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Particle Swarm Optimization: Advances, Applications, and Experimental Insights

Computers, Materials & Continua 2025年第2期82卷 1539-1592页

作者： Laith Abualigah Computer Science Department Al al-Bayt UniversityMafraq25113Jordan

Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different *** paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other *** six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the *** results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of *** results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the *** review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.

关键词： Particle swarm optimization(PSO) optimization algorithms data mining machine learning engineer-ing design energy systems healthcare applications robotics comparative analysis algorithm performance evaluation

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CLASSIFICATION-algorithm evaluation - 5 performance-MEASURES BASED ON CONFUSION MATRICES

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JOURNAL OF CLINICAL MONITORING 1995年第3期11卷 189-206页

作者： FORBES, AD Medical Department Hewlett-Packard Laboratories Palo Alto

Objective. The objective of this paper is to introduce, explain, and extend methods for comparing the performance of classification algorithms using error tallies obtained on properly sized, populated, and labeled data sets. Methods. Two distinct contexts of classification are defined, involving ''objects-by-inspection'' and ''objects-by-segmentation.'' In the former context, the total number of objects to be classified is unambiguously and self-evidently defined. In the latter, there is troublesome ambiguity. All five of the measures of performance here considered are based on confusion matrices, tables of counts revealing the extent of an algorithm's ''confusion'' regarding the true classifications. A proper measure of classification-algorithm performance must meet four requirements. A proper measure should obey six additional constraints. Results. Four traditional measures of performance are critiqued in terms of the requirements and constraints. Each measure meets the requirements, but fails to obey at least one of the constraints. A nontraditional measure of algorithm performance, the normalized mutual information (NMI), is therefore introduced. Based on the NMI, methods for comparing algorithm performance using confusion matrices are devised. Conclusions. The five performance measures lead to similar inferences when comparing a trio of QRS-detection algorithms using a large data set. The modified NMI is preferred, however, because it obeys each of the constraints and is the most conservative measure of performance.

关键词： algorithm performance evaluation CONFUSION MATRIX MEASURES OF AGREEMENT MEASURES OF ASSOCIATION MUTUAL INFORMATION

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Parallel Simulation of Superscalar Scheduling 43

Parallel Simulation of Superscalar Scheduling

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43rd Annual International Conference on Parallel Processing (ICPP)

作者： Haugen, Blake Kurzak, Jakub YarKhan, Asim Luszczek, Piotr Dongarra, Jack Univ Tennessee Knoxville Innovat Comp Lab Knoxville TN 37996 USA Univ Manchester Oak Ridge Natl Lab Manchester M13 9PL Lancs England

ISBN: (纸本)9781479956180

Computers have been moving toward a multicore paradigm for the last several years. As a result of the recent multicore paradigm shift, software developers must design applications that exploit the inherent parallelism of modern computing architectures. One of the areas of research to simplify this shift is the development of dynamic scheduling utilities that allow the developer to specify serial code that can be parallelized using a library or compiler technology. While these tools certainly increase the developer's productivity, they can obfuscate performance bottlenecks. For this reason, it is important to evaluate algorithm performance in order to ensure that the performance of a given algorithm is being realized using dynamic scheduling utilities. This paper presents the methodology and results of a new performance analysis tool that aims to accurately simulate the performance of various superscalar schedulers, including OmpSs, StarPU, and QUARK. The process begins with careful timing of each of the computational routines that make up the algorithm. The simulation tool then uses the timing of the computational kernels in conjunction with the dependency management provided by the superscalar scheduler in order to simulate the execution time of the algorithm. This tool demonstrates that simulation results of various algorithms can accurately predict the performance of a complex dynamic scheduling system.

关键词： multiprocessing systems parallel processing performance evaluation processor scheduling OmpSs QUARK StarPU algorithm performance evaluation complex dynamic scheduling system performance prediction computational kernels computational routines computing architectures dependency management dynamic scheduling utility development execution time simulation multicore paradigm parallel simulation tool parallelism serial code superscalar scheduling Computational modeling Kernel Libraries Linear algebra Multicore processing Parallel processing performance modeling simulation

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A Method of Measuring Quality of algorithm in Stream Structure from Motion Applied to Aerial Photography 19

A Method of Measuring Quality of Algorithm in Stream Structu...

