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1
Mary, A. Viji Amutha, and Dr T. Jebarajan Dr. T. Jebarajan. "Performance Metrics of Clustering Algorithm." Indian Journal of Applied Research 4, no. 8 (2011): 165–67. http://dx.doi.org/10.15373/2249555x/august2014/47.
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Han, Xiaofeng, Xuejia Qi, Jincheng Zhang, Songlin Yang, and Shuling Chen. "The Analysis Method of Performance Comprehensive Performance Optimization Design for Multi-body Unmanned Boat." Journal of Physics: Conference Series 2219, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1742-6596/2219/1/012048.
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Abstract In order to compare and analyze the comprehensive performance of multi-hull unmanned boats under specific conditions, this paper conducts in-depth research on the multi-objective decision-making theory and intelligent optimization algorithms of boat type design. First of all, this paper establishes the corresponding ship type and resistance database using the response surface fitting method through the drag resistance test of the ship model. Secondly, comprehensively considering the impact of the three major performances of multi-hull unmanned craft on the design of the boat’s speed, maneuverability and seakeeping, a comprehensive optimization mathematical model was established, and the comprehensive optimization design software was adapted. Then, using genetic algorithm, particle swarm algorithm, and chaos algorithm to conduct a preliminary calculation and analysis of the comprehensive performance of the two ship types. By comparing the calculation results, it is concluded that the particle swarm algorithm is the best algorithm among the three intelligent algorithms. Finally, the particle swarm algorithm is used to compare and analyze the comprehensive performance of the two ship types in the multi-hull unmanned craft. It is concluded that the overall performance of the three-hull unmanned craft is better at the same speed and displacement. This paper discusses the pros and cons of the three intelligent algorithms, compares and analyzes the calculation results of each optimization algorithm, and provides a practical way and method for the selection of multihull unmanned boats under the same speed and same displacement.
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Qiu, Jian Lin, Li Chen, Jian Ping Chen, Xiang Gu, and Yan Yun Chen. "Grid-Based Task Scheduling PMTS Algorithm." Applied Mechanics and Materials 121-126 (October 2011): 4491–97. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4491.
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This paper analyses the Min-min algorithm and its improved algorithms through the performances of load balance, time span, quality of service and economic principle. Based on the analysis of the merits of these algorithms, we propose an improved algorithm as PMTS (Priority-based maximum time-span algorithm) by integrating. In the instance of the application, we analyse and compare the performances of these algorithms, and experimental results show that, PMTS algorithm is better than other algorithms in the comprehensive performance of load-balance, time-span, quality of service and other aspects.
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Civicioglu, P., U. H. Atasever, C. Ozkan, E. Besdok, A. E. Karkinli, and A. Kesikoglu. "Performance Comparison Of Evolutionary Algorithms For Image Clustering." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 71–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-71-2014.
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Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.
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Kaur, Baljit. "Performance Comparison between LMS and NLMS Algorithm." Indian Journal of Applied Research 1, no. 6 (2011): 63–65. http://dx.doi.org/10.15373/2249555x/mar2012/20.
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Saini, Deepali, and Prof Anand Rajavat. "Performance Evaluation System for Decision Tree Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (2013): 2879–86. http://dx.doi.org/10.24297/ijct.v11i8.3006.
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In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth. Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate data mining algorithm for Business domain required comparative analysis of different algorithms based on several input parameters such as accuracy, build time and memory usage.To make analysis and comparative study, implementation of popular algorithm required on the basis of literature survey and frequency of algorithm used in present scenario. The performance of algorithms are enhanced and evaluated after applying boosting on the trees. We selected numerical and nominal types of dataset and apply on algorithms. Comparative analysis is perform on the result obtain by the system. Then we apply the new dataset in order to generate generate prediction outcome.
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YE, DESHI, and QINMING HE. "WORST-CASE PERFORMANCE EVALUATION ON MULTIPROCESSOR TASK SCHEDULING WITH RESOURCE AUGMENTATION." International Journal of Foundations of Computer Science 22, no. 04 (2011): 971–82. http://dx.doi.org/10.1142/s0129054111008519.
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We study the worst-case performance of approximation algorithms for the problem of multiprocessor task scheduling on m identical processors with resource augmentation, whose objective is to minimize the makespan. In this case, the approximation algorithms are given k (k ≥ 0) extra processors than the optimal off-line algorithm. For on-line algorithms, the Greedy algorithm and shelf algorithms are studied. For off-line algorithm, we consider the LPT (longest processing time) algorithm. Particularly, we prove that the schedule produced by the LPT algorithm is no longer than the optimal off-line algorithm if and only if k ≥ m - 2.
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Zhang, Qingyang, Tianji Peng, Guangchun Zhang, et al. "An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing." Science and Technology of Nuclear Installations 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/5549602.
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This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.
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Qiu, Dong Wei, and Shan Shan Wan. "Research on Algorithms Performance about JSP Scheduling." Advanced Materials Research 457-458 (January 2012): 20–25. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.20.
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Three typical intelligent evolutionary algorithms are applied on Job Shop scheduling problem which are Quantum algorithm, Genetic Algorithm and Population Based Incremental Learning algorithm. They three algorithms have some common features in computation, encoding strategy and probability application, but with the different problems and different scale sizes of the same problem they show different performance. In this paper we take JSP as example to test their performance difference and analyze their applicability. Two benchmark Job Shop problems are used to fulfill the comparison. The results denote that Quantum algorithm is good in a great quantity of solution individual, GA is excellent in stability and PBIL had good performance in accuracy. The research also makes a reliable instruction on the application or combination of the three algorithms.
