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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 Article
TL;DR: Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).
Abstract: In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

496 citations

Book ChapterDOI
07 Aug 2006
TL;DR: Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).
Abstract: In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

316 citations

01 Jan 2007
TL;DR: A new optimization algorithm, namely, Cat Swarm Optimization (CSO) is proposed, which is generated by observing the behavior of cats, and composed of two sub-models by simulating thebehavior of cats.
Abstract: Optimization problems are very important in many fields To the present, many optimization algorithms based on computational intelligence have been proposed, such as the Genetic Algorithm, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) In this paper, a new optimization algorithm, namely, Cat Swarm Optimization (CSO) is proposed CSO is generated by observing the behavior of cats, and composed of two sub-models by simulating the behavior of cats According to the experiments, the results reveal that CSO is superior to PSO

274 citations

Journal Article
TL;DR: A parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data, which demonstrates the usefulness of the proposed PPSO algorithm.
Abstract: Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.

250 citations

Journal ArticleDOI
TL;DR: Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed.
Abstract: Grey Wolf Optimizer (GWO) is a new swarm intelligence algorithm mimicking the behaviours of grey wolves. Its abilities include fast convergence, simplicity and easy realization. It has been proved its superior performance and widely used to optimize the continuous applications, such as, cluster analysis, engineering problem, training neural network and etc. However, there are still some binary problems to optimize in the real world. Since binary can only be taken from values of 0 or 1, the standard GWO is not suitable for the problems of discretization. Binary Grey Wolf Optimizer (BGWO) extends the application of the GWO algorithm and is applied to binary optimization issues. In the position updating equations of BGWO, the a parameter controls the values of A and D , and influences algorithmic exploration and exploitation. This paper analyses the range of values of A D under binary condition and proposes a new updating equation for the a parameter to balance the abilities of global search and local search. Transfer function is an important part of BGWO, which is essential for mapping the continuous value to binary one. This paper includes five transfer functions and focuses on improving their solution quality. Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed. It successfully implements feature selection in the UCI datasets and acquires low classification errors with few features.

204 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