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

The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.

https://www.mdpi.com/2073-8994/12/3/445/pdf

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

A maximum descent (MD) method has been proposed for vector quantization (VQ) codebook design. Compared with the traditional generalized Lloyd algorithm (GLA), the MD algorithm achieves better codebook performance with far less computation time. However, searching for the optimal partitioning hyperplane of a multidimensional cluster is a difficult problem in the MD algorithm. Three partition techniques have been pro- posed for the MD method in the literature. In this paper, a new partition technique based on the tabu search (TS) approach is presented for the MD algorithm. Experimental re- sults show that the tabu search based MD algorithm can produce a better codebook than can the conventional MD algorithms.

https://ir.nctu.edu.tw:443/bitstream/11536/29425/1/000171597200003.pdf

A Tabu Search Based Maximum Descent Algorithm for VQ Codebook Design

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

/pdf/a-lightweight-intelligent-intrusion-detection-model-for-8n6slg43mg.pdf

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method

Digital color image encryption is different from text encryption because of some inherent features of image such as huge data capacity and high correlation among the neighboring pixels. Because of the desirable cryptographic properties of the chaotic maps such as sensitivity to initial conditions and random-like behave, more and more researches use these properties for encryption. This paper proposed an efficient image encryption scheme. Logistic chaos-based stream cipher is utilized to permute the color image. The MD5 hash function and the ZUC stream cipher algorithm are combined to diffusion the color image. Theoretical and experimental analyses both confirm the security and the validity of the proposed algorithm.

Shu-Chuan Chu

Papers

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

A Tabu Search Based Maximum Descent Algorithm for VQ Codebook Design

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method

An Efficient Image Encryption Scheme Based on ZUC Stream Cipher and Chaotic Logistic Map