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Wei, Xian Min. "Routing Lookup Algorithm Performance Analysis and Research." Advanced Materials Research 181-182 (January 2011): 109–12. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.109.
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This paper describes several current routing lookup algorithms, to study and analysize the complexity and operating practical performance of these routing lookup algorithms. The results show that although the binary search algorithm is not greatly improved in the searching performance, but in IPv6 environment, because searching performance of multi-branch Trie tree will decrease greatly, thus the superiority of binary search algorithm will be reflected better.
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Ozkan, Ramazan, and Ruya Samli. "Flood algorithm: a novel metaheuristic algorithm for optimization problems." PeerJ Computer Science 10 (October 2, 2024): e2278. http://dx.doi.org/10.7717/peerj-cs.2278.
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Metaheuristic algorithms are an important area of research that provides significant advances in solving complex optimization problems within acceptable time periods. Since the performances of these algorithms vary for different types of problems, many studies have been and need to be done to propose different metaheuristic algorithms. In this article, a new metaheuristic algorithm called flood algorithm (FA) is proposed for optimization problems. It is inspired by the flow of flood water on the earth’s surface. The proposed algorithm is tested both on benchmark functions and on a real-world problem of preparing an exam seating plan, and the results are compared with different metaheuristic algorithms. The comparison results show that the proposed algorithm has competitive performance with other metaheuristic algorithms used in the comparison in terms of solution accuracy and time.
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Yang, Yeling, Feng Yi, Chuancheng Deng, and Guang Sun. "Performance Analysis of the CHAID Algorithm for Accuracy." Mathematics 11, no. 11 (2023): 2558. http://dx.doi.org/10.3390/math11112558.
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The chi-squared automatic interaction detector (CHAID) algorithm is considered to be one of the most used supervised learning methods as it is adaptable to solving any kind of problem at hand. We are keenly aware of the non-linear relationships among CHAID maps, and they can empower predictive models with stability. However, we do not precisely know how high its accuracy. To determine the perfect scope the CHAID algorithm fits into, this paper presented an analysis of the accuracy of the CHAID algorithm. We introduced the causes, applicable conditions, and application scope of the CHAID algorithm, and then highlight the differences in the branching principles between the CHAID algorithm and several other common decision tree algorithms, which is the first step towards performing a basic analysis of CHAID algorithm. We next employed an actual branching case to help us better understand the CHAID algorithm. Specifically, we used vehicle customer satisfaction data to compare multiple decision tree algorithms and cited some factors that affect the accuracy and some corresponding countermeasures that are more conducive to obtaining accurate results. The results showed that CHAID can analyze the data very well and reliably detect significantly correlated factors. This paper presents the information required to understand the CHAID algorithm, thereby enabling better choices when the use of decision tree algorithms is warranted.
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Arslan, Sibel, Yıldız Zoralioğlu, and Muhammed Furkan Gul. "A Comparative Analysis Of African Vultures Optimization Algorithm With Current Metaheuristics." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8, no. 1 (2025): 325–52. https://doi.org/10.47495/okufbed.1480875.
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With the increasing complexity of optimization problems, new metaheuristic algorithms are being developed. These algorithms show their success by exhibiting superior performances on different problems. In this paper, the performance of 4 recently proposed metaheuristic algorithms, namely Artificial Hummingbird Algorithm (AHA), African Vultures Optimization Algorithm (AVOA), Crayfish Optimization Algorithm (COA) and Marine Predators Optimization Algorithm (MPA) on 26 test functions are compared. As a result of the comparisons, it was observed that the algorithms outperformed each other with very small differences on different functions. At the same time, the comparison results were evaluated by t-test statistical test. AVOA has shown better or comparable performance to other recent metaheuristics in evaluating the quality of solutions for several test functions. It is aimed to use AVOA on different problems in future research.
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Barbulescu, L., A. E. Howe, L. D. Whitley, and M. Roberts. "Understanding Algorithm Performance on an Oversubscribed Scheduling Application." Journal of Artificial Intelligence Research 27 (December 28, 2006): 577–615. http://dx.doi.org/10.1613/jair.2038.
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The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient information on the plateaus). Based on our explanations of algorithm performance, we develop a new algorithm that combines characteristics of the best performers; the new algorithm's performance is better than the previous best. We show how hypothesis driven experimentation and search modeling can both explain algorithm performance and motivate the design of a new algorithm.
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Sumertajaya, I. Made Sumertajaya, Wiwik Andriyani Lestari Ningsih, Asep Saefuddin, and Embay Rohaeti. "Biclustering Performance Evaluation of Cheng and Church Algorithm and Iterative Signature Algorithm." JTAM (Jurnal Teori dan Aplikasi Matematika) 7, no. 3 (2023): 643. http://dx.doi.org/10.31764/jtam.v7i3.14778.
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Biclustering has been widely applied in recent years. Various algorithms have been developed to perform biclustering applied to various cases. However, only a few studies have evaluated the performance of bicluster algorithms. Therefore, this study evaluates the performance of biclustering algorithms, namely the Cheng and Church algorithm (CC algorithm) and the Iterative Signature Algorithm (ISA). Evaluation of the performance of the biclustering algorithm is carried out in the form of a comparative study of biclustering results in terms of membership, characteristics, distribution of biclustering results, and performance evaluation. The performance evaluation uses two evaluation functions: the intra-bicluster and the inter-bicluster. The results show that, from an intra-bicluster evaluation perspective, the optimal bicluster group of the CC algorithm produces bicluster quality which tends to be better than the ISA. The biclustering results between the two algorithms in inter-bicluster evaluation produce a deficient level of similarity (20-31 percent). This is indicated by the differences in the results of regional membership and the characteristics of the identifying variables. The biclustering results of the CC algorithm tend to be homogeneous and have local characteristics. Meanwhile, the results of biclustering ISA tend to be heterogeneous and have global characteristics. In addition, the results of biclustering ISA are also robust.
