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Using binary particle swarm optimization to search for maximal successful coalition

APPLIED INTELLIGENCE 2015年第2期42卷 195-209页

作者： Zhang, Guofu Yang, Renzhi Su, Zhaopin Yue, Feng Fan, Yuqi Qi, Meibin Jiang, Jianguo Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China

Coalitional Resource Games (CRGs) are a natural and formal framework in which agents wish to form coalitions to pool their scarce resources in order to achieve a set of goals that satisfy all members of a coalition. Thus far, many computational questions surrounding CRGs have been studied, but to our knowledge, a number of natural decision problems in CRGs have not been solved. Therefore, in this paper we investigate the possibility of using binary particle swarm optimization (BPSO) as a stochastic search process to search for Maximal Successful Coalition (MAXSC) in CRGs, which is a D (P) -complete problem. For this purpose, we develop a one-dimensional binary encoding scheme, propose strategies for encoding repair to ensure that each encoding in every iteration process is approximately valid and logicallsy consistent, and discuss some key properties of repair strategies. To evaluate the effectiveness of our algorithms, we compare them with the only other algorithm available in the literature for identifying MAXSC (due to Shrot, Aumann, and Kraus). The result shows that our algorithms are significantly faster especially for large-scale datasets.

关键词： Coalition formation Coalitional resource games Maximal successful coalition binary particle swarm optimization Encoding repair

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A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期219卷 224-233页

作者： Bin, Wei Qinke, Peng Jing, Zhao Xiao, Chen Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Sch Elect & Informat Engn Xian 710049 Peoples R China

Recently, nature-inspired algorithms have increasingly attracted the attention of researchers. Due to the fact that in BPSO the position vectors consisting of '0' and '1' can be seen as a decision behavior (support or oppose), in this paper, we propose a BPSO with hierarchical structure (BPSO_HS for short), on the basis of multi-level organizational learning behavior. At each iteration of BPSO_HS, particles are divided into two classes, named 'leaders' and 'followers', and different evolutionary strategies are used in each class. In addition, the mutation strategy is adopted to overcome the premature convergence and slow convergent speed during the later stages of optimization. The algorithm was tested on two discrete optimization problems (Traveling Salesman and Bin Packing) as well as seven real-parameter functions. The experimental results showed that the performance of BPSO_HS was significantly better than several existing algorithms. (C) 2012 Elsevier B.V. All rights reserved.

关键词： binary particle swarm optimization Multi-level organizational learning behavior Hierarchical structure Mutation strategy Evolutionary strategy

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Haplotype inference using a novel binary particle swarm optimization algorithm

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APPLIED SOFT COMPUTING 2014年 21卷 415-422页

作者： Bin, Wei Jing, Zhao Xi An Jiao Tong Univ Sch Elect & Informat Engn State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

The knowledge of haplotypes allows researchers to identify the genetic variation affecting phenotypic such as health, disease and response to drugs. However, getting haplotype data by experimental methods is both time-consuming and expensive. Haplotype inference (HI) from the genotypes is a challenging problem in the genetics domain. There are several models for inferring haplotypes from genotypes, and one of the models is known as haplotype inference by pure parsimony (HIPP) which aims to minimize the number of distinct haplotypes used. The HIPP was proved to be an NP-hard problem. In this paper, a novel binary particle swarm optimization (BPSO) is proposed to solve the HIPP problem. The algorithm was tested on variety of simulated and real data sets, and compared with some current methods. The results showed that the method proposed in this paper can obtain the optimal solutions in most of the cases, i.e., it is a potentially powerful method for HIPP. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Haplotype inference Pure parsimony Genotypes binary particle swarm optimization

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A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation

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PATTERN RECOGNITION LETTERS 2015年第Jan.15期52卷 94-100页

作者： Banka, Haider Dara, Suresh Indian Sch Mines Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

Gene expression data typically contain fewer samples (as each experiment is costly) and thousands of expression values (or features) captured by automatic robotic devices. Feature selection is one of the important and challenging tasks for this kind of data where many traditional methods failed and evolutionary based methods were succeeded. In this study, the initial datasets are preprocessed using a quartile based fast heuristic technique to reduce the crude domain features which are less relevant in categorizing the samples of either group. Hamming distance is introduced as a proximity measure to update the velocity of particle(s) in binary PSO framework to select the important feature subsets. The experimental results on three benchmark datasets vis-A-vis colon cancer, defused B-cell lymphoma and leukemia data are evaluated by means of classification accuracies and validity indices as well Detailed comparative studies are also made to show the superiority and effectiveness of the proposed method. The present study clearly reveals that by choosing proper preprocessing method, fine tuned by HDBPSO with Hamming distance as a proximity measure, it is possible to find important feature subsets in gene expression data with better and competitive performances. (C) 2014 Published by Elsevier B.V.

