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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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Journal ArticleDOI
TL;DR: Comparisons of MFPA using three strategies with FPA and PSO show that MFPA based on novel communication strategies has a good global optimization ability, improving the convergence speed and accuracy of the FPA.
Abstract: Multi-group Flower Pollination Algorithm (MFPA) based on novel communication strategies was proposed with an eye to the disadvantages of the Flower Pollination Algorithm (FPA), such as tardy convergence rate, inferior search accuracy, and strong local optimum. By introducing a parallel operation to divide the population into some groups, the global search capability of the algorithm was improved. Then three new communication strategies were proposed. Strategy 1 combined high-quality pollens of each group for evolution and replaced the old pollens. Strategy 2 let each group’s inferior pollens approaching to the optimal pollen. Strategy 3 was a combination of strategies 1 and 2. Then, experiments on 25 classical test functions show that MFPA based on novel communication strategies has a good global optimization ability, improving the convergence speed and accuracy of the FPA. Thus, we compare MFPA using three strategies with FPA and PSO, its result shows that MFPA is better than FPA and PSO. Finally, we also applied it to two practical problems and achieved a better convergence effect than FPA.

7 citations

Journal ArticleDOI
TL;DR: In this paper, an automatic pennation angle measuring approach based on deep learning is proposed, where the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the pennation angles.

7 citations

Journal ArticleDOI
TL;DR: Improvements are suggested that can be applied to most k-medoids-based algorithms - conceptual/algorithmic improvements, and implementational improvements that include the revisiting of the accepted cases for swap comparison and the application of partial distance searching and previous medoid indexing to clustering.
Abstract: In this paper two categories of improvements are suggested that can be applied to most k-medoids-based algorithms - conceptual/algorithmic improvements, and implementational improvements. These include the revisiting of the accepted cases for swap comparison and the application of partial distance searching and previous medoid indexing to clustering. Various hybrids are then applied to a number of k-medoids-based algorithms and the method is shown to be generally applicable. Experimental results on both artificial and real datasets demonstrate that when applied to CLARANS the number of distance calculations can be reduced by up to 98%.

7 citations

Book ChapterDOI
01 Dec 2018
TL;DR: The results compared with the other methods in the literature shows that the proposed approach can provide the robot achieve to its target with collision-free obstacles, and be a competitive approach for optimal robot planning.
Abstract: This paper proposes a novel multi-objective approach for optimal robot path planning based on Ion Motion Optimization (IMO). Two criteria are the distance to the target and smooth path that considered to optimize for the robot path planning issue. Location targets and obstacles are used to model mathematically the fitness function. Robots update information during the move because of partially unknown environment due to the limited sensors in detecting range. Simulations of the robot reached to target are implemented in different scenario environments for the optimal path. The results compared with the other methods in the literature shows that the proposed approach can provide the robot achieve to its target with collision-free obstacles, and be a competitive approach for optimal robot planning.

7 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