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Lehong, Charles, Bassey Isong, Francis Lugayizi, and Adnan Abu-Mahfouz. "A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm." Journal of Sensor and Actuator Networks 10, no. 4 (2021): 70. http://dx.doi.org/10.3390/jsan10040070.
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LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance against that of four other related algorithms to study the performance improvements the algorithm brings to LoRaWAN in terms of DER and EC. LoRaSim was used to evaluate the algorithms’ performances in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the proposed algorithm outperforms the currently existing implementations of the ADR in terms of both DER and EC. However, the proposed algorithm is slightly outperformed by the native ADR in terms of EC. This was expected as the algorithm was mainly built to improve DER. The proposed algorithm builds on the existing algorithms and the ADR and significantly improves them in terms of DER and EC (excluding the native ADR), which is a significant step towards an ideal implementation of LoRaWAN’s ADR.
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Yang, Ying, Xingchuang Xiong, Zilong Liu, Shangzhong Jin, and Juan Wang. "High-Performance Encryption Algorithms for Dynamic Images Transmission." Electronics 13, no. 1 (2023): 131. http://dx.doi.org/10.3390/electronics13010131.
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With the proliferation of the internet, the issue of image tampering has escalated, necessitating robust image encryption schemes. Despite the multitude of proposed image encryption algorithms, challenges such as slow computational speed, weak security, and suboptimal visibility persist. This study addresses these challenges by introducing a high-performance encryption algorithm tailored for dynamic images—QEDI (Quick Encryption Algorithm for Dynamic Images). QEDI leverages elliptic curve-based asymmetric encryption algorithms and hash algorithms. The process involves using a hash algorithm to calculate the hash of the dynamic image slated for encryption, employing the elliptic curve algorithm to generate public and private keys, and utilizing the asymmetric encryption algorithm to process the image hash, resulting in a signature ciphertext. This ciphertext is embedded into the dynamic image, completing the encryption process. To enhance the execution speed without compromising the image quality and visibility, the bit depth of the dynamic image is reduced before hash calculation, and custom information fields are encapsulated to embed the signature ciphertext into the dynamic image. Experimental evaluations, conducted within a dynamic image generation system, encompassed assessments of security, encryption algorithm execution time, and image quality. The results indicate that utilizing QEDI for dynamic image encryption yields better security compared to EDI (Encryption Algorithm for Dynamic Images). QEDI exhibits minimal impact on image quality, with a noteworthy 69.77% reduction in execution time compared to EDI. Furthermore, when compared to existing algorithms employing image conversion for encryption, QEDI-encrypted images demonstrate enhanced visibility, better image quality, and expedited encryption speed.
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Li, Jonathan, Rohan Potru, and Farhad Shahrokhi. "A Performance Study of Some Approximation Algorithms for Computing a Small Dominating Set in a Graph." Algorithms 13, no. 12 (2020): 339. http://dx.doi.org/10.3390/a13120339.
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We implement and test the performances of several approximation algorithms for computing the minimum dominating set of a graph. These algorithms are the standard greedy algorithm, the recent Linear programming (LP) rounding algorithms and a hybrid algorithm that we design by combining the greedy and LP rounding algorithms. Over the range of test data, all algorithms perform better than anticipated in theory, and have small performance ratios, measured as the size of output divided by the LP objective lower bound. However, each have advantages over the others. For instance, LP rounding algorithm normally outperforms the other algorithms on sparse real-world graphs. On a graph with 400,000+ vertices, LP rounding took less than 15 s of CPU time to generate a solution with performance ratio 1.011, while the greedy and hybrid algorithms generated solutions of performance ratio 1.12 in similar time. For synthetic graphs, the hybrid algorithm normally outperforms the others, whereas for hypercubes and k-Queens graphs, greedy outperforms the rest. Another advantage of the hybrid algorithm is to solve very large problems that are suitable for application of LP rounding (sparse graphs) but LP formulations become formidable in practice and LP solvers crash, as we observed on a real-world graph with 7.7 million+ vertices and a planar graph on 1,000,000 vertices.
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Alsammak, Ihab L. Hussein, Ali Hussein Mohammed, and ntedhar Shakir Nasir. "E-learning and COVID-19: Predicting Student Academic Performance Using Data Mining Algorithms." Webology 19, no. 1 (2022): 3419–32. http://dx.doi.org/10.14704/web/v19i1/web19225.
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The satisfaction of E-learners has the main effect on the success of the E-learning process and leads to improvements in the E-learning system's quality and several factors affect this satisfaction. Based on the dimensions of e-learning, the main objective of this study was to evaluate the factors that contributed to students' satisfaction with e-learning during pandemic the Covid-19 and to give a thorough understanding and knowledge of different data mining techniques that have been used to predict student performance and development, as well as how these techniques help in the identification of the most relevant student attribute for prediction. Currently, to search for information in large databases, data mining techniques have become very popular and proven itis effective. Because of the performance and effectiveness of data mining techniques, it has been adopted by many areas such as telecommunication, education, sales management, banking, etc. In this paper, data mining algorithms were relied on to build e-learning classification models for a "student performance" data set, the proposed model includes 1000 instances with 35 attributes. Data mining algorithms have been implemented on the student performance data set in E-learning. Among these algorithms are the Decision Tree algorithm, Random Tree algorithm, Naive Bayes algorithm, Random Forest algorithm, REP Tree algorithm, Bagging algorithm and KNN algorithm. After comparing the results and conducting the assessment, the impact of the proposed features in e-learning on the student's performance was clarified. The final result of this study is important for providing greater insight into evaluating student performance in the COVID-19 pandemic and underscores the importance of data mining in education.
