Shu-Chuan Chu

Bio: Shu-Chuan Chu is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 28, co-authored 231 publications receiving 3652 citations. Previous affiliations of Shu-Chuan Chu include University of South Australia & Sewanee: The University of the South.

Load balancing for mitigating hotspot problem in wireless sensor network based on enhanced diversity pollen

Abstract: This paper proposes a load balancing to mitigate the hot spot problem in wireless sensor network (WSN), based on enhancing diversity pollens in Flower pollination algorithm (FPA). The hotspot problem in the WSN is spots near base station (BS) that consume more energy and drain out energy more quickly than other nodes farther from the BS. The spots near BS are hotter than other places due to the heavy traffic from the cluster members and other cluster heads (CH) for relaying data to BS. Enhancing diversity pollens for FPA is one of the solutions to deal smoothly with trapping local extrema for solving the hotspot problem. To evaluate the proposed algorithm, we firstly use a set of benchmark functions to test performance quality, and secondly, we deal with the load balancing problem in WSN. The results compared with some metaheuristic approaches and other related clustering algorithms demonstrate that the proposed method performs better than the others regarding various performance metrics such as t...

Optimizing matrix mapping with data dependent kernel for image classification

Abstract: Kernel based nonlinear feature extraction is feasible to extract the feature of image for classification.The current kernel-based method endures two problems: 1) kernelbased method is to use the data vector through transforming the image matrix into vector, which will cause the store and computing burden; 2) the parameter of kernel function has the heavy influences on kernel based learning method. In order to solve the two problems, we present the method of optimizing matrix mapping with data dependent kernel for feature extraction of the image for classification. The method implements the algorithm without transforming the matrix to vector, and it adaptively optimizes the parameter of kernel for nonlinear mapping. The comprehensive experiments are implemented evaluate the performance of the algorithms.

An Incremental Multi-Centroid, Multi-Run Sampling Scheme for k-medoids-based Algorithms - Extended Report

Abstract: Data clustering has become an important task for discovering significant patterns and characteristics in large spatial databases. The Mufti- Centroid, Multi-Run Sampling Scheme (MCMRS) has been shown to be effective in improving the k-medoids-based clustering algorit hms in our previous work. In this paper, a more advanced sampling scheme termed Incremental MultiCentrozd, Multi-Run Sampling Scheme (IMCMRS) is proposed for k-medoidsbased clustering algorithms. Experimental results demonstrate the proposed scheme can not only reduce by more than 80’ZOcomputation time but also reduce the average distance per object compared with CLARA and CLARANS. IMCMRS is also superior to MCMRS.

Non-redundant VQ channel coding using tabu search strategy

Abstract: The tabu search approach is applied to codevector index assignment for noisy channels for the purpose of minimising the distortion due to bit errors without introducing any redundancy. Experimental results demonstrate the robustness of this approach compared with the standard parallel genetic algorithm and the binary switching algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Abstract: Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

Nature-Inspired Optimization Algorithms

Abstract: Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Maximum neighborhood margin discriminant projection for classification

Abstract: We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.