Shengwu Xiong

FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices

Defining a relation between granules and computing ever-changing granules are two important issues in granular computing. In view of this, this work proposes a partial order relation and lattice computing, respectively, for dealing with the aforementioned issues. A fuzzy lattice granular computing classification algorithm, or FL-GrCCA for short, is proposed here in the framework of fuzzy lattices. Algorithm FL-GrCCA computes a fuzzy inclusion relation between granules by using an inclusion measure function based on both a nonlinear positive valuation function, namely arctan, and an isomorphic mapping between lattices. Changeable classification granules are computed with a dilation operator using, conditionally, both the fuzzy inclusion relation between two granules and the size of a dilated granule. We compare the performance of FL-GrCCA with the performance of popular classification algorithms, including support vector machines (SVMs) and the fuzzy lattice reasoning (FLR) classifier, for a number of two-class problems and multi-class problems. Our computational experiments showed that FL-GrCCA can both speed up training and achieve comparable generalization performance.

Multi-ant colony system for evacuation routing problem with mixed traffic flow

Evacuation routing problem with mixed traffic flow is complex due to the interaction among different types of evacuees. The positive feedback mechanism of single ant colony system may lead to congestion on some optimum routes. Like different ant colony systems in nature, different components of traffic flow compete and interact with each other during evacuation process. In this paper, an approach based on multi-ant colony system was proposed to tackle evacuation routing problem with mixed traffic flow. Total evacuation time is minimized and traffic load of the whole road network is balanced by this approach. The experimental results show that this approach based on multi-ant colony system can obtain better solutions than single ant colony system and solve mixed traffic flow evacuation problem with reasonable routing plans.

A conflict-congestion model for pedestrian-vehicle mixed evacuation based on discrete particle swarm optimization algorithm

A simulation model based on temporal-spatial conflict and congestion for pedestrian-vehicle mixed evacuation has been investigated. Assuming certain spatial behaviors of individuals during emergency evacuation, a discrete particle swarm optimization with neighborhood learning factor algorithm has been proposed to solve this problem. The proposed algorithm introduces a neighborhood learning factor to simulate the sub-group phenomenon among evacuees and to accelerate the evacuation process. The approach proposed here is compared with methods from the literatures, and simulation results indicate that the proposed algorithm achieves better evacuation efficiency while maintaining lower pedestrian-vehicle conflict levels.

The Incremental Probabilistic Neural Network

With the development of the Internet, the Intrusion Detection has been gradually playing a more and more important role in Network Security. Radial Basis Function Neural Network are widely used in Intrusion Detection, especially Probabilistic Neural Network. However, the detection speed is a problem which impedes it to be applied to Real-time Intrusion Detection. In this paper, for increasing the Detection Speed, the Incremental Training Method replaces the Exact Training Method. The simulation experiment shows that the detection speed of Incremental Probabilistic Neural Network is much faster than that of Exact Probabilistic Neural Network. Therefore, the Incremental Probabilistic Neural Network is more suitable for real-time intrusion detection than Exact Probabilistic Neural Network.

A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.

Multitarget tracking of pedestrians in video sequences based on particle filters

Video target tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in target tracking for nonlinear and non-Gaussian estimation problems. Although most existing algorithms are able to track targets well in controlled environments, it is often difficult to achieve automated and robust tracking of pedestrians in video sequences if there are various changes in target appearance or surrounding illumination. To surmount these difficulties, this paper presents multitarget tracking of pedestrians in video sequences based on particle filters. In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. During the process of tracking, the algorithm uses the similarity between candidates and codebooks as observation likelihood function and processes severe occlusion condition to prevent drift and loss phenomenon caused by target occlusion. Experimental results demonstrate that our algorithm improves the tracking performance in complicated real scenarios.

Subspace Clustering Mutation Operator for Developing Convergent Differential Evolution Algorithm

Many researches have identified that differential evolution algorithm (DE) is one of the most powerful stochastic real-parameter algorithms for global optimization problems. However, a stagnation problem still exists in DE variants. In order to overcome the disadvantage, two improvement ideas have gradually appeared recently. One is to combine multiple mutation operators for balancing the exploration and exploitation ability. The other is to develop convergent DE variants in theory for decreasing the occurrence probability of the stagnation. Given that, this paper proposes a subspace clustering mutation operator, called SC_qrtop. Five DE variants, which hold global convergence in probability, are then developed by combining the proposed operator and five mutation operators of DE, respectively. The SC_qrtop randomly selects an elite individual as a perturbation’s center and employs the difference between two randomly generated boundary individuals as a perturbation’s step. Theoretical analyses and numerical simulations demonstrate that SC_qrtop prefers to search in the orthogonal subspace centering on the elite individual. Experimental results on CEC2005 benchmark functions indicate that all five convergent DE variants with SC_qrtop mutation outperform the corresponding DE algorithms.

Prediction based multi-strategy differential evolution algorithm for dynamic environments

Many real world optimization problems are dynamic optimization problems (DOPs) whose optima change over time. In this paper, we propose new variants of differential evolution (DE) to solve DOPs. A hybrid method that combines population core based multi-population strategy and prediction strategy and new local search scheme is introduced into DE to enhance its performance for solving DOPs. The population core based multi-population strategy is useful to maintain the diversity of population by using the multi-population and population core concept. The prediction strategy is useful to rapidly adapt to the dynamic environment by using the prediction area. The local search scheme is useful to improve the searching accuracy by suing the new chaotic local search method. Experimental results on the moving peaks benchmark show that the proposed schemes enhance the performance of DE in the dynamic environments.

Particle swarm and NSGA-II based evacuation simulation and multi-objective optimization

Because of the high-dense population and complex structure, the large public building faces a unique challenge in developing effective emergency evacuation plans. And due to the large scale and numbers of evacuees in real evacuation, real tests are impractical. Therefore, the simulation of evacuation becomes a wonderful choice in program planning. Particle Swarm is as one of the multi-agent based simulation method that can simulate complex behaviors of individuals. NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a kind of optimization method for multi-objective optimization problem. In this paper, we propose a novel multi-objective evolutionary algorithm (named as PNMO, Particle swarm & NSGA-II based Multi-objective Optimization) which simulates evacuation process as well as optimizing the generated evacuation plans. The experiment shows that this method possesses superior performance in evacuation planning.

Behavior characteristics of mixed traffic flow on campus

Campus security is an important part of social security in China. As reported in exist literature, very limited efforts are made to study mixed traffic flow behavior on campus. Present study attempts to highlight studies of single traffic flow or pedestrian-vehicle traffic flow. This paper deals with the research into the analysis of the characteristics of mixed traffic flow on campus, including cars, motorbikes, bicycles, and pedestrians. Total 440 minutes video data on two different locations on campus are extracted by employing videographic technique. The research is designed determine factors for traffic flow variety. Fluctuations in traffic flow depends on the student schedules, particularly during the peak time as there are large pedestrian flow and bicycle flow in short interval time. At the same time, a spatial-temporal analysis for establishing the relationship about mixed traffic flows is discussed. Flow models of speed-flow, speed-occupancy, flow-occupancy about mixed traffic are developed to illustrate behavior characteristics of mixed traffic stream on campus of different dimensions. The results obtained are significant for evacuation simulation and planning under various conditions on campus.