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Kim, Bong-Seok, Youngseok Jin, Jonghun Lee, and Sangdong Kim. "FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area." Sensors 22, no. 3 (2022): 1202. http://dx.doi.org/10.3390/s22031202.
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We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar parameters as a single target. On the other hand, subspace-based algorithms have the advantage of a high-resolution performance, but have a problem of very high complexity. In this paper, we propose an algorithm with reduced complexity, while achieving the high-resolution performance of the subspace-based algorithm by utilizing the advantages of the two algorithms; namely, the low-complexity advantage of FFT-based algorithms and the high-resolution performance of the MUSIC algorithms. The proposed algorithm first reduces the amount of data used as input to the subspace-based algorithm by using the estimation results obtained by FFT. Secondly, it significantly reduces the range of search regions considered for pseudo-spectrum calculations in the subspace-based algorithm. The simulation and experiment results show that the proposed algorithm achieves a similar performance compared with the conventional and low complexity MUSIC algorithms, despite its considerably lower complexity.
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Grinshpoun, T., and A. Meisels. "Completeness and Performance Of The APO Algorithm." Journal of Artificial Intelligence Research 33 (October 23, 2008): 223–58. http://dx.doi.org/10.1613/jair.2611.
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Asynchronous Partial Overlay (APO) is a search algorithm that uses cooperative mediation to solve Distributed Constraint Satisfaction Problems (DisCSPs). The algorithm partitions the search into different subproblems of the DisCSP. The original proof of completeness of the APO algorithm is based on the growth of the size of the subproblems. The present paper demonstrates that this expected growth of subproblems does not occur in some situations, leading to a termination problem of the algorithm. The problematic parts in the APO algorithm that interfere with its completeness are identified and necessary modifications to the algorithm that fix these problematic parts are given. The resulting version of the algorithm, Complete Asynchronous Partial Overlay (CompAPO), ensures its completeness. Formal proofs for the soundness and completeness of CompAPO are given. A detailed performance evaluation of CompAPO comparing it to other DisCSP algorithms is presented, along with an extensive experimental evaluation of the algorithms unique behavior. Additionally, an optimization version of the algorithm, CompOptAPO, is presented, discussed, and evaluated.
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Zheng, Junlong, Chaiyan Jettanasen, and Pathomthat Chiradeja. "Performance and Application Analysis of a New Optimization Algorithm." Computation 12, no. 1 (2023): 1. http://dx.doi.org/10.3390/computation12010001.
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Our research focused on an optimization algorithm. Our work makes three contributions. First, a new optimization algorithm, the Maritime Search and Rescue Algorithm (MSRA), is creatively proposed. The algorithm not only has better optimization performance, but also has the ability to plan the path to the best site. For other existing intelligent optimization algorithms, it has never been found that they have both of these performances. Second, the mathematical model of the MSRA was established, and the computer program pseudo-code was created. Third, the MSRA was verified by experiments.
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Lian, Naiyu, Hengzhe Xu, and Feiyang Zhang. "The Differentiation of Residents’ Cultural Consumption Tendency and Consumption Recommendation System Based on Network Inference Algorithm." Foundations of Computing and Decision Sciences 49, no. 2 (2024): 121–38. http://dx.doi.org/10.2478/fcds-2024-0008.
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Abstract To address the issue of insufficient accuracy in consumer recommendation systems, a new biased network inference algorithm is proposed based on traditional network inference algorithms. This new network inference algorithm can significantly improve the resource allocation ability of the original one, thereby improving recommendation performance. Then, the performance of this algorithm is verified through comparative experiments with network-based inference algorithms, network inference algorithms with initial resource optimization, and heterogeneous network inference algorithms. The results showed that the accuracy of the new network inference algorithm was 24.5%, which was superior to traditional one. In terms of system performance testing, the recommendation hit rate of the new network inference algorithm increased by 13.97%, which was superior to the other three comparative algorithms. The experimental results indicated that a novel network inference algorithm with bias can improve the performance of consumer recommendation systems, providing new ideas for improving the performance of consumer recommendation systems.
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Alaa, A. Alomoush, Rahman A. Alsewari Abdul, Z. Zamli Kamal, Alrosan Ayat, Alomoush Waleed, and Alissa Khalid. "Enhancing three variants of harmony search algorithm for continuous optimization problems." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2343–49. https://doi.org/10.11591/ijece.v11i3.pp2343-2349.
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Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
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Seçkiner, Serap Ulusam, and Şeyma Yilkici Yüzügüldü. "A new health-based metaheuristic algorithm: cholesterol algorithm." International Journal of Industrial Optimization 4, no. 2 (2023): 115–30. http://dx.doi.org/10.12928/ijio.v4i2.7651.
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This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Search, Multi-Verse Optimizer, and JAYA algorithms. Results showed that this novel cholesterol algorithm implementation can compete effectively with the best-known solution to test functions.
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Fang, Jie. "Performance Tuning of Coordinated Active Traffic Control Algorithm: Simultaneously Improving Corridor Safety and Mobility Performances." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/130804.
