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Journal articles on the topic 'Algorithm performance'

Author: Grafiati

Published: 28 June 2021

Last updated: 2 February 2022

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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 (October 1, 2011): 165–67. http://dx.doi.org/10.15373/2249555x/august2014/47.

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2

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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3

Kaur, Baljit. "Performance Comparison between LMS and NLMS Algorithm." Indian Journal of Applied Research 1, no. 6 (October 1, 2011): 63–65. http://dx.doi.org/10.15373/2249555x/mar2012/20.

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4

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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5

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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6

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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7

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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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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Saini, Deepali, and Prof Anand Rajavat. "Performance Evaluation System for Decision Tree Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (November 27, 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.Â 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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10

N, Krishnamoorthy. "TV Shows Popularity and Performance Prediction Using CNN Algorithm." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1541–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202257.

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11

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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12

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 (June 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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13

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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14

Zhang, Qingyang, Tianji Peng, Guangchun Zhang, Jie Liu, Xiaowei Guo, Chunye Gong, Bo Yang, and Xukai Fan. "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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15

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 (December 14, 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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16

Seah, Chze Eng, and Inseok Hwang. "Algorithm for Performance Analysis of the IMM Algorithm." IEEE Transactions on Aerospace and Electronic Systems 47, no. 2 (April 2011): 1114–24. http://dx.doi.org/10.1109/taes.2011.5751246.

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17

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 (December 1, 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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18

Jian, Di, and Yanfeng Peng. "Research of Performance of Distributed Platforms Based on Clustering Algorithm." Journal of Computers 11, no. 3 (May 2016): 195–200. http://dx.doi.org/10.17706/jcp.11.3.195-200.

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19

Li, Lin-lin, Bing Xia, and He He. "Service-Differentiated Routing Algorithm in DTN Based on Node Performance." International Journal of Information and Electronics Engineering 7, no. 1 (2017): 1–6. http://dx.doi.org/10.18178/ijiee.2017.7.1.652.

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20

Angeline, D. Magdalene Delighta. "Association Rule Generation for Student Performance Analysis using Apriori Algorithm." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 01 (April 29, 2013): 16–20. http://dx.doi.org/10.9756/sijcsea/v1i1/01010252.

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21

Malviya, Deepak Kumar, and Prashant Shrivastav. "Enhanced Performance for Military Vehicles Using Clustering Algorithm in VANET." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (December 31, 2018): 882–88. http://dx.doi.org/10.31142/ijtsrd19102.

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22

Ando, Jun, Shiro Kataoka, and Tomohiro Ryu. "Method to Improve Propeller Performance Using Real-coded Genetic Algorithm." Journal of The Japan Institute of Marine Engineering 46, no. 3 (2011): 289–95. http://dx.doi.org/10.5988/jime.46.289.

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23

Raviya, Kaushik H., and Biren Gajjar. "Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA." Paripex - Indian Journal Of Research 2, no. 1 (January 15, 2012): 19–21. http://dx.doi.org/10.15373/22501991/jan2013/8.

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24

LIU, YU, MINGWEI LV, and WEI ZUO. "A NEW MULTIMODAL PARTICLE SWARM OPTIMIZATION ALGORITHM BASED ON GREEDY ALGORITHM." International Journal of Computational Intelligence and Applications 11, no. 03 (September 2012): 1250020. http://dx.doi.org/10.1142/s1469026812500204.

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A new multimodal particle swarm optimization algorithm based on greedy algorithm (GAPSO) is proposed in this paper. A ring topology is employed in each niche instead of a fully connected topology in GAPSO. More importantly, GAPSO does not depend on any niching parameter, which improves its effectiveness and efficiency greatly. A range of standard test functions are employed to test this proposed algorithm's performance. Experimental results demonstrate that GAPSO performs as well as existed optimization algorithms, and needs smaller number of evaluations to locate all the global optima for simple problems. For complex problems, GAPSO can achieve better and more consistent performance, and can locate more global optima than SPSO, ANPSO and rpso, which makes it possible to be widely used in the real world.

