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

This paper proposes an equilibrium optimiser of interswarm interactive learning strategy to plan the shortest flight path of drones. This paper divide the population into a learning swarm and a lea...

Equilibrium optimizer of interswarm interactive learning strategy

Due to interference phenomena among constrained dimensions of the multimodal optimization or complex constrained optimization problems, a local optimum is easily converged, rather than for the expected global optimum. The enhanced diversity agent in optimal algorithms is one of the solutions to this issue. This paper proposes a novel optimization algorithm, namely BPO, based on the communication of the bees in artificial bee colony optimization (ABC), with the pollen in flower pollination algorithm (FPA) to solve the multimodal optimization problems. A new communication strategy for Bees and Pollens is presented to explore and exploit the diversity of the algorithm. Six multimodal benchmark functions are used to verify the convergent behavior, the accuracy, and the speed of the proposed algorithm. Experimental results show that the proposed scheme increases the accuracy more than the original algorithms.

Bees and Pollens with Communication Strategy for Optimization

The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm’s exploration capability and the solution’s quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm’s validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.

/pdf/improved-binary-grasshopper-optimization-algorithm-for-4cggv2wk.pdf

Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

Data hiding is a widely used technique to embed secrets in multimedia area so to achieve lower distortion and higher embedding capacity. Up to now, many methods which focus on solving these two problems have proposed constantly. Previous methods use various shaped shells to carry secret data in an image, but their schemes have high distortion of images and low capacity of carrying secrets due to their simple geometry layouts. We find out adjacent pixels have similar values, which means we can utilize this large area, and, therefore, we manipulate the data embedding and extracting on a difference-coordinate plan instead of the traditional pixel-coordinate plan. In this paper we propose a method aiming at solving these two problems mentioned above. This scheme embeds our secrets by using 3 pixels every time with the guidance of the flower-shaped reference matrix under a different coordinate system. The flower-shaped reference matrix combines three parts including petal matrix, calyx matrix and stamen matrix for secret embedding, which brings outstanding payload with good visual quality.

Data Hiding Scheme Based on A Flower-Shaped Reference Matrix.

Today, people can use various database techniques to discover useful knowledge from large collections of data. However, people also face the risk of disclosing sensitive information to competitor when the data is shared between different organizations. Thus, there is a balance between the legitimate mining need and protection of confidential knowledge when people release or share data. In this paper, we study the privacy preserving in association rule mining. A new distortion-based method was proposed which hides sensitive rules by removing some items in database so as to reduce the support or confidence of sensitive rules below specified thresholds. Aimed at minimizing side effects, the number of sensitive rules and the number of non-sensitive rules supported by each transaction are utilized to sort the transactions and the candidates which contain most sensitive rules and least non-sensitive rules are selected to modify. Comparative experiments on real datasets showed that the new method can achieve satisfactory results with fewer side effects and data loss.

Shu-Chuan Chu

Papers

Equilibrium optimizer of interswarm interactive learning strategy

Bees and Pollens with Communication Strategy for Optimization

Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

Data Hiding Scheme Based on A Flower-Shaped Reference Matrix.

Distortion-Based Heuristic Sensitive Rule Hiding Method --- The Greedy Way