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Proactive traffic control based on macroscopic traffic flow model is an innovative approach to active traffic management. An online, model predictive control (MPC) based active traffic control algorithm, DynaTAM, is proposed to implement integrated control through ramp metering (RM) and variable speed limit (VSL). DynaTAM predicts traffic states to anticipate incoming traffic congestion and to provide control plan recommendations for optimizing the network traffic conditions. However, as with other sophisticated prediction-based control algorithms, a system fine-tuning procedure is required for DynaTAM. In this study, two aspects will be addressed to further improve system performance. First, the control algorithm is evaluated to find the correlations between the prediction horizon length and the controlled system performance to suggest the most efficient prediction horizon length for the control algorithm. Second, safety considerations are quantitatively incorporated into the control algorithm. The control algorithm optimizes the traffic network targeting the cost reductions achieved by both improved mobility and reduced crash risk. A field-data-based simulation study is conducted to evaluate the system performance within various parameters and to determine the most suitable algorithm parameters. Optimized by the refined DynaTAM algorithm, the targeted area shows significant improvements in terms of both traffic safety and mobility.
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Hidayat, Trifenaus Prabu, and Andre Sugioko. "Performance Analyzes of Bee Colony Split-Plot Algorithm." International Journal of Information and Education Technology 5, no. 7 (2015): 549–52. http://dx.doi.org/10.7763/ijiet.2015.v5.566.
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Priya, C. Geetha, and S. Thilagavathi. "Performance of SISID Algorithm for LTE Channel Models." Journal of Advances in Computer Networks 4, no. 1 (2016): 1–5. http://dx.doi.org/10.18178/jacn.2016.4.1.194.
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Siva Brindha, G., and M. Gobi. "Improving the MapReduce Performance using Symmetric Key Algorithm." International Journal of Science and Research (IJSR) 10, no. 4 (2021): 690–95. https://doi.org/10.21275/sr21204101422.
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Emima, A., and D. I. George Amalarethinam. "Integrative Ensemble Learning Algorithm for Predicting Students’ Performance." Indian Journal Of Science And Technology 18, no. 1 (2025): 72–84. https://doi.org/10.17485/ijst/v18i1.3718.
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Objectives: To create a stable student performance prediction model utilizing ensemble learning methods. Methods: The study uses boosting techniques such as CatBoost, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as simple classifiers, which are then combined into a composite classifier to improve predictive accuracy. During the training phase, a 5-level hyperparameter optimization for the basic classifiers is performed using ETLBO Optimization IELA's distinguishing feature is its Stacking ensemble method, which functions as an ensemble technique, combining the expected outcomes of the simple classifiers to build the final prediction model ETLBO algorithm is applied for hyper parameter optimization to discover the best hyperparameters of 28 features from 33 features in the base classifiers to yield the better result in experiment. This approach improves prediction efficiency and accuracy by combining the capabilities of separate models using boosting algorithms and stacking-based ensemble techniques. Findings: Student achievement Dataset in secondary education for Mathematics are taken from Public repository is used in this research work. It comprises features such as student grades, demographic information, social aspects, and school-related data. The working of boosting and stacking approaches with the classifier in the dataset consisting of LightGBM and CatGB are performed higher than those of either classifier when used separately without the ensemble technique. Additionally, CatGB operates substantially better when combined with XGBoost. With an accuracy of 86.43% and an F-score of 84.98%, the composite classifier that combines the three simple classifiers achieves the highest gain. Novelty: This research is unusual to combine the boosting and stacking ensemble techniques, which improves prediction accuracy and efficiency when compared to previous models for predicting student performance. Keywords: Educational Data Mining, Ensemble Algorithm, Boosting Algorithm, Machine Learning Algorithm, Base Classifier, Composite Classifier
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Yang, Huiwei. "Application of Hybrid Encryption Algorithm in Hardware Encryption Interface Card." Security and Communication Networks 2022 (May 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/7794209.
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In order to effectively solve the increasingly prominent network security problems, cryptographic algorithm is the key factor affecting the effectiveness of IPSec VPN encryption. Therefore, this paper mainly studies cryptographic algorithms and puts forward the following solutions: briefly analyze the concept and function of IPSec VPN, as well as the basic theoretical knowledge of IPSec Security Protocol and cryptography, and analyze the traditional cryptography, modern cryptography, symmetric cryptographic algorithms and asymmetric algorithms, and their security. At the same time, the executable and security performances of AES and DES algorithms are compared and analyzed. This paper studies the elliptic curve encryption algorithm ECC, expounds the mathematical basis of realizing the algorithm, and compares and analyzes the security performance and execution efficiency of ECC. Based on the above two algorithms, a hybrid encryption algorithm is proposed, and the realization mechanism of the hybrid encryption algorithm is studied and discussed. The hybrid encryption algorithm combines the advantages of ECC and AES. The algorithm selects 128-bit AES and 256-bit ECC. In order to better cover up plaintext C, AES is used to encrypt information. While enhancing security, speed is also considered. The improved encryption, decryption, and signature authentication algorithms are relatively safe and fast schemes. ECC algorithm is improved, and on this basis, ECC algorithm and AES algorithm are combined. Moreover, HMAC message authentication algorithm is added, and the performance of the improved algorithm is significantly improved.
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Soroushnia, Shima, Masoud Daneshtalab, Juha Plosila, Tapio Pahikkala, and Pasi Liljeberg. "High Performance Pattern Matching on Heterogeneous Platform." Journal of Integrative Bioinformatics 11, no. 3 (2014): 88–98. http://dx.doi.org/10.1515/jib-2014-253.
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Summary Pattern discovery is one of the fundamental tasks in bioinformatics and pattern recognition is a powerful technique for searching sequence patterns in the biological sequence databases. Fast and high performance algorithms are highly demanded in many applications in bioinformatics and computational molecular biology since the significant increase in the number of DNA and protein sequences expand the need for raising the performance of pattern matching algorithms. For this purpose, heterogeneous architectures can be a good choice due to their potential for high performance and energy efficiency. In this paper we present an efficient implementation of Aho-Corasick (AC) which is a well known exact pattern matching algorithm with linear complexity, and Parallel Failureless Aho-Corasick (PFAC) algorithm which is the massively parallelized version of AC algorithm without failure transitions, on a heterogeneous CPU/GPU architecture. We progressively redesigned the algorithms and data structures to fit on the GPU architecture. Our results on different protein sequence data sets show that the new implementation runs 15 times faster compared to the original implementation of the PFAC algorithm.
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Ababneh, Jehad. "Greedy particle swarm and biogeography-based optimization algorithm." International Journal of Intelligent Computing and Cybernetics 8, no. 1 (2015): 28–49. http://dx.doi.org/10.1108/ijicc-01-2014-0003.
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Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings – Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm. Originality/value – The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.
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Hasibuan, Eka Hayana, Surya Hendraputra, GS Achmad Daengs, and Liharman Saragih. "Comparison Fletcher-Reeves and Polak-Ribiere ANN Algorithm for Forecasting Analysis." Journal of Physics: Conference Series 2394, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2394/1/012008.
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Abstract Each method and algorithm ANN has different performances depending on the algorithm used and the parameters given. The purpose of this research is to obtain the best algorithm information from the two algorithms that will be compared based on the performance value or the smallest / lowest MSE value so that it can be used as a reference and information for solving forecasting problems. The ANN algorithms compared were Conjugate Gradient Fletcher-Reeves and Conjugate Gradient Polak-Ribiere. The conjugate gradient algorithm can solve unlimited optimization problems and is much more efficient than gradient descent-based algorithms because of its faster turnaround time and less iteration. The research data used for the forecasting analysis of the two algorithms are data on the number of rural poor people in Sumatra, Indonesia. 6-10-1, 6-15-1, and 6-20-1 architectural analysis. The results showed that the Polak-Ribiere Conjugate Gradient algorithm with the 6-10-1 architecture has the best performance results and the smallest / lowest MSE value compared to the Fletcher-Reeves algorithm and two other architectures. So it can be concluded that the 6-10-1 architectural architecture with the Conjugate Gradient Polak-Ribiere algorithm can be used to solve forecasting problems because the training time to achieve convergence is not too long, and the resulting performance is quite good.
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Lei, Tao, Deng Ping He, and Fang Tang Chen. "Study of BLAST Signal Detection Algorithm in LTE System." Advanced Materials Research 756-759 (September 2013): 3183–88. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3183.
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BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.
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Adane, Michael Donkor, Joshua Kwabla Deku, and Emmanuel Kwaku Asare. "Performance Analysis of Machine Learning Algorithms in Prediction of Student Academic Performance." Journal of Advances in Mathematics and Computer Science 38, no. 5 (2023): 74–86. http://dx.doi.org/10.9734/jamcs/2023/v38i51762.
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The advancement in technology has contributed largely to the application of data mining in education in recent times. However, selecting appropriate algorithm(s) to “mine” knowledge about educational data presents a difficult challenge to researchers and analyst. This paper contributes to the use of classification algorithms in academic performance prediction. The predictive ability of four popular algorithms; C4.5 Decision tree (CDT), Multilayer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF) algorithms were compared. The models were built using student dataset from selected private senior high schools in Ghana. The comparative analysis of the algorithms was made based on their Accuracy, Recall, Specificity, F-Measure and Running time. On all the training and test ratios; 80:20, 70:30 and 10-fold cross validation, the results indicated that all the algorithms performed well in the classification. However, the Naïve Bayes algorithm performed significantly better than the MLP and CDT on some ratios. The running time of the NB, CDT and RF were the quickest while MLP took the longest time.
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Mahmud, Hasan, A. K. M. Najmul Islam, Xin (Robert) Luo, and Patrick Mikalef. "Decoding algorithm appreciation: Unveiling the impact of familiarity with algorithms, tasks, and algorithm performance." Decision Support Systems 179 (April 2024): 114168. http://dx.doi.org/10.1016/j.dss.2024.114168.
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Zheng, Rong, Heming Jia, Laith Abualigah, Qingxin Liu, and Shuang Wang. "Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization." Processes 9, no. 10 (2021): 1774. http://dx.doi.org/10.3390/pr9101774.
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In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems.
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Ahmed, Mohiuddin, Raihan Seraj, and Syed Mohammed Shamsul Islam. "The k-means Algorithm: A Comprehensive Survey and Performance Evaluation." Electronics 9, no. 8 (2020): 1295. http://dx.doi.org/10.3390/electronics9081295.
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The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.
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Bijwe, Abhijit, and C. G. Dethe. "Performance Analysis of Vertical Handoff Metrics Using Variance Based Algorithms." Journal of Applied Computer Science Methods 7, no. 1 (2015): 33–42. http://dx.doi.org/10.1515/jacsm-2015-0008.
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Abstract Vertical handover is the evolving concept in 4G for seamless communication between heterogeneous networks. In this paper, our main objective is to analyze handover between two WLAN, two Wimax, two UMTS networks. The vertical handover decision is taken based on the variance based algorithm, which calculates the variance of parameters such as delay, jitter, bandwidth and packet loss for various above mentioned networks and the network with most of the parameters with minimum variance is selected. This algorithm is also compared with other algorithms such as MEW (Multiplicative experiment weighting), SAW (Simple Additive Weighting), TOPOSIS (Technique for order preference by similarity to ideal solution) and GRA (Grey Relational Analysis). These algorithms are appropriate for different traffic classes. Simulation results for proposed variance based algorithm in Matlab is discussed and compared with other Multiple attribute decision making algorithm basis of bandwidth; jitter, delay etc. are discussed in the paper. It can be seen that the proposed variance algorithm gives less packet delay than all the algorithms, Jitter is also is least than all the other algorithms.