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19th IEEE International Conference on Soft Computing and Measurements (SCM)

作者： Stoyanov, Dmitriy Yu. Filchenkov, Andrey A. Gedertsev, Anton S. ITMO Univ Comp Technol Dept St Petersburg Russia STC Ltd St Petersburg Russia

ISBN: (纸本)9781467389198

Structure from Motion problem has many solutions nowadays. However, only a small number of them try to work in real-time restrictions, where reconstruction can be not perfect, but must be obtained in very short time. We introduce stream SfM problem, in which input is a stream images, and output us a stream of reconstructions corresponding to each time moment. To solve this problem, we start from developing a method that can measure results of stream SfM algorithms. It must pay equal attention to the quality of reconstruction and speed of its estimation. With focus on SfM applied to aerial photography, we propose such measuring method and use it to evaluate a bunch of algorithms, including one, which we develop specially for this quality measuring.

关键词： Structure from motion stream computation algorithm performance evaluation image processing

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Application of cloud models in digital libraries

Application of cloud models in digital libraries

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6th International Conference on Machine Learning and Cybernetics

作者： Kang, Hai-Yan Li, Yan-Fang Tang, Shi-Ping Beijing Informat Sci & Technol Univ Dept Comp Informat Syst Beijing 100101 Peoples R China Beijing Normal Univ Sch Management Beijing 100875 Peoples R China Beijing Inst Technol Comp Sch Beijing 100081 Peoples R China

ISBN: (纸本)9781424409723

In digital libraries, the traditional retrieval methods cannot efficiently retrieve uncertain concepts, and traditional evaluation methods have disadvantages on algorithm measures. To overcome above disadvantages, this paper proposes two new methods based on cloud models. One is to retrieve efficiently uncertain concepts, which can change the granularity of retrieval information. By this method it remarkably improves the recall and precision of information retrieval. Another is to evaluate efficiently retrieval algorithm, which can reflect not only average performance of an algorithm but also stability and randomicity. These two methods set up a transform of qualitative concepts and quantity. This kind of transform is carried out through strict mathematic means. Experimental data showed the method is practical. Results of evaluation will be more accurate and approach to the fact better.

关键词： digital libraries(DLs) information retrieval(IR) uncertain concept cloud model algorithm performance evaluation

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A Genetic Programming Approach for Fraud Detection in Electronic Transactions

A Genetic Programming Approach for Fraud Detection in Electr...

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IEEE Symposium on Computational Intelligence in Cyber Security

作者： Assis, Carlos A. S. Pereira, Adriano C. M. Pereira, Marconi A. Carrano, Eduardo G. Ctr Fed Educ Tecnol Minas Gerais PPGMMC Belo Horizonte MG Brazil Univ Fed Minas Gerais DCC Belo Horizonte MG Brazil Univ Fed Sao Joao Del Rei DETCH Ouro Branco MG Brazil Univ Fed Minas Gerais DEE Belo Horizonte MG Brazil

ISBN: (纸本)9781479945214

The volume of online transactions has increased considerably in the recent years. Consequently, the number of fraud cases has also increased, causing billion dollar losses each year worldwide. Therefore, it is mandatory to employ mechanisms that are able to assist in fraud detection. In this work, it is proposed the use of Genetic Programming (GP) to identify frauds (charge back) in electronic transactions, more specifically in online credit card operations. A case study, using a real dataset from one of the largest Latin America electronic payment systems, has been conducted in order to evaluate the proposed algorithm. The presented algorithm achieves good performance in fraud detection, obtaining gains up to 17% with regard to the actual company baseline. Moreover, several classification problems, with considerably different datasets and domains, have been used to evaluate the performance of the algorithm. The effectiveness of the algorithm has been compared with other methods, widely employed for classification. The results show that the proposed algorithm achieved good classification effectiveness in all tested instances.

