Catálogo de publicaciones - libros

This paper is the first to propose a quantum-inspired genetic algorithm (QGA) for permutation flow shop scheduling problem to minimize the maximum completion time (makespan). In the QGA, Q-bit based representation is employed for exploration in discrete 0-1 hyperspace by using updating operator of quantum gate as well as genetic operators of Q-bit. Meanwhile, the Q-bit representation is converted to random key representation, which is then transferred to job permutation for objective evaluation. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the QGA, whose searching quality is much better than that of the famous NEH heuristic.

This paper is mainly about methodology in designing quantum algorithm. Based on study of Grover’s algorithm, we argue that it is a short cut to design and interpret quantum algorithms from the viewpoint of Householder transformation directly. We give an example for this claim, which extends Grover’s quantum search algorithm to some structured database. In this example, we show how to exploit some special structure information of problem, which restricts the search in some subspace. Based on an instantiation of this framework, we show that it does can utilize the information to the full extent. This paper gives the details that produce the algorithm framework. The idea, which is simple and intelligible, is universal to some extent, and therefore can be applied to other similar situations.

In the dynamic and uncertain network environment, the trust is an important mechanism for security and reliability of multi-agent system. This paper proposes a trust model for multi-agent system using fuzzy sets (TMMASFS). The distinguishing feature of TMMASFS is that there is the self-recommendation trust besides the direct trust and the recommendation trust. The self-recommendation trust is useful very much when the manager agent has neither the direct experience nor the recommendation experience about the contractor agents. Then the trust dynamic modification theorem is introduced, which can not only valuate the trust but also monitor the executed process of the task. At last, the trust valuation algorithm is presented, and the result of the experiment shows TMMASFS is efficient and adapted to the dynamic and uncertain network environment.

Task scheduling is one of the bottlenecks in realizing grid computing. We introduce swarm intelligence into task scheduling in a grid environment, and propose a new dynamic task-scheduling algorithm. This algorithm schedules effectively a group of independent tasks based on the interaction model between a wasp colony and its environment. We also present an effective method, using the self-organized dominance hierarchy of wasp colony to solve the dominance struggle problem that occurs in the proposed algorithm. Our evaluation results show that the proposed algorithm is more efficient and more adaptive to the dynamic grid environment than other task-scheduling algorithms.

Ant based routing protocols for MANETs have been widely explored, but most of them are essentially single-path routing methods which tend to have heavy burden on the hosts along the shortest path from source to destination. The robustness of these protocols is comparatively not good which is further weakened by the positive feedback mechanism of ant. Link-disjoint multi-path routing is more robust and can support QoS better than single-path routing in MANETs. In this paper we combine swarm intelligence and link-disjoint multi-path routing to solve the problem mentioned above. A novel approach named Ant colony based Multi-path QoS-aware Routing (AMQR) is proposed. AMQR establishes and utilizes multiple routes of link-disjoint paths to send data packets concurrently and adopts pheromone to disperse communication traffic, thus it can adapt to the dynamic changes of the network and support QoS better. The simulation results show that the proposed approach outperforms other pertinent algorithms.

In order to find most centre of the density of the sample set this paper combines MCA and PSO, and presents a mountain clustering based on improved PSO (MCBIPSO) algorithm. A mountain clustering method constructs a mountain function according to the density of the sample, but it is not easy to find all peaks of the mountain function. The improved PSO algorithm is used to find all peaks of the mountain function. The simulation results show that the MCBIPSO algorithm is successful in deciding the density clustering centers of data samples.

VQ coding is a powerful technique in digital image compression. Conversional methods such as classic LBG algorithm always generate local optimal codebook. In this paper, we introduce Particle Swarm Optimization (PSO) cluster method to build high quality codebook for image compression. We also set the result of LBG algorithm to initialize global best particle by which it can speed the convergence of PSO. Both image encoding and decoding process are simulated in our experiments. Results show that the algorithm is reliable and the reconstructed images get higher quality to images reconstructed by other methods.

Ant colonies behavior and their self-organizing capabilities have been popularly studied, and various swarm intelligence models and clustering algorithms also have been proposed. Unfortunately, the cluster number is often too high and convergence is also slow. We put forward a novel structure-attractor, which actively attracts and guides the ant’s behavior, and implement an efficient strategy to adaptively control the clustering behavior. Our experiments show that swarm intelligence clustering algorithm based on attractor ( for short) greatly improves the convergence speed and clustering quality compared with LF and also has many notable virtues such as flexibility, decentralization compared with conventional algorithms.

Establishment of Hydraulic Fracture in Oil field is a complicated system. The process of establishment of project involves many departments, which frequently interact each other. In general, The Orient-Object technology is not suitable to construct this system, which has these characters. The technology of Agent is a new method that analyses and designs the complicated system, which is suitable to develop the intricate and dynamic system and is able to simulate the society. This paper presents a soft computing society model by the methodology of Gaia, based on the characters of establishment of Hydraulic Fracture.

A general or agent-based security system is usually constructed hierarchically and has a central manager acting as head of the whole system. However, the manager becomes a bottleneck for being connected by each client. It can even overload when too many clients request service simultaneously. The whole system may collapse when the central manager is attacked. And these systems are passive to detect and deal with the secure problem. Hereby we present a mobile agent-based P2P Autonomous Security Hole Discovery system (PASHD). It can detect infection and network intrusion based on knowledge of the local host. Viruses will be removed and connection will be refused after identification. In case of a suspicious activity, PASHD initiates a voting approach to make a collective decision and take further action. This system acts self-learning when encountering intrusion or infection with new patterns. And it has the capability of autonomous discovery the security hole of hosts in network. The integration of peer-to-peer behavior with mobile agents reduces latency and load; however, flexibility, effectivity, security and cooperation of the system are enhanced.