-world effects was formulated in this paper. The analysis and the simulation experiments in the global numerical optimization problems indicated that the method achieved a fast convergence rate and obtained a good searching performance in optimization.

A novel method for the real-time global optimal path planning of mobile robots is proposed based on the modified ant system (AS) algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the Dijkstra algorithm to find a sub-optimal collision-free path, and the third step is adopting the modified AS algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path. The results of simulation experiments confirm that the proposed method is effective and has better performance in convergence speed, solution variation, dynamic convergence behavior, and computation efficiency as compared with the path planning method based on the real-coded genetic algorithm.

It is very important to design a good routing-system for the whole compile-synthesis system of reconfigurable architecture (RA). Because the routing resources of coarse-grain RA (CGRA) are less than those of fine-grain RA, and several functions are often defined in same one element of RA, it is difficult to find a good route. Therefore, it is more important for routing-algorithm of CGRA to have stronger ability of finding feasible and optimum path. In the paper, the improved max—min Ant System (MMAS) that added the ability of smell for ant is applied for the routing problem of CGRA. By several benchmarks on CTaiJi that is a new developed CGRA, The improved MMAS shows better ability to find the best solution than PathFinder that is often used now.

This paper investigates the problem of robot planning with ant colony optimization and artificial potential filed algorithms. Robot planning is to find a feasible path from a source to a goal while avoiding obstacles in configuration space. Artificial potential field (APF) is verified as an efficient method to find a path by following the maximum potential field gradient. But it suffers from the local minima. However, ant colony optimization (ACO) is characterized as powerful probabilistic search ability, which is thought to be fit for solving such local minima problems. By the combination of both merits, an APF guided ACO algorithm is proposed, which shows some good features in searching for the optimal path solution. The length optimal path solution can always be achieved with the proposed hybrid algorithm in different obstacles environment from simulation results.

Filter banks are found many applications for images and signal processing. A lower complex filter banks are desired for their effective implementation. In this paper, we address a problem how to design low complex filter banks. A perfect restructure (PR) filter banks can be factorized by lifting steps and the procedure of factorization are described as multi-fork tree-structure. A heuristic-searching algorithm was proposed for finding the optimization PR filter banks with low dynamical coefficients. The given examples show the proposed method is effective.

To mitigate the inter-chip and inter-symbol interferences (ISI), multi-carrier code-division multiple access (MC-CDMA) communication systems, which integrate the advantages of multi-carrier transmission systems with those of CDMA, are considered as promising techniques for future broadband wireless multimedia communications. In this paper, we focus on the uplink multi-user detection (MUD) on the scheme that a uniform linear array (ULA) is applied to the base station of the MC-CDMA system. We first describe the equivalent spatial-temporal channel model of multi-user multiple-input multiple-output (MIMO) MC-CDMA systems. Based on this scheme, by utilizing the finite alphabet property of transmitted symbols, an multi-user detector is derived for the MC-CDMA system with the ULA over frequency-selective fading channels. Computer simulations illustrate that our algorithm is more robust against noise and can well mitigate multiple access interference (MAI) in multi-user scenarios. In particular, the proposed scheme has the potential of providing the anticipated MUD performance gains with a complexity that would be manageable for MC-CDMA systems.

In this paper, we propose a simple design method of nonuniform filter banks (NUFBs) with rational sampling factors. The analysis filters are respectively generated by cosine modulating several prototype filters with certain matching condition. By following a cosine roll-off function of the transition bands of the stretched prototype filters, the overall system is approximately power complementary and therefore possesses the near-prefect-reconstruction (NPR) property. Further, due to the use of the Parks-McClellan algorithm in the filter design, no objective function in the optimization is explicitly required and optimal prototype filters for NPR cosine-modulated NUFBs can be obtained. Design examples along with comparisons show that the resulting filter banks are of high filter quality and the proposed method is of simplicity and effectiveness.

This paper introduces a XCP framework based reliable multicast transport protocol XRMCCP (XCP framework based Reliable Multicast Congestion controlled protocol) that has been designed to be simple, scalable and reliable for high bandwidth-delay product (BDP) networks. We observe that TCP Friendly Throughput Equation (TFTE), which many current multicast transport protocols use to estimate available rates of multicast sessions, has limits of performance in high BDP networks. XRMCCP generalizes XCP framework to support one-to-many transport congestion control. The scalability issue is addressed with an exponential timers scheme that is also used to estimate the number of receivers involved in the communication. The paper presents a number of simulation results on its performance. Besides, some design choices are evaluated.