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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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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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Ahmed, Mohiuddin, Raihan Seraj, and Syed Mohammed Shamsul Islam. "The k-means Algorithm: A Comprehensive Survey and Performance Evaluation." Electronics 9, no. 8 (August 12, 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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Manaseer, Saher, and Ahmad K. Al Hwaitat. "Measuring Parallel Performance of Sorting Algorithms." Modern Applied Science 12, no. 10 (September 12, 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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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 (February 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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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 (August 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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Abouaroek, Musaeed, and Khaleel Ahmad. "Performance analysis of NTRU algorithm with non-post-quantum algorithms." Journal of Discrete Mathematical Sciences and Cryptography 24, no. 5 (July 4, 2021): 1349–63. http://dx.doi.org/10.1080/09720529.2021.1932926.

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Mansouri, Taha, Ahad Zare Ravasan, and Mohammad Reza Gholamian. "A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering." International Journal of Data Warehousing and Mining 10, no. 3 (July 2014): 1–14. http://dx.doi.org/10.4018/ijdwm.2014070101.

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One of the most widely used algorithms to solve clustering problems is the K-means. Despite of the algorithm's timely performance to find a fairly good solution, it shows some drawbacks like its dependence on initial conditions and trapping in local minima. This paper proposes a novel hybrid algorithm, comprised of K-means and a variation operator inspired by mutation in evolutionary algorithms, called Noisy K-means Algorithm (NKA). Previous research used K-means as one of the genetic operators in Genetic Algorithms. However, the proposed NKA is a kind of individual based algorithm that combines advantages of both K-means and mutation. As a result, proposed NKA algorithm has the advantage of faster convergence time, while escaping from local optima. In this algorithm, a probability function is utilized which adaptively tunes the rate of mutation. Furthermore, a special mutation operator is used to guide the search process according to the algorithm performance. Finally, the proposed algorithm is compared with the classical K-means, SOM Neural Network, Tabu Search and Genetic Algorithm in a given set of data. Simulation results statistically demonstrate that NKA out-performs all others and it is prominently prone to real time clustering.

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Ahmed, Aram M., Tarik A. Rashid, and Soran Ab M. Saeed. "Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation." Computational Intelligence and Neuroscience 2020 (January 22, 2020): 1–20. http://dx.doi.org/10.1155/2020/4854895.

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This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Elsayad, Dina, Safawat Hamad, Howida Abd-Alfatah Shedeed, and Mohamed Fahmy Tolba. "Hybrid High-Performance Computing Algorithm for Gene Regulatory Network." International Journal of Sociotechnology and Knowledge Development 13, no. 1 (January 2021): 52–64. http://dx.doi.org/10.4018/ijskd.2021010105.

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This paper presents a parallel algorithm for gene regulatory network construction, hereby referred to as H2pcGRN. The construction of gene regulatory network is a vital methodology for investigating the genes interactions' topological order, annotating the genes functionality and demonstrating the regulatory process. One of the approaches for gene regulatory network construction techniques is based on the component analysis method. The main drawbacks of component analysis-based algorithms are its intensive computations that consume time. Despite these drawbacks, this approach is widely applied to infer the regulatory network. Therefore, introducing parallel techniques is indispensable for gene regulatory network inference algorithms. H2pcGRN is a hybrid high performance-computing algorithm for gene regulatory network inference. The proposed algorithm is based on both the hybrid parallelism architecture and the generalized cannon's algorithm. A variety of gene datasets is used for H2pcGRN assessment and evaluation. The experimental results indicated that H2pcGRN achieved super-linear speedup, where its computational speedup reached 570 on 256 processing nodes.

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Mostert, Werner, Katherine M. Malan, and Andries P. Engelbrecht. "A Feature Selection Algorithm Performance Metric for Comparative Analysis." Algorithms 14, no. 3 (March 22, 2021): 100. http://dx.doi.org/10.3390/a14030100.

