PSO-based Algorithm Research Articles

A fast particle swarm optimization for clustering

This paper presents a high-performance method to reduce the time complexity of particle swarm optimization (PSO) and its variants in solving the partitional clustering problem. The proposed method works by adding two additional operators to the PSO-based algorithms. The pattern reduction operator is aimed to reduce the computation time, by compressing at each iteration patterns that are unlikely to change the clusters to which they belong thereafter while the multistart operator is aimed to improve the quality of the clustering result, by enforcing the diversity of the population to prevent the proposed method from getting stuck in local optima. To evaluate the performance of the proposed method, we compare it with several state-of-the-art PSO-based methods in solving data clustering, image clustering, and codebook generation problems. Our simulation results indicate that not only can the proposed method significantly reduce the computation time of PSO-based algorithms, but it can also provide a clustering result that matches or outperforms the result PSO-based algorithms by themselves can provide.

An Improved Particle Swarm Optimization Deployment for Wireless Sensor Networks

This paper addresses the issues associated with deployment of sensors, which are critical in wireless sensor networks. This paper provides an improved particle swarm optimization (PSO) algorithm by changing the basic form of PSO and introducing disturbance (d-PSO). By comparing with other PSO-based algorithms, simulation results show that the d-PSO algorithm provides a good-coverage solution with a satisfying coverage rate in a short time. This feature is especially useful for the rapid deployment of sensors.

Invulnerability Optimization of Chinese Railway Traffic Network for Suppressing Cascading Failure

Chinese Railway Traffic Network (CRTN) is one of the primary transport modes and artery for economic development, it contains three network models which are physical network, transport network and transfer network. This paper focuses on the transfer network which is established by setting the stations as nodes and the train running through the stations as edges. We optimize the invulnerability of CRTN by adding edges to the network, and evaluate it based on the natural connectivity. A Directed Chaosmutation Sorted Discrete PSO (DCSD-PSO) algorithm is proposed for obtaining the optimal edges which can maximize the natural connectivity based on mapping the candidate edges to a continuous domain. Subsequently, compared with Basic PSO (BPSO) algorithm, the proposed algorithm is proved to be more effective in improving the invulnerability of CRTN, and performs better when cascading failure occurs.

A Novel Collaborative Optimization Algorithm for Solving TSP

For the premature convergence and initial pheromone distribution problem of the basic ACO algorithm, PSO algorithm and chaos optimizing strategy are introduced into the ant colony algorithm in order to propose a novel collaborative optimization (CPACO) algorithm based on the collaboration theory. The first, the CPACO algorithm divides the ant colony into several subgroups, and the parameters of the subgroup are regarded as the particles. Then these advantages of PSO algorithm and chaos optimization strategy are fully utilized to optimize these parameters of the ACO algorithm in order to obtain the optimal values of these parameters. And the pheromone exchange operation is introduced into the subgroup. In order to validate the performance of the CPACO algorithm, the TSP problems are selected in here. The simulation results show that the proposed CPACO algorithm has better optimization performance than the traditional ACO algorithm.

A novel adjustment method for shearer traction speed through integration of T-S cloud inference network and improved PSO.

In order to efficiently and accurately adjust the shearer traction speed, a novel approach based on Takagi-Sugeno (T-S) cloud inference network (CIN) and improved particle swarm optimization (IPSO) is proposed. The T-S CIN is built through the combination of cloud model and T-S fuzzy neural network. Moreover, the IPSO algorithm employs parameter automation adjustment strategy and velocity resetting to significantly improve the performance of basic PSO algorithm in global search and fine-tuning of the solutions, and the flowchart of proposed approach is designed. Furthermore, some simulation examples are carried out and comparison results indicate that the proposed method is feasible, efficient, and is outperforming others. Finally, an industrial application example of coal mining face is demonstrated to specify the effect of proposed system.

Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.

Coverage Optimization of Hybrid Wireless Sensor Networks Based on Modified Particle Swarm Algorithm

A strategy for controlling mobile nodes based on PSO algorithm with neighborhood disturbance was proposed for improving the network coverage rate in wireless sensor networks. The non-dominated sorting strategy was led into basic PSO algorithm to seek best particle and adaptive neighborhood disturbance operation was used to conquer the drawback of PSO falling into local optimum. Therefore, the effect of network coverage had been improved and the network energy consumption can be reduced.

Financial Distress Study Based on PSO K-Means Clustering Algorithm and Rough Set Theory

The traditional financial distress method normally divided samples into two categories by healthy and bankruptcy. And the financial indicators are typically chosen without using a systematic and reasonable theory. To be more realistic, this paper selected all the companies in a certain industry as the research objects. Twenty-one financial indicators were primarily chosen as the condition attributes, reduction set was obtained by matrix reduction identification based on rough set theory. Then PSO-based clustering algorithm K-means was used to divide subjects into 5 categories of different financial status. The decision-making table was formed with the reduction set using the classification as a decision attribute. Finally, we tested the reasonableness of the classification and generated early warning rules together with rough set theory to evaluate the financial status of listed companies. The results showed that PSO-based K-means algorithm was able to reasonably classify companies, at the same time to overcome the subjective impacts in the artificial measure of financial crisis level. Data generated using this method agreed with the rough set theory for up to 87.0%, thus proving this method to be effective and feasible.

Efficient multi-feature PSO for fast gray level object-tracking

Robust and real-time moving object tracking is a tricky job in computer vision systems. The development of an efficient yet robust object tracker faces several obstacles, namely: dynamic appearance of deformable or articulated targets, dynamic backgrounds, variation in image intensity, and camera (ego) motion. In this paper, a novel tracking algorithm based on particle swarm optimization (PSO) method is proposed. PSO is a population-based stochastic optimization algorithm modeled after the simulation of the social behavior of bird flocks and animal hordes. In this algorithm, a multi-feature model is proposed for object detection to enhance the tracking accuracy and efficiency. The object's model is based on the gray level intensity. This model combines the effects of different object cases including zooming, scaling, rotating, etc. into a single cost function. The proposed algorithm is independent of object type and shape and can be used for many object tracking applications. Over 30 video sequences and having over 20,000 frames are used to test the developed PSO-based object tracking algorithm and compare it to classical object tracking algorithms as well as previously published PSO-based tracking algorithms. Our results demonstrate the efficiency and robustness of our developed algorithm relative to all other tested algorithms.

An efficient eye detection and tracking system based on particle swarm optimization and adaptive block-matching search algorithm

The problem of eye detection and tracking in video sequences is very important for a large number of applications ranging from face recognition to gaze tracking. Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this paper, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms the deformable template matching based methods such as genetic and PSO. Moreover, it achieves better performance compared to model-based methods such as the statistical active appearance model (AAM) method and the edge projections based method in terms of accuracy and computational complexity.

Multilevel Image Thresholding with Glowworm Swam Optimization Algorithm based on the Minimum Cross Entropy

The measure of cross entropy is a widely used index applied to many areas of computer science. In this article, we proposed a new multilevel thresholding algorithm for image segmentation using the glowworm swarm optimization based on the criterion of minimum cross entropy (MCE). In this paper, a new multilevel MCE thresholding (MCET) algorithm using the glowworm swam optimization (GSO) algorithm is proposed. The proposed image thresholding algorithm is called the GSO-based MCET algorithm. The five different methods including the exhaustive search, the honey bee mating optimization (HBMO), the firefly (FF) algorithm, the artificial bee colony algorithm (ABC) and the particle swarm optimization (PSO) are also implemented for performance comparison. The experimental results demonstrate that the proposed GSO-based MCET algorithm can efficiently search for multiple thresholds that are very close to the optimal ones examined by the exhaustive search method. Compared with the other five thresholding methods, the needs of computation time using the FF-based MCET algorithm is the smallest. Furthermore, the segmentation performance of GSO-based MCET algorithm is better than the PSO-based MCET algorithm, while the results of GSO-based MCET algorithm are not significant different to the other three bio-inspired computing algorithms.

Particle Swarm Algorithms to Solve Engineering Problems: A Comparison of Performance

In many disciplines, the use of evolutionary algorithms to perform optimizations is limited because of the extensive number of objective evaluations required. In fact, in real-world problems, each objective evaluation is frequently obtained by time-expensive numerical calculations. On the other hand, gradient-based algorithms are able to identify optima with a reduced number of objective evaluations, but they have limited exploration capabilities of the search domain and some restrictions when dealing with noncontinuous functions. In this paper, two PSO-based algorithms are compared to evaluate their pros and cons with respect to the effort required to find acceptable solutions. The algorithms implement two different methodologies to solve widely used engineering benchmark problems. Comparison is made both in terms of fixed iterations tests to judge the solution quality reached and fixed threshold to evaluate how quickly each algorithm reaches near-optimal solutions. The results indicate that one PSO algorithm achieves better solutions than the other one in fixed iterations tests, and the latter achieves acceptable results in less-function evaluations with respect to the first PSO in the case of fixed threshold tests.

