Shu-an Chu

Ant colony system with communication strategies

In this paper an ant colony system (ACS) with communication strategies is developed. The artificial ants are partitioned into several groups. Seven communication methods for updating the pheromone level between groups in ACS are proposed and work on the traveling salesman problem using our system is presented. Experimental results based on three well-known traveling salesman data sets demonstrate the proposed ACS with communication strategies are superior to the existing ant colony system (ACS) and ant system (AS) with similar or better running times.

Kernel class-wise locality preserving projection

In the recent years, the pattern recognition community paid more attention to a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. However, when LPP is applied to the classification tasks, it shows some limitations, such as the ignorance of the label information. In this paper, we propose a novel local structure based feature extraction method, called class-wise locality preserving projection (CLPP). CLPP utilizes class information to guide the procedure of feature extraction. In CLPP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and the class information. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Moreover, a kernel version of CLPP namely Kernel CLPP (KCLPP) is developed through applying the kernel trick to CLPP to increase its performance on nonlinear feature extraction. Experiments on ORL face database and YALE face database are performed to test and evaluate the proposed algorithm.

Cat swarm optimization

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

A ladder diffusion algorithm using ant colony optimization for wireless sensor networks

In this paper, an algorithm based on ladder diffusion and ACO [5,6] is proposed to solve the power consumption and transmission routing problems in wireless sensor networks. The proposed ladder diffusion algorithm is employed to route paths for data relay and transmission in wireless sensor networks, reducing both power consumption and processing time to build the routing table and simultaneously avoiding the generation of circle routes. Moreover, to ensure the safety and reliability of data transmission, our algorithm provides backup routes to avoid wasted power and processing time when rebuilding the routing table in case part of sensor nodes are missing. According to the experimental results, the proposed algorithm not only reduces power consumption by 52.36% but also increases data forwarding efficiency by 61.11% as compared to the directed diffusion algorithm. This decrease is because the algorithm properly assigns the transmission routes to balance the load on every sensor node.

Tabu search based multi-watermarks embedding algorithm with multiple description coding

Digital watermarking is a useful solution for digital rights management systems, and it has been a popular research topic in the last decade. Most watermarking related literature focuses on how to resist deliberate attacks by applying benchmarks to watermarked media that assess the effectiveness of the watermarking algorithm. Only a few papers have concentrated on the error-resilient transmission of watermarked media. In this paper, we propose an innovative algorithm for vector quantization (VQ) based image watermarking, which is suitable for error-resilient transmission over noisy channels. By incorporating watermarking with multiple description coding (MDC), the scheme we propose to embed multiple watermarks can effectively overcome channel impairments while retaining the capability for copyright and ownership protection. In addition, we employ an optimization technique, called tabu search, to optimize both the watermarked image quality and the robustness of the extracted watermarks. We have obtained promising simulation results that demonstrate the utility and practicality of our algorithm.

Hadamard transform based fast codeword search algorithm for high-dimensional VQ encoding

An efficient nearest neighbor codeword search algorithm based on Hadamard transform for vector quantization is presented in This work. Four efficient elimination criteria are derived from two important inequalities based on three characteristic values in the Hadamard transform domain. Before the encoding process, all codewords in the codebook are Hadamard-transformed and sorted in the ascending order of their first elements. During the encoding process, we firstly perform the transform on the input vector and calculate its characteristic values, and initialize the current closest codeword of the input vector to be the codeword whose first element of Hadamard transform is nearest to that of the input vector, and secondly use the proposed elimination criteria to find the nearest codeword to the input vector using the up-down search mechanism near the initial best-match codeword. Experimental results demonstrate the proposed algorithm is much more efficient than most existing nearest neighbor codeword search algorithms in the case of high dimension.

Constrained ant colony optimization for data clustering

Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This paper extends ant colony systems and discusses a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the Ant Colony Optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters.

Intelligent Parallel Particle Swarm Optimization Algorithms

Some social systems of natural species, such as flocks of birds and schools of fish, possess interesting collective behavior. In these systems, globally sophisticated behavior emerges from local, indirect communication amongst simple agents with only limited capabilities. In an attempt to simulate this flocking behavior by computers, Kennedy and Eberthart (1995) realized that an optimization problem can be formulated as that of a flock of birds flying across an area seeking a location with abundant food. This observation, together with some abstraction and modification techniques, led to the development of a novel optimization technique particle swarm optimization. Particle swarm optimization has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. Some communication strategies for PPSO are discussed in this work, which can be used according to the strength of the correlation of parameters. Experimental results confirm the superiority of the PPSO algorithms.

A Novel Optimization Approach: Bacterial-GA Foraging

In this paper, we proposed a novel optimization model, which combines bacterial foraging with genetic algorithm. Though these two well-known optimization algorithms have their own good points, they also have their own drawbacks respectively. In our work, a combined evolutional model, bacterial-GA foraging, is proposed. Via applying this new model, experimental results indicate that the new combined model performs much better performance than applying any of these two algorithms singly.

Overview of algorithms for Swarm intelligence

Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.