Guest Editorial
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| Title | Guest Editorial |
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| Abstract | The Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In particular, the discipline focuses on the collective behavior that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems.We believe that the series of works in this special issue provide a useful reference for understanding new trends on swarm intelligent systems. In total, eleven papers have been selected to reflect the call thematic vision. The contents of these studies are briefly described as follows. Pulse coupled neural network (PCNN), a well-known class of neural networks, has original advantage when applied to image processing because of its biological background. However, when PCNN is used, the main problem is that its parameters are not self-adapting according to different image which limits the application range of PCNN. Considering that, in the paper entitled with ‘Particle Swarm Optimization for Automatic Parameters Determination of Pulse Coupled Neural Network’, X.Z.Xu et al. propose a new method based on particle swarm optimization (PSO) to determine automatically the parameters of PCNN. In this method, the algorithm of PSO is applied to search automatically optimum in the solution space of PCNN’s parameters until finding global optimal solution.Aiming at robot path planning in an environment with danger sources, in paper, ‘Multi-objective particle swarm optimization for robot path planning in environment with danger sources’, D.W.Gong, J.H.Zhang and Y.Zhang present a global path planning approach based on multi-objective particle swarm optimization. First, based on the environment map of a mobile robot described with a series of horizontal and vertical lines, an optimization model of the above problem including two indices, i.e. the length and the danger degree of a path, is established. Then, an improved multi-objective particle swarm optimization algorithm of solving the above model is developed. In this algorithm, a self-adaptive mutation operation based on the degree of a path blocked by obstacles is designed to improve the feasibility of a new path. To improve the performance of our algorithm in exploration, another archive is adopted to save infeasible solutions besides a feasible solutions archive, and the global leader of particles is selected from either the feasible solutions archive or the infeasible one. Moreover, a constrained Pareto domination based on the degree of a path blocked by obstacles is employed to update local leaders of a particle and the two archives.Non- linear complementarity problems are one general type of problems in economics and industry. In the paper, ‘A Mixed Algorithm for Nonlinear Complementarity Problems’, X.Y.Wang, Y.Wang and Y.Y.Wang propose a hybrid algorithm combining nonmonotone trust region algorith- ms and PSO methods, simulation results show it is effective.Due to the multi-variable, nonlinear, large time delay and strong coupling features of the wastewater treatment process, in the paper, ‘Recurrent High Order Neural Network Modeling for Wastewater Treatment Process’, J.F.Qiao and W.W.Yang use a recurrent high-order neural network to model the key water quality parameters(Chemical Oxygen Demand, Biological Oxygen Demand, Suspended Solid and Ammonia Nitrogen) for the wastewater treatment process, and the neural network is trained by an filtering algorithm. Operational data of a wastewater treatment plant is employed to illustrate the efficacy of the proposed modeling method. Meanwhile, the results are compared with feed-forward neural network and the general recurrent neural network to indicate the modeling accuracy of the recurrent high-order neural network.Particle swarm optimization (PSO) is a novel swarm intelligent algorithm inspired by fish schooling and birds flocking. Due to the complex nature of engineering optimization tasks, the standard version can not always meet the optimization requirements. Therefore, in the paper, ‘Newman-Watts Particle Swarm Optimization with Group Decision’, Z.H.Zhu introduces a new group decision mechanism into PSO to enhance the escaping capability from local optimum. Furthermore, a Watts Strogatz small-world model is incorporated into PSO to increase the population diversity.Seven famous numerical benchmarks are used to testify the new algorithm. Simulation results show it achieves the best performance when compared with three other variants of particle swarm optimization especially for multi-modal problems.The structure optimization of main beam is a nonlinear constrained optimization problem, which is important for bridge crane to save manufacturing cost on quality assurance. In the paper, ‘The Structure Optimization of Main Beam for Bridge Crane Based on An Improved PSO’, Chaoli Sun et al. use one modified particle swarm optimization (MPSO) with feasibility-based rules to optimize the structure of main beam in order to find the optimal parameters so as to make minimize the deadweight of main beam. The comparison results with the enumeration algorithm illustrated that MPSO can get best optimal solutions in much less calculation numbers.In the paper, ‘Evolutionary Neural Networks with Mixed-Integer Hybrid Differential Evolution’, Yung-Chin Lin et al. present a novel application to the optimization of neural networks. Here, the weight and architecture optimization of neural networks can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the neural network. Finally, the optimized neural network is applied to the prediction of chaotic time series. The satisfactory results are achieved, and demonstrate that the neural network optimized by MIHDE can effectively predict the chaotic time series.In the paper, ‘Fast Human Detection Using Motion Detection and Histogram of Oriented Gradients’, B.P.Hou and W.Zhu present a real-time Human detection algorithm based on HOG (Histograms of Oriented Gradients) features and SVM (Support Vector Machine) architecture. Motion detection is used to extract moving regions, which can be scanned by sliding windows; detecting moving region can subtract unnecessary sliding windows of static background regions under the surveillance conditions, then detection efficiency can be improved. Every sliding window is regarded as an individual image region and HOG features are calculated as classified eigenvectors. At last, the detected video objects can be categorized into pre-defined groups of humans and other objects by using SVM classifier. Experimental results from real-time video are provided which demonstrate the effectiveness of the method.In order to realize precision measurement of parts, in the paper, ‘Form Errors Evaluation Based on a Hybrid Optimization Algorithm’, K.Zhang and S.M.Wang provide a hybrid evaluation method. The hybrid global optimization algorithm based on ant colony optimization and simplex search method is proposed. The optimum model and the calculation process are introduced, where the planar straightness error is discussed as an example. The hybrid optimization algorithm can improve the efficiency and accuracy of searching in the whole field by gradually shrinking the area of optimization variable. Finally, a control experiment is carried out, and the calculation results by using different method such as the least square, simplex search, Powell optimum methods and GA, show that the hybrid evaluation method is feasible and satisfactory in the evaluation of form errors.Group search optimizer is a new population-based swarm intelligent algorithm inspired by the animal searching behavior. However, the exploitation capability is not very well. In the paper, ‘A Modified GSO Based on Limited Storage Quasi-Newton Method’, J.Y.Fang, Z.S.Zhong and W.Li incorporate the Limited Storage Quasi-Newton Method into group search optimizer (GSO) to increase the local search capability. To test the performance, it is applied to solve non-linear equations. Simulation results show it is effective.In the paper, ‘Mode III Stress Singularity Analysis of Isotropic and Orthotropic Bi-material near the Interface End’, the stress singularity near the Mode III interface end of isotropic and orthotropic bi-material was studied. Based on arbitrary angle boundary conditions, by solving a class of harmonic equations, J.L.Li et al. discusse the root of the characteristic equation with the help of complex function method of material fracture. The expression and variation of the singularity index of the tip of interfacial crack and the symmetric interface end is obtained as well. The analytical expressions of the stress and displacements fields and the stress intensity factor around the interfacial crack are derived.The guest editors of this special issue in Journal of Computers would like to thank all authors for submitting their interesting work. We are grateful to the reviewers for their great contributions to this special issue. This special issue have been supported by National Natural Science Foundation of China under Grant 61003053, the Key Project of Chinese Ministry of Education under Grant 209021. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2011-08-01 |
| Source | Journal of Computers Vol 6, No 8 (2011): Special Issue: Swarm Intelligent Systems: Theory and Applications |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |