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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.


Papers
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Book ChapterDOI
15 Oct 2012
TL;DR: A novel feature extraction algorithm based on nearest feature line that can extract the local discriminant features of the samples using two discriminant power criterions to adaptively determine the parameter.
Abstract: A novel feature extraction algorithm based on nearest feature line is proposed in this paper. The proposed algorithm can extract the local discriminant features of the samples. The performance of the proposed algorithm is directly associated with the parameter, so we use two discriminant power criterions to adaptively determine the parameter. Some experiments are implemented to evaluate the proposed algorithm and the experimental results demonstrate the efficiency of the proposed algorithm.

11 citations

Journal ArticleDOI
23 May 2022-Drones
TL;DR: A modified Mayfly Algorithm is introduced, which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost.
Abstract: The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.

11 citations


Cited by
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Proceedings Article
01 Jan 1999

2,010 citations

Journal ArticleDOI
TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
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.

1,645 citations

01 Jan 1996

1,282 citations

Book
17 Feb 2014
TL;DR: This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences, and researchers and engineers as well as experienced experts will also find it a handy reference.
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

901 citations

Journal ArticleDOI
TL;DR: A novel maximum neighborhood margin discriminant projection technique for dimensionality reduction of high-dimensional data that cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes.
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.

771 citations