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.

Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization

Abstract: Easy convergence to a local optimum, rather than global optimum could unexpectedly happen in practical multimodal optimization problems due to interference phenomena among physically constrained dimensions. In this paper, an altering strategy for dynamic diversity Flower pollination algorithm (FPA) is proposed for solving the multimodal optimization problems. In this proposed method, the population is divided into several small groups. Agents in these groups are exchanged frequently the evolved fitness information by using their own best historical information and the dynamic switching probability is to provide the diversity of searching process. A set of the benchmark functions is used to test the quality performance of the proposed method. The experimental result of the proposed method shows the better performance in comparison with others methods.

A Compact Flower Pollination Algorithm Optimization

Abstract: A restricted hardware condition is difficult for optimization problems. This paper proposes a novel compact flower pollination algorithm for addressing the class of optimization problems in the restricted hardware condition. In this proposed method, the actual population of tentative solutions is not stored, but a novel probabilistic representation on the population is employed based on the single competition. In the simulation, several problems of numerical optimizations in the benchmark are used to evaluate the accuracy, the computational time and the saving memory of the proposed method. The results compared with the original algorithm and the other algorithms in the literature show that the new proposed method provides the effective way of using a limited memory.

A competitive mechanism based multi-objective differential evolution algorithm and its application in feature selection

Abstract: A large number of evolutionary algorithms have been introduced for multi-objective optimization problems in the past two decades. However, the compromise of convergence and diversity of the non-dominated solutions is still the main difficult problem faced by optimization algorithms. To handle this problem, an efficient competitive mechanism based multi-objective differential evolution algorithm (CMODE) is designed in this work. In CMODE, the rank based on the non-dominated sorting and crowding distance is first adopted to create the leader set, which is utilized to lead the evolution of the differential evolution (DE) algorithm. Then, a competitive mechanism using the shift-based density estimation (SDE) strategy is employed to design a new mutation operation for producing offspring, where the SDE strategy is beneficial to balance convergence and diversity. Meanwhile, two variants of the CMODE using the angle competitive mechanism and the Euclidean distance competitive mechanism are proposed. The experimental results on three test suites show that the proposed CMODE performs better than six state-of-the-art multi-objective optimization algorithms on most of the twenty benchmark functions in terms of hypervolume and inverted generation distance. Furthermore, the proposed CMODE is applied to the feature selection problem. The comparison results on feature selection also demonstrate the efficiency of our proposed CMODE.

Finger Vein Recognition Scheme Based on Convolutional Neural Network Using Curvature Gray Image.

Abstract: Finger vein recognition technology refers to the use of finger vein angiography image authentication technology, which has became one of the hot spots of biometric identification technique. Conventional finger vein recognition technology is based on image features, and its main idea is to extract features of the overall image or features of the vein pattern. Because there are a large amount of redundant data based on features acquired from the whole finger vein image, the time complexity is high, and the features extracted from the vein pattern are greatly affected by the image segmentation algorithm. In order to improve the accuracy of the finger vein recognition algorithm under small samples, a finger vein recognition algorithm based on convolutional neural network using curvature gray images is proposed in this paper. First, we calculate the curvature of a finger vein image using a two-dimensional Gaussian template. Then we extract two gray images of the finger vein image with different scales and add these two images to obtain the final curvature gray image. Using curvature gray images as input, an improved convolutional neural network is trained and used to recognize the identity of the input curvature gray image. Experimental results show that our scheme is effective and better than existing schemes.

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.