A morphable model for the synthesis of 3D faces

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.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Probability and Measure

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

Nature-Inspired Optimization Algorithms

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.

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Maximum neighborhood margin discriminant projection for classification

Clustering formation is the modern energy efficient techniques in the designing and implementing Wireless sensor networks (WSN). Clustering provides various advantages like energy efficiency, lifetime, scalability, and less delay; but it leads to hot spot problem. The proposed unequal clustering is to overcome this issue. In uneven clustering, the cluster size varies proportionally to the distance to the base station (BS). In this paper, a comprehensive survey of various clustering formation approaches with their objectives, characteristics, etc. is presented. Also, the classifications of uneven clustering methods are carried out and compared them based on various cluster properties, Cluster Head (CH) properties, and clustering process.

Clustering Formation in Wireless Sensor Networks: A Survey.

Addressing to the computational requirements of the hardware devices with limited resources such as memory size or low price is critical issues. This paper, a novel algorithm, namely compact Bat Algorithm (cBA), for solving the numerical optimization problems is proposed based on the framework of the original Bat algorithm (oBA). A probabilistic representation random of the Bat’s behavior is inspired to employ for this proposed algorithm, in which the replaced population with the probability vector updated based on single competition. These lead to the entire algorithm functioning applying a modest memory usage. The simulations compare both algorithms in terms of solution quality, speed and saving memory. The results show that cBA can solve the optimization despite a modest memory usage as good performance as oBA displays with its complex population-based algorithm. It is used the same as what is needed for storing space with six solutions.

Compact Bat Algorithm

The meta-heuristic evolutionary algorithm is widely used because of its excellent global optimization ability. However, its demand for a mass of evaluation times will lead to an increase in time complexity. Especially when the dimensions of actual problems are too high, the time cost for fitness evaluation is usually minutes, hours, or even days. To improve the above shortcomings and the ability to solve high-dimensional expensive problems, a Fuzzy Hierarchical Surrogate Assisted Probabilistic Particle Swarm Optimization is proposed in this paper. This algorithm first uses Fuzzy Surrogate-Assisted (FSA), Local surrogate-assisted (LSA), and Global surrogate-assisted (GSA) models to fit the fitness evaluation function individually. Secondly, a probabilistic particle swarm optimization is implemented to predict the trained model and update the samples. FSA mainly uses a Fuzzy Clustering algorithm that divides the archive DataBase ( D B ) into multiple sub-archives to model separately to accurately estimate the function landscape of the function in the partial search space. LSA is mainly designed to capture the local details of the fitness function around the current individual neighborhood and enhance the local optimal accuracy estimation. GSA will build an accurate global model in the entire search space. To verify the performance of our proposed algorithm in solving high-dimensional expensive problems, experiments on seven benchmark functions are conducted in 30D, 50D, and 100D. The final test results show that our proposed algorithm is more competitive than other most advanced algorithms.

Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem

In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance.

3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm

Face recognition has the wide research and applications on many areas. Many surveys of face recognition are implemented. Different from previous surveys on from a single viewpoint of application, method or condition, this paper has a comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions. In the sensory inputs, we review face recognition from image-based, video-based, 3D-based and hypersprectral image based face recognition, and a comprehensive survey of face recognition methods from the viewpoints of signal processing and machine learning are implemented, such as kernel learning, manifold learning method. Moreover we discuss the single training sample based face recognition and under the variable poses. The prominent algorithms are described and critically analyzed, and relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed

Shu-Chuan Chu

Papers

Clustering Formation in Wireless Sensor Networks: A Survey.

Compact Bat Algorithm

Fuzzy Hierarchical Surrogate Assists Probabilistic Particle Swarm Optimization for expensive high dimensional problem

3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm

Multiple viewpoints based overview for face recognition