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Manaseer, Saher, and Ahmad K. Al Hwaitat. "Measuring Parallel Performance of Sorting Algorithms." Modern Applied Science 12, no. 10 (2018): 23. http://dx.doi.org/10.5539/mas.v12n10p23.
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The performance evaluation of sorting algorithm play a major role in understanding the behavior which has great benefit in most of the field of sciences, knowing the difference between parallel and sequential performance will help the researchers to choose the best algorithm bucket and bubble sort to use and implement. In this research we study the performance of two sorting algorithm and evaluate the difference in performance in aspect of speed up and efficiency, the two algorithms has been tested on IMAN1 super computer with different evaluate input size and different number of processors. The results showed that he performance of runtime for the bubble and bucket sorting algorithms has been effectively reduced by the parallel computing over the large data size and the number of processor of 64 get the lowest running time, and the parallel performance was better than other methods.
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Xiao, Shi Song, Ao Lin Wang, and Hui Feng. "An Improved Algorithm Based on AC-BM Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 1576–79. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1576.
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The pattern matching algorithm is the mainstream technology in the instruction detection system, and therefore as a pattern-matching methods core string matching algorithm directly affect an intrusion detection system performance and efficiency. So based on the discussions of the most fashionable pattern matching algorithms at present, an improved algorithm of AC-BM is presented. From the experiments in the Snort ,it is concluded that the improved algorithm of the performance and efficiency is higher than AC-BM algorithm.
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Ordys, Andrzej W., Masayoshi Tomizuka, and Michael J. Grimble. "State-Space Dynamic Performance Preview-Predictive Controller." Journal of Dynamic Systems, Measurement, and Control 129, no. 2 (2006): 144–53. http://dx.doi.org/10.1115/1.2431810.
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The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the performance index through a dynamic optimization. A simple example illustrates the features of the three algorithms and prompts a discussion on what is actually minimized in predictive control. The DPPC algorithm, derived in this paper, provides for a minimum of the predictive performance index. The differences and similarities between the preview control and the predictive control have been discussed and optimization approach of predictive control has been explained.
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Ghimire, Roshani, and Ram Kumar Basnet. "Shortest Path Routing Performance Evaluation over SDN Environment." December 2023 5, no. 4 (2023): 405–22. http://dx.doi.org/10.36548/jei.2023.4.004.
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Static routing has a manual configuration setup system, and the scope of static routing in an SDN network is just for small networks. The solution to this problem rises up with the new technology defined as software-defined networking (SDN) based on shortest path first dynamic routing. SDN has the facility of a centralized controller that smooth the controls and routes computation over a data packet. The performance analysis of SDN networks that have SDN switches connected to the network based on the shortest path first protocol are simulated on Mininet. The POX controller with Mininet programming feature for creating smart topologies was chosen. In this research, the SDN network using Dijkstra’s algorithm, Bellman-Ford algorithm, extended Dijkstra’s algorithm and Floyd Warshall Algorithm were implemented. The quality factors of SDN created by using four algorithms are measured in terms of delay, jitter, latency, packet loss, transmit, received, throughput, and bandwidth based on experimental results and European Telecommunications Standards Institute (ETSI) data. The performance parameters of SDN network topology created using Dijkstra’s, bellman ford, extended Dijkstra’s, and Floyd Warshall algorithms were compared and the experimental results showed that Bellman-Ford algorithm is better in terms of performance parameters than the other three algorithms.
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Lee, Dongkyu, Jae-Weon Jeong, and Guebin Choi. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model." Energies 14, no. 10 (2021): 2822. http://dx.doi.org/10.3390/en14102822.
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Photovoltaics are methods used to generate electricity by using solar cells, which convert natural energy from the sun. This generation makes use of unlimited natural energy. However, this generation is irregular because they depend on weather occurrences. For this reason, there is a need to improve their economic efficiency through accurate predictions and reducing their uncertainty. Most researches were conducted to predict photovoltaic generation with various machine learning and deep learning methods that have complicated structures and over-fitted performances. As improving the performance, this paper explores the probabilistic approach to improve the prediction of the photovoltaic rate of power output per hour. This research conducted a variable correlation analysis with output values and a specific EM algorithm (expectation and maximization) made from 6054 observations. A comparison was made between the performance of the EM algorithm with five different machine learning algorithms. The EM algorithm exhibited the best performance compared to other algorithms with an average of 0.75 accuracies. Notably, there is the benefit of performance, stability, the goodness of fit, lightness, and avoiding overfitting issues using the EM algorithm. According to the results, the EM algorithm improves photovoltaic power output prediction with simple weather forecasting services.
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Abdelwahab, Amira, and Nesma Youssef. "Performance Evaluation of Sequential Rule Mining Algorithms." Applied Sciences 12, no. 10 (2022): 5230. http://dx.doi.org/10.3390/app12105230.
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Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of application. The three compared algorithms are the TRuleGrowth algorithm, which is an extension sequential rule algorithm of RuleGrowth; the top-k non-redundant sequential rules algorithm (TNS); and a non-redundant dynamic bit vector (NRD-DBV). The analysis compares the three algorithms regarding the run time, the number of produced rules, and the used memory to nominate which of them is best suited in prediction. Additionally, it explores the most suitable applications for each algorithm to improve the efficiency. The experimental results proved that the performance of the algorithms appears related to the dataset characteristics. It has been demonstrated that altering the window size constraint, determining the number of created rules, or changing the value of the minSup threshold can reduce execution time and control the number of valid rules generated.