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This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems. The baseline fitness improvement (BFI) measure quantifies the potential value gained by applying feature selection. The BFI measure can be used to compare the performance of feature selection algorithms across datasets by measuring the change in classifier performance as a result of feature selection, with respect to the baseline where all features are included. Empirical results are presented to show that there is performance complementarity for a suite of feature selection algorithms on a variety of real world datasets. The BFI measure is a normalised performance metric that can be used to correlate problem characteristics with feature selection algorithm performance, across multiple datasets. This ability paves the way towards describing the performance space of the per-instance algorithm selection problem for feature selection algorithms.

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GRAYSON, BRIAN, and ROBERT VAN DE GEIJN. "A HIGH PERFORMANCE PARALLEL STRASSEN IMPLEMENTATION." Parallel Processing Letters 06, no. 01 (March 1996): 3–12. http://dx.doi.org/10.1142/s0129626496000029.

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In this paper, we give a practical high performance parallel implementation of Strassen’s algorithm for matrix multiplication. We show how under restricted conditions, this algorithm can be implemented plug compatible with standard parallel matrix multiplication algorithms. Results obtained on a large Intel Paragon system show a 10– 20% reduction in execution time compared to what we believe to be the fastest standard parallel matrix multiplication implementation available at this time.

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37

Ababneh, Jehad. "Greedy particle swarm and biogeography-based optimization algorithm." International Journal of Intelligent Computing and Cybernetics 8, no. 1 (March 9, 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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38

Singh, Akanksha, and Pramod Kumar Mishra. "Performance Analysis of Floyd Warshall Algorithm vs Rectangular Algorithm." International Journal of Computer Applications 107, no. 16 (December 18, 2014): 23–27. http://dx.doi.org/10.5120/18837-0372.

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Tu, Yong Jun, Gao Ming Huang, Jing Hui Li, and Qing Lin Ge. "Performance Analysis of Radar and ESM Track Association Algorithms." Applied Mechanics and Materials 130-134 (October 2011): 1925–28. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.1925.

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Statistical theory and fuzzy analysis are two primary methods of radar and ESM track association algorithms. Absolute-double-threshold rule was used to analyze these two association algorithms. Each algorithm was simulated in this paper. The simulation results show that both of these two track association algorithms have high probability of correct association, but the algorithm based on statistical theory has faster convergent speed and litter samples, while the other is simple, and has less calculation.

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Rockett, John A. "Zone Model Plume Algorithm Performance." Fire Science and Technology 15, no. 1+2 (1995): 1–15. http://dx.doi.org/10.3210/fst.15.1.

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Rockett, John A. "Zone Model Plume Algorithm Performance." Fire Science and Technology 17, no. 1 (1997): 28–39. http://dx.doi.org/10.3210/fst.17.28.

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Takahashi, Kiyoshi. "Performance improvement of LMS algorithm." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 74, no. 5 (1991): 98–104. http://dx.doi.org/10.1002/ecjc.4430740511.

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Chishiro, Hiroyuki, Masayoshi Takasu, Rikuhei Ueda, and Nobuyuki Yamasaki. "Performance evaluation of RUNT algorithm." ACM SIGBED Review 13, no. 1 (March 25, 2016): 15–21. http://dx.doi.org/10.1145/2907972.2907974.

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Thazhuthaveetil, Matthew J., and Anish V. Shah. "Parallel hough transform algorithm performance." Image and Vision Computing 9, no. 2 (April 1991): 88–92. http://dx.doi.org/10.1016/0262-8856(91)90017-j.

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Kumaravel, Dr K., and Dr M. Sengaliappan. "Performance Study on Multipath Routing Algorithm using Temporarily Ordered Routing Algorithm TORA using Link – Reversal in Wireless Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 328–31. http://dx.doi.org/10.31142/ijtsrd5951.

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Hou, Guo Zhao, Jin Biao Wang, and Jing Wu. "MANET-Based Stable Clustering Algorithm and its Performance Analysis." Applied Mechanics and Materials 571-572 (June 2014): 100–104. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.100.

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In MANET, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a MANET-based stable clustering algorithm (MSCA) was proposed. Firