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.

/pdf/maximum-neighborhood-margin-discriminant-projection-for-4vo54g9wbt.pdf

Maximum neighborhood margin discriminant projection for classification

. This article reports on our work on the clustering of non-uniformly dis-tributed data. An innovative method, termed incremental splitting, is presented. Tak-ing the K-means method as the core, the proposed approach splits only clusters withthe largest total error in each iteration. This heuristic has the effect of allocating moreclusters to those regions having more sample data. Consistent experimental resultsreveal that our method outperforms commonly used heuristics, including random ini-tialization, binary splitting and pair-wise nearest neighbour. 1 IntroductionData clustering is a common practice in various ﬁelds of rese arch and application develop-ment. For instance, in data mining, we might need to extract and capture hidden regularitiesdiffused across a large database and store them as a limited number of representative en-tities. For codebook design in vector quantization, we require a small number of the mostrepresentative vectors from potentially vast volumes of training data in order to minimize thequantization error.Without loss of generality, data clustering can be formulated as a problem of ﬁnding

Pattern Clustering Using Incremental Splitting for Non-Uniformly Distributed Data

In this paper, a novel Genetic Generalized Discriminant Analysis GGDA is proposed. GGDA is a generalized version of Exponential Discriminant Analysis EDA. EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.

Genetic Generalized Discriminant Analysis and Its Applications

The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In the first process, WCO tries to make the seeds explore the solution space to find the local optimal solutions. In the second process, it works to develop each optimal local solution and find the optimal global solution. In the experimental section, the performance of WCO is tested with 30 test functions from CEC 2017. WCO was applied in the Time Difference of Arrival and Frequency Difference of Arrival (TDOA-FDOA) co-localization problem of moving nodes in Wireless Sensor Networks (WSNs). Experimental results show the performance and applicability of the WCO algorithm.

/pdf/willow-catkin-optimization-algorithm-applied-in-the-tdoa-2va2jp3y.pdf

Shu-Chuan Chu

Papers

Pattern Clustering Using Incremental Splitting for Non-Uniformly Distributed Data

Genetic Generalized Discriminant Analysis and Its Applications

Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem

Improved butterfly optimization algorithm applied to prediction of combined cycle power plant

A fake threshold visual cryptography of QR code