关键词： credit transactions electronic commerce fraud genetic algorithms pattern classification transaction processing GP algorithm Latin America electronic payment systems algorithm performance evaluation charge back classification problems company baseline electronic transactions fraud detection fraud identification genetic programming approach online credit card operations online transactions Accuracy Companies Credit cards Genetic programming Sociology Statistics Training Genetic algorithm (GA) Fraud vandalism online transactions Credit cards credit transactions forgery Sociology electronic commerce Pattern recognition Companies Functional training

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Towards Efficient algorithms for Compressed Sparse-Sparse Matrix Product 15

Towards Efficient Algorithms for Compressed Sparse-Sparse Ma...

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15th International Conference on High performance Computing & Simulation (HPCS)

作者： Ezouaoui, Sana Hamdi-Larbi, Olfa Mahjoub, Zaher Univ Tunis El Manar Fac Sci Tunis URAPOP Res Team Univ Campus 2092 Manar 2 Tunis Tunisia

ISBN: (纸本)9781538632505

We study the sparse matrix product problem where the input matrices are sparse. Starting with an original DO-loop nest structured algorithm, different versions involving body kernels such as GAXPY, AXPY and DOT are generated by the loop interchange technique. We particularly focus on the GAXPY-Row body kernel where the matrices are acceded row-wise. Various versions corresponding to the most used sparse matrix compression formats are designed. We then derive other versions by applying improving techniques such as loop invariant motion and loop unrolling. A theoretical multi-fold performance study permits to establish accurate comparisons between the different versions. Our contribution is validated through experiments achieved on two input sets i.e. a set of randomly generated matrices and a set of benchmark matrices of different sizes and densities. This permitted to notice that the improvement procedure led to an efficient version dramatically reducing the run time up to 98%. Our algorithms were also compared with kernels from NIST Sparse Blas, CSparse and SPARSKIT2 libraries.

关键词： algorithm performance evaluation compressing/ storing format loop body kernel sparse matrix product

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A simplified approach for efficiency analysis of machine learning algorithms

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2418-e2418页

作者： Sivakumar, Muthuramalingam Parthasarathy, Sudhaman Padmapriya, Thiyagarajan Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India Thiagarajar Coll Engn Dept Appl Math & Computat Sci Madurai Tamil Nadu India

The efficiency of machine learning (ML) algorithms plays a critical role in their deployment across various applications, particularly those with resource constraints or real-time requirements. This article presents a comprehensive framework for evaluating ML algorithm efficiency by incorporating metrics, such as training time, prediction time, memory usage, and computational resource utilization. The proposed methodology involves a multistep process: collecting raw metrics, normalizing them, applying the Analytic Hierarchy Process (AHP) to determine weights, and computing a composite efficiency score. We applied this framework to two distinct datasets: medical image data and agricultural crop prediction data. The results demonstrate that our approach effectively differentiates algorithm performance based on the specific demands of each application. For medical image analysis, the framework highlights strengths in robustness and adaptability, whereas for agricultural crop prediction, it emphasizes scalability and resource management. This study provides valuable insights into optimizing ML algorithms, and offers a versatile tool for practitioners to assess and enhance algorithmic efficiency across diverse domains.

关键词： Machine learning efficiency Composite efficiency score Analytic Hierarchy Process (AHP) Metric normalization algorithm performance evaluation Medical image analysis Agricultural data prediction

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APPLICATION OF CLOUD MODELS IN DIGITAL LIBRARIES

APPLICATION OF CLOUD MODELS IN DIGITAL LIBRARIES

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： HAI-YAN KANG YAN-FANG LI SHI-PING TANG Department of Computer Information System Beijing Information Science&Technology University Beijing School of management Beiing Normal University Beijing 100875 China Computer School Beijing Institute of Technology Beijing 100081 China

In digital libraries, the traditional retrieval methods cannot efficiently retrieve uncertain concepts, and traditional evaluation methods have disadvantages on algorithm *** overcome above disadvantages, this paper proposes two new methods based on cloud *** is to retrieve efficiently uncertain concepts, which can change the granularity of retrieval *** this method it remarkably improves the recall and precision of information *** is to evaluate efficiently retrieval algorithm, which can reflect not only average performance of an algorithm but also stability and *** two methods set up a transform of qualitative concepts and *** kind of transform is carried out through strict mathematic *** data showed the method is *** of evaluation will be more accurate and approach to the fact better.

关键词： Digital Libraries(DLs) Information Retrieval(IR) Uncertain concept Cloud model algorithm performance evaluation

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