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Shi, Ruifeng, Ning Zhang, Runhai Jiao, Zhenyu Zhou, and Li Zhang. "Study on Evolutionary Algorithm Online Performance Evaluation Visualization Based on Python Programming Language." Journal of Systems Science and Information 2, no. 1 (2014): 86–96. http://dx.doi.org/10.1515/jssi-2014-0086.
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Abstract Evolutionary computations are kinds of random searching algorithms derived from natural selection and biological genetic evolution behavior. Evaluating the performance of an algorithm is a fundamental task to track and find the way to improve the algorithm, while visualization technique may play an important act during the process. Based on current existing algorithm performance evaluation criteria and methods, a Python-based programming tracking strategy, which employs 2-D graphical library of python matplotlib for online algorithm performance evaluation, is proposed in this paper. Tracking and displaying the performance of genetic algorithm (GA) and particle swarm optimization (PSO) optimizing two typical numerical benchmark problems are employed for verification and validation. Results show that the tracking strategy based on Python language for online performance evaluation of evolutionary algorithms is valid, and can be used to help researchers on algorithms’ performance evaluation and finding ways to improve it.
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Krimpenis, Agathoklis A., and Loukas Athanasakos. "“Optimizing the Optimization”: A Hybrid Evolutionary-Based AI Scheme for Optimal Performance." Computers 14, no. 3 (2025): 97. https://doi.org/10.3390/computers14030097.
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Optimization algorithms for solving technological and scientific problems often face long convergence times and high computational costs due to numerous input/output parameters and complex calculations. This study focuses on proposing a method for minimizing response times for such algorithms across various scientific fields, including the design and manufacturing of high-performance, high-quality components. It introduces an innovative mixed-scheme optimization algorithm aimed at effective optimization with minimal objective function evaluations. Indicative key optimization algorithms—namely, the Genetic Algorithm, Firefly Algorithm, Harmony Search Algorithm, and Black Hole Algorithm—were analyzed as paradigms to standardize parameters for integration into the mixed scheme. The proposed scheme designates one algorithm as a “leader” to initiate optimization, guiding others in iterative evaluations and enforcing intermediate solution exchanges. This collaborative process seeks to achieve optimal solutions at reduced convergence costs. This mixed scheme was tested on challenging benchmark functions, demonstrating convergence speeds that were at least three times faster than the best-performing standalone algorithms while maintaining solution quality. These results highlight its potential as an efficient optimization approach for computationally intensive problems, regardless of the included algorithms and their standalone performance.
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Ertas, Gokhan. "Fitting Intravoxel Incoherent Motion Model to Diffusion MR Signals of the Human Breast Tissue using Particle Swarm Optimization." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 9, no. 2 (2019): 105–12. http://dx.doi.org/10.11121/ijocta.01.2019.00642.
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Intravoxel incoherent motion (IVIM) modeling offers the parameters f, D and D* as biomarkers for different lesion types and cancer stages from diffusion MR signals. Challenges with the available optimization algorithms in fitting the model to the signals motive new studies for improved parameter estimations. In this study, one thousand value sets of f, D, D* for human breast are assembled and used to generate five thousand diffusion MR signals considering noise-free and noisy situations exhibiting signal-to-noise ratios (SNR) of 20, 40, 60 and 80. The estimates of f, D, D* are obtained using Levenberg-Marquardt (LM), trust-region (TR) and particle swarm (PS) algorithms. On average, the algorithms provide the highest fitting performance for the noise-free signals (R2adj=1.000) and great fitting performances on the noisy signals with SNR>20 (R2adj>0.988). TR algorithm performs slightly better for SNR=20 (R2adj=0.947). TR and PS algorithms achieve the highest parameter estimation performance for all the parameters while LM algorithm reveals the highest performance for f and D only on the noise-free signals (r=1.00). For the noisy signals, performances increase while SNR increases. All algorithms accomplish poor performances for D* (r=0.01-0.20) while TR and PS algorithms perform same for f (r=0.48-0.97) and D (r=0.85-0.99) but remarkably better than LM algorithm for f (r=0.08-0.97) and D (r=0.53-0.99). Overall, TR and PS algorithms demonstrate better but indistinguishable performances. Without requiring any user-given initial value, PS algorithm may facilitate improved estimation of IVIM parameters of the human breast tissue. Further studies are needed to determine its benefit in clinical practice.
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Pramudita, Rully, Saludin Muis, Nadya Safitri, and Fitri Shafirawati. "Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara." TEMATIK 11, no. 1 (2024): 128–34. http://dx.doi.org/10.38204/tematik.v11i1.1928.
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Classification algorithms have a very important role in Machine Learning, but not all algorithms have the same performance in every case. Algorithm performance can be affected by the type of data used, differences in problem characteristics, and the parameters used. Additionally, ensemble learning techniques such as Bagging can affect algorithm performance. Therefore, the problem arises of how to choose the most suitable algorithm for a particular classification task and how to optimize the performance of the algorithm. This research aims to carry out a comparative analysis and optimization of classification algorithms in Machine Learning. Classification algorithms that will be evaluated include Support Vector Machine (SVM), Neural Network, Logistic Regression, Decision Tree, and K-Nearest Neighbors (K-NN). Evaluation of the performance of these algorithms will be carried out using the confusion matrix, Receiver Operating Characteristic (ROC) Curve, and Area Under Curva (AUC). The result of this research is a comparative analysis of the optimization of classification algorithms using the bagging technique. After carrying out the evaluation process using the confusion matrix and ROC curve, it was found that the algorithm optimization using the bagging technique only had an effect on the Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms. . The accuracy of the DT algorithm increased by 0.6% while the accuracy of KNN increased by 1.3%. The AUC value for the DT algorithm increased by 1.4% and the KNN algorithm increased by 0.3%.