Peng-Yeng Yin

Multilevel minimum cross entropy threshold selection based on particle swarm optimization

Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications.

A fast scheme for optimal thresholding using genetic algorithms

Abstract Traditional optimal thresholding methods are very popular and efficient in the case of bi-level thresholding. But they are very computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. In this paper, a fast scheme using genetic algorithms is proposed to render these optimal thresholding techniques more practical. The experimental results show that the proposed scheme can make the optimal thresholding methods applicable in the case of multilevel thresholding and the performances are better than those of some property-based multilevel thresholding methods.

Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time

Ant colony optimization (ACO) is a meta-heuristic proposed to derive approximate solutions for computationally hard problems by emulating the natural behaviors of ants. In the literature, several successful applications have been reported for graph-based optimization problems, such as vehicle routing problems and traveling salesman problems. In this paper, we propose an application of the ACO to a two-machine flowshop scheduling problem. In the flowshop, no intermediate storage is available between two machines and each operation demands a setup time on the machines. The problem seeks to compose a schedule that minimizes the total completion time. We first present a transformation of the scheduling problem into a graph-based model. An ACO algorithm is then developed with several specific features incorporated. A series of computational experiments is conducted by comparing our algorithm with previous heuristic algorithms. Numerical results evince that the ACO algorithm exhibits impressive performances with small error ratios. The results in the meantime demonstrate the success of ACOs applications to the scheduling problem of interest.

A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems

In a distributed system, a number of application tasks may need to be assigned to different processors such that the system cost is minimized and the constraints with limited resource are satisfied. Most of the existing formulations for this problem have been found to be NP-complete, and thus finding the exact solutions is computationally intractable for large-scaled problems. This paper presents a hybrid particle swarm optimization algorithm for finding the near optimal task assignment with reasonable time. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also, our method converges at a fast rate and is suited to large-scaled task assignment problems.

A discrete particle swarm algorithm for optimal polygonal approximation of digital curves

Polygonal approximation of digital curves is one of the crucial steps prior to many image analysis tasks. This paper presents a new polygonal approximation approach based on the particle swarm optimization (PSO) algorithm. Each particle represented as a binary vector corresponds to a candidate solution to the polygonal approximation problem. A swarm of particles are initiated and fly through the solution space for targeting the optimal solution. We also propose to use a hybrid version of PSO embedding a local optimizer to enhance the performance. The experimental results manifest that the proposed discrete PSO is comparable to the genetic algorithm, and it outperforms another discrete implementation of PSO in the literature. The proposed hybrid version of PSO can significantly improve the approximation results in terms of the compression ratio, and the results obtained in different runs are more consistent. 2003 Elsevier Inc. All rights reserved.

Ant colony search algorithms for optimal polygonal approximation of plane curves

This paper presents a new polygonal approximation method using ant colony search algorithm. The problem is represented by a directed graph such that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The important properties of the proposed method are thoroughly investigated. The performance of the proposed method as compared to those of the genetic-based and the tabu search-based approaches is very promising.

Integrating relevance feedback techniques for image retrieval using reinforcement learning

Relevance feedback (RF) is an interactive process which refines the retrievals to a particular query by utilizing the users feedback on previously retrieved results. Most researchers strive to develop new RF techniques and ignore the advantages of existing ones. In this paper, we propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques in a content-based image retrieval system. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. Also, a concept digesting method is proposed to reduce the complexity of storage demand. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone, and that the sharing of relevance knowledge between multiple query sessions significantly improves the performance. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model with the increasing-size of database.

Task allocation for maximizing reliability of a distributed system using hybrid particle swarm optimization

In a distributed computing system, a number of program modules may need to be allocated to different processors such that the reliability of executing successfully these modules is maximized and the constraints with limited resources are satisfied. The problem of finding an optimal task allocation with maximum system reliability has been shown to be NP-hard; thus, existing approaches to finding exact solutions are limited to the use in problems of small size. This paper presents a hybrid particle swarm optimization (HPSO) algorithm for finding the near-optimal task allocation within reasonable time. The experimental results show that the HPSO is robust against different problem size, task interaction density, and network topology. The proposed method is also more effective and efficient than a genetic algorithm for the test-cases studied. The convergence and the worst-case characteristics of the HPSO are addressed using both theoretical and empirical analysis.

An Ant Colony Optimization Algorithm for the Minimum Weight Vertex Cover Problem

Given an undirected graph and a weighting function defined on the vertex set, the minimum weight vertex cover problem is to find a vertex subset whose total weight is minimum subject to the premise that the selected vertices cover all edges in the graph. In this paper, we introduce a meta-heuristic based upon the Ant Colony Optimization (ACO) approach, to find approximate solutions to the minimum weight vertex cover problem. In the literature, the ACO approach has been successfully applied to several well-known combinatorial optimization problems whose solutions might be in the form of paths on the associated graphs. A solution to the minimum weight vertex cover problem however needs not to constitute a path. The ACO algorithm proposed in this paper incorporates several new features so as to select vertices out of the vertex set whereas the total weight can be minimized as much as possible. Computational experiments are designed and conducted to study the performance of our proposed approach. Numerical results evince that the ACO algorithm demonstrates significant effectiveness and robustness in solving the minimum weight vertex cover problem.

A fast iterative scheme for multilevel thresholding methods

Abstract The previously published optimal thresholding techniques based on some objective functions are very efficient in the bi-level thresholding case, but they are impractical when extended to multilevel thresholding. The reason for this is their computational complexity which grows exponentially with the number of thresholds. In this paper, an iterative scheme is proposed to render these optimal thresholding techniques more practical. The proposed algorithm starts with a bi-level thresholding, then uses the initial results to obtain higher-order thresholds. This algorithm is iterative and the convergence is proved. We also introduce some useful programming techniques to make the computation more efficient. The proposed algorithm can therefore determine the number of thresholds automatically as well as save a significant amount of computing time.