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.

Efficient search approaches for k-medoids-based algorithms

Abstract: In this paper, the concept of previous medoid index is introduced The utilization of memory for efficient medoid search is also presented We propose a hybrid search approach for the problem of nearest neighbor search The hybrid search approach combines the previous medoid index, the utilization of memory, the criterion of triangular inequality elimination and the partial distance search The proposed hybrid search approach is applied to the k-medoids-based algorithms Experimental results based on Gauss-Markov source, curve data set and elliptic clusters demonstrate that the proposed algorithm applied to the CLARANS algorithm may reduce the number of distance calculations from 884% to 952% with the same average distance per object compared with CLARANS The proposed hybrid search approach can also be applied to nearest neighbor searching and the other clustering algorithms

Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles

Abstract: This paper studies the problem of intelligence optimization, a fundamental problem in analyzing the optimal solution in a wide spectrum of applications such as transportation and wireless sensor network (WSN). To achieve better optimization capability, we propose a multigroup Multistrategy Compact Sine Cosine Algorithm (MCSCA) by using the compact strategy and grouping strategy, which makes the initialized randomly generated value no longer an individual in the population and avoids falling into the local optimum. New evolution formulas are proposed for the intergroup communication strategy. Performance studies on the CEC2013 benchmark demonstrate the effectiveness of our new approach regarding convergence speed and accuracy. Finally, we apply MCSCA to solve the dispatch system of public transit vehicles. Experimental results show that MCSCA can achieve better optimization.

An extensible particles swarm optimization for energy-effective cluster management of underwater sensor networks

Abstract: Acoustic communication networks in underwater environment are the key technology to explore global ocean. There are major challenges including (1) lack of stable and sufficient power supply, (2) disable of radio frequency signal and (3) no communication protocol designed for underwater environment. Thus, acoustic so far is the only media suitable to operate for underwater communication. In this paper, we study the technology of underwater acoustic communication to support underwater sensor networks. Toward the energy-effective goal, a cluster-based sensor network is assumed. The energy-dissipation of sensor nodes is optimized by biological computing such as Particle Swarm Optimization (PSO). The objective function of sensor node clustering is formulized to constraint on the network coverage and energy dissipation. The problem of dual-objective optimization is solved by the proposed extensible PSO (ePSO). ePSOis an innovation from traditional PSO. The major innovation is to offer an extensible particle structure and to enable more flexible search for optimal solutions in space. The experimental results demonstrate that the proposed ePSO effectively and fast tackles multi-objective optimization problem. The application of ePSO on underwater acoustic communication systems shows the feasibility in real world.

Equal-average Equal-variance Equal-norm Nearest Neighbor Codeword Search Algorithm Based on Ordered Hadamard Transform

Abstract: This paper presents a fast codeword search algorithm that performs the equalaverage equal-variance equal-norm nearest neighbor search (EEENNS) in the ordered Hadamard transform (OHT) domain. By reordering the rows of Hadamard transform matrix, we can obtain the OHT with better energy packing efficiency, which is very important to the partial distance search (PDS) stage. Four elimination criteria based on three characteristic values, the first element, variance, and norm of the transformed vector, are introduced to reject a large number of unlikely codewords. Experimental results show that the proposed OHTEEENNS algorithm outperforms most of existing algorithms in the case of high dimension, especially for high-detail images.

Improved binary pigeon-inspired optimization and its application for feature selection

Abstract: The Pigeon-Inspired Optimization (PIO) algorithm is an intelligent algorithm inspired by the behavior of pigeons returned to the nest. The binary pigeon-inspired optimization (BPIO) algorithm is a binary version of the PIO algorithm, it can be used to optimize binary application problems. The transfer function plays a very important part in the BPIO algorithm. To improve the solution quality of the BPIO algorithm, this paper proposes four new transfer function, an improved speed update scheme, and a second-stage position update method. The original BPIO algorithm is easier to fall into the local optimal, so a new speed update equation is proposed. In the simulation experiment, the improved BPIO is compared with binary particle swarm optimization (BPSO) and binary grey wolf optimizer (BGWO). In addition, the benchmark test function, statistical analysis, Friedman’s test and Wilcoxon rank-sum test are used to prove that the improved algorithm is quite effective, and it also verifies how to set the speed of dynamic movement. Finally, feature selection was successfully implemented in the UCI data set, and higher classification results were obtained with fewer feature numbers.

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.