关键词： Feature selection Hamming distance High dimensional data binary particle swarm optimization Classification Stability indices

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Optimized Fuzzy Logic-Based Fire Monitoring in Underground Coal Mines: binary particle swarm optimization Approach

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IEEE SYSTEMS JOURNAL 2020年第2期14卷 3039-3046页

作者： Muduli, Lalatendu Mishra, Devi Prasad Jana, Prasanta K. Indian Sch Mines Dept Comp Sci & Engn Indian Inst Technol Dhanbad 826004 Bihar India Indian Sch Mines Dept Min Engn Indian Inst Technol Dhanbad 826004 Bihar India

Underground coal mines are susceptible to many kinds of hazards. Mine fire is one of the most vulnerable threats that leads to loss of human lives and other resources inside the mines. Hence, continuous monitoring of underground coal mines is very important for early detection of mine fire. Nowadays, wireless sensor networks are being widely used for monitoring underground coal mines. However, the collected data from sensor nodes are inaccurate in nature, and there is a need for analyzing such data for taking preventive measure. In this article, we propose an optimized fuzzy logic-based fire monitoring system for wireless underground sensor network, which strengthens reliability of making decision in preventing mine fire. However, storing of large fuzzy rule set in the memory constraint sensor nodes and their traversal for every sensor reading are very challenging. Therefore, we use the binary particle swarm optimization (BPSO) algorithm to optimize the proposed fuzzy system that eliminates redundant rules but preserves event detection accuracy of the monitoring system. The proposed system is simulated using BPSO algorithm and Fuzzy Logic Toolbox inbuilt in the MATLAB. The simulation results demonstrate that the proposed system outperforms the existing monitoring systems for underground coal mines.

关键词： Monitoring Coal mining Event detection Fuel processing industries Fuzzy systems Fuzzy logic binary particle swarm optimization fire monitoring fuzzy logic underground coal mines wireless underground sensor networks

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Towards optimized binary pattern generation for grayscale digital halftoning: A binary particle swarm optimization (BPSO) approach

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2012年第8期23卷 1245-1259页

作者： Chatterjee, Arpitam Tudu, Bipan Paul, Kanai Ch. Jadavpur Univ Dept Printing Engn Kolkata 700032 India Jadavpur Univ Dept Instrumentat & Elect Engn Kolkata 700032 India

Grayscale digital halftoning produces bi-level representation of original continuous tone images. This process plays pivotal role for devices like printers, plasma panels, LCD displays, etc. The bi-level images can be considered as binary images where '0' and '1' correspond to black and white, respectively. This paper investigates potential of binary particle swarm optimization (BPSO) to generate faithful binary halftone patterns. The cost function addresses important characteristics of original images and pleasant visual appearance of halftone images. The paper also shows the application of pattern look-up-table (p-LUT) approach to address the high processing time of BPSO optimization and simple gradient-based edge enhancement for improved edge retention. Results are evaluated subjectively by statistical measures and psychovisual test. Results are evaluated objectively using image quality evaluation metrics as well. The comparisons with state-of-the-art techniques are also drawn. The evaluation results along with the comparisons show the competitive potential of the presented technique. (c) 2012 Elsevier Inc. All rights reserved.

关键词： Digital halftoning particle swarm optimization binary particle swarm optimization Pattern LUT Edge-enhanced halftone Blue-noise halftoning Green-noise halftoning Optimized binary halftone

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A new intelligent fault diagnosis method for bearing in different speeds based on the FDAF-score algorithm, binary particle swarm optimization, and support vector machine

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SOFT COMPUTING 2020年第13期24卷 10005-10023页

作者： Chegini, Saeed Nezamivand Bagheri, Ahmad Najafi, Farid Univ Guilan Fac Mech Engn Rasht Iran

In this paper, a new hybrid intelligent technique is presented based on the improvement in the feature selection method for multi-fault classification. The bearing conditions used in this study include healthy condition, defective inner ring, defective outer ring, and the faulty rolling element at different rotating motor speeds. To form the feature matrix, at first, the vibration signals are decomposed using empirical mode decomposition and wavelet packet decomposition. Then, the time and frequency domain features are extracted from the raw signals and the components are obtained from the signal decomposition. The high-dimensional feature matrix leads to increasing the computational complexity and reducing the efficiency in the classification accuracy of faults. Therefore, in the first stage of the feature selection process, the redundant and unnecessary features are eliminated by the FDAF-score feature selection method and the preselected feature set is formed. The FDAF-score technique is a combination of both F-score and Fisher discriminate analysis (FDA) algorithms. Since there may exist the features that are not susceptible to the presence of faults, the binary particle swarm optimization (BPSO) algorithm and the support vector machine (SVM) are used to select the optimal features from the preselected features. The BPSO algorithm is used to determine the optimal feature set and SVM classifier parameters so that the predictive error of the bearing conditions and the number of selected features are minimized. The results obtained in this paper demonstrate that the selected features are able to differentiate the different bearing conditions at various speeds. Comparing the results of this article with other fault detection methods indicates the ability of the proposed method.

关键词： Bearing fault detection Feature extraction FDAF-score method binary particle swarm optimization Support vector machine

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Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients

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RENEWABLE ENERGY 2013年 55卷 266-276页

作者： Pookpunt, Sittichoke Ongsakul, Weerakorn Asian Inst Technol Energy Field Study Sch Environm Resources & Dev Pathum Thani 12120 Thailand

This paper proposes a binary particle swarm optimization (BPSO) with time-varying acceleration coefficients (TVAC) for solving optimal placement of wind turbines within a wind farm. The objective is to extract the maximum turbine power output in a minimum investment cost within a wind farm. The BPSO-TVAC algorithm is applied to 100 square cells test site considering uniform wind and non-uniform wind speed with variable direction characteristics. Linear wake model is used to calculate downstream wind speed. Test results indicate that BPSO-TVAC investment cost per installed power of both uniform and non-uniform wind speed with variable wind direction are lower than those obtained from genetic algorithm and evolutive algorithm, BPSO-TVIW (time-varying inertia weight factor), BPSO-RANDIW (random inertia weight factor) and BPSO-RTVIWAC (random time-varying inertia weight and acceleration coefficients), leading to maximum power extracted in a least investment cost manner. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Wind farm Wind turbine Placement design binary particle swarm optimization

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Accelerating Analytics Using Improved binary particle swarm optimization for Discrete Feature Selection

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COMPUTER JOURNAL 2022年第10期65卷 2547-2569页

作者： Moorthy, Rajalakshmi Shenbaga Pabitha, P. St Josephs Inst Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India Anna Univ Chennai Madras Inst Technol Campus Dept Comp Technol Chennai 600044 Tamil Nadu India

Feature selection, a combinatorial optimization problem, remains broadly applied in the area of Computational Learning with the aim to construct a model with reduced features so as to improve the performance of the model. Feature selection algorithm aims to identify admissible subgroup of features without sacrificing the accuracy of the model. This research works uses Improved binary particle swarm optimization (IBPSO) to optimally identify subset of features. The problem of stagnation, trapping in local optima and premature convergence of binary particle swarm optimization (BPSO) for solving discrete feature selection dispute has been tackled using IBPSO. IBPSO prevents the model from overfitting and also takes less computational time for constructing the model because of reduced feature subset. The sine function, cosine function, position of the random particle and linear decrement of inertial weight are integrated in IBPSO, which balances between exploration and exploitation to identify optimal subset of features. The linear decrement of inertial weight tends to do good level of exploration at the starting phase, whereas at the end it tends to exploit solution space to find the optimal subset of features that are more informative and thereby discarding redundant and irrelevant features. Experimentation is carried out on seven benchmarking datasets obtained from University of California, Irvine repository, which includes various real-world datasets for processing with machine learning algorithms. The proposed IBPSO is compared with conventional metaheuristic algorithms such as BPSO, Simulated Annealing, Ant Colony optimization, Genetic Algorithm and other hybrid metaheuristic feature selection algorithms. The result proves that IBPSO maximizes the accuracy of the classifier together with maximum dimensionality reduction ratio. Also, statistical tests such as T-test, Wilcoxon signed-pair test are also carried out to demonstrate IBPSO is better than other algorithms

关键词： feature selection binary particle swarm optimization Improved binary particle swarm optimization accuracy dimension reduction statistical test

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Holistic and partial facial features fusion by binary particle swarm optimization

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NEURAL COMPUTING & APPLICATIONS 2008年第5-6期17卷 481-488页

作者： Pu, Xiaorong Yi, Zhang Fang, Zhongjie Univ Elect Sci & Technol China Sch Comp Sci & Engn Computat Intelligence Lab Chengdu 610054 Peoples R China

This paper proposes a novel binary particle swarm optimization (PSO) algorithm using artificial immune system (AIS) for face recognition. Inspired by face recognition ability in human visual system (HVS), this algorithm fuses the information of the holistic and partial facial features. The holistic facial features are extracted by using principal component analysis (PCA), while the partial facial features are extracted by non-negative matrix factorization with sparseness constraints (NMFs). Linear discriminant analysis (LDA) is then applied to enhance adaptability to illumination and expression. The proposed algorithm is used to select the fusion rules by minimizing the Bayesian error cost. The fusion rules are finally applied for face recognition. Experimental results using UMIST and ORL face databases show that the proposed fusion algorithm outperforms individual algorithm based on PCA or NMFs.

关键词： face recognition fusion multimodal biometrics principal component analysis nonnegative matrix factorization binary particle swarm optimization artificial immune system

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