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

An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.

Discriminant Feature Fusion Strategy for Supervised Learning

A single-phase-to-ground fault location method based on convolutional deep belief network

The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.

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A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

This study proposes a novel DE variant for global optimizations based on both top collective information and p-best information (called CIpBDE). A combined mutation strategy (CIpBM) takes advantage of the mutation strategies “target-to-ci_pbest/1” and “target-to-pbest/1” is introduced trying to escape from stuck of local optima. A modified crossover operation (CIpBX) is proposed to handle the stagnation of DE. CIpBX adopts a collective vector or top p-best individual based on probability to execute crossover operation when stagnation occurs. An improved parameter adaptation strategy is figured out to adaptability to adjust the parameters crossover probability and scale factor value in each generation. To evaluate the performance of CIpBDE, comprehensive experiments are conducted on the CEC2013 benchmark test suit with 28 functions. Experimental results show that CIpBDE outperforms the seven state-of-the-art DE variants. What’s more, we also apply CIpBDE to the feature selection problem. Compared results on several standard data sets indicate that CIpBDE outperforms the four comparing algorithms in terms of classification accuracy.

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An Efficient Differential Evolution Via Both Top Collective and p-Best Information

This paper proposes a compact Differential Evolution (namely cED) for optimizing the deployment Wireless Sensor Network (WSN). The optimal scheme for deploying WSN should be a light and efficient algorithm because WSN limitations of size, memory, battery power, and computation. The proposed cDE uses a probabilistic model to generate candidate solutions for locating the promising area in search space. The solution of the population-based algorithm is expressed its distributed probability and is responding the order-one behavior for DE. So that cDE is a light and efficient tool that is suitable for deploying WSN. Simulation results are compared with the original and the other methods in the literature e.g. LEACH, LEACH-C, and HEED shows that the proposed method is the better performance regarding residual energy, nodes alive, and received items to save the energy of nodes.

Shu-Chuan Chu

Papers

Discriminant Feature Fusion Strategy for Supervised Learning

A single-phase-to-ground fault location method based on convolutional deep belief network

A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

An Efficient Differential Evolution Via Both Top Collective and p-Best Information

An Optimal Deployment Wireless Sensor Network Based on Compact Differential Evolution.