Recursive Neural Networks Based on PSO for Image Parsing

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

RFID Indoor Localization Algorithm Based on PSO

In order to improve the positioning accuracy and reduce the localization cost, a kind of PSO-based RFID indoor localization algorithm is proposed in this paper. The main idea of this algorithm contains the following two aspects. First, due to the influence of none line of sight and multipath transmission in indoor environment, we adopt Gaussian Smoothing Filter to process Received Signal Strength Indicator (RSSI) values, which can reduce the impact of environmental factors on the position estimation effectively. Second, Particle of Swarm Optimization (PSO) algorithm is introduced to obtain a better positioning result. By experimenting in different indoor environment, the results demonstrate that the proposed approach can not only improve the precision of indoor localization, but has a lower cost and better robustness when compared to VIRE approach.

Parallel PSO-Based Optimal Strategy Study of Energy Efficient Operation Control for Train

Different from existing evolutionary algorithms which usually are implemented in serial computation mode, two improved parallel particle swarm optimization algorithms based on parallel computation structure model are proposed to search the energy efficient operation strategy for a train in railway network. The algorithms are designed and verified using a simulation case. Compared with other methods, these improved parallel PSO-based algorithms have a much better performance in efficiency and may be considered to put into practice.

Optimized Spectral Transformation for Detection and Classification of Buried Radioactive Materials

We investigate the detection and classification of buried radioactive materials of interest using data collected by a sodium iodide (NaI) detector with a short sensor dwell time (i.e., less than or equal to 1 s). The objective of detection is to detect a target from background and nontarget materials, while the objective of classification is to classify targets buried at different depths. Binned energy windows can reduce data dimensionality, help alleviate the negative impact from background, and suppress trivial spectral variations. However, the performance is sensitive to bin partition parameters including the number of bins and their bin widths. We have developed a particle swarm optimization (PSO)-based automatic system to determine these parameters. We also propose to apply a multiobjective PSO to optimize both the detection and classification accuracy simultaneously. The experimental results show that the PSO-based algorithm can outperform the Powell's direction set optimization method. The multiobjective PSO can achieve the balance between the two objectives, and it may provide even better individual accuracy than a single-objective PSO.

A Two-Phase Hybrid Particle Swarm Optimization Algorithm for Solving Permutation Flow-Shop Scheduling Problem

In this paper, a two-phase hybrid particle swarm optimization algorithm (PRHPSO) is proposed for the permutation flowshop scheduling problem (PFSP) with the minimizing makespan measure. The smallest position value (SPV) rule is used for encoding the particles that enable PSO for suitable PFSP, and the NEH and Tabu search algorithms are used for initializing the particles. In the first phase, the pattern reduction (PR) operator is used in the PSO algorithm for reducing the computation time. In order to avoid a premature convergence, a regeneration operator is used for escaping to the local optimal and balancing exploitation and exploration in the second phase. Additionally, a simulated annealing (SA) algorithm is utilized for local search to improve the best solution after the PSO search process. Finally, the results show that PRHPSO is significantly faster than two PSO-based algorithms and presents large-sized benchmarks. General Terms Hybrid Metaheuristic Algorithm, Combinatorial optimization.

PSO-Based Optimal Strategy Study of Train’s Energy Efficient Operation under Disturbed Condition

Instead of changeable chromosome length genetic algorithm, a new method based on particle swarm optimization algorithm is proposed to search a train’s energy efficient operation strategy under running disturbance condition in railway network. The algorithm is designed and validated using a referenced simulation case. Compared with other methods, it demonstrates this new PSO-based algorithm has a better result in efficiency and may be considered to use in practice.

A modified particle swarm approach for multi-objective optimization of laminated composite structures

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV

A new variant of particle swarm optimization (PSO), named phase angle-encoded and quantum-behaved particle swarm optimization (θ-QPSO), is proposed. Six versions of θ-QPSO using different mappings are presented and compared through their application to solve continuous function optimization problems. Several representative benchmark functions are selected as testing functions. The real-valued genetic algorithm (GA), differential evolution (DE), standard particle swarm optimization (PSO), phase angle-encoded particle swarm optimization ( θ-PSO), quantum-behaved particle swarm optimization (QPSO), and θ-QPSO are tested and compared with each other on the selected unimodal and multimodal functions. To corroborate the results obtained on the benchmark functions, a new route planner for unmanned aerial vehicle (UAV) is designed to generate a safe and flyable path in the presence of different threat environments based on the θ-QPSO algorithm. The PSO, θ-PSO, and QPSO are presented and compared with the θ-QPSO algorithm as well as GA and DE through the UAV path planning application. Each particle in swarm represents a potential path in search space. To prune the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. Experimental results demonstrated good performance of the θ-QPSO in planning a safe and flyable path for UAV when compared with the GA, DE, and three other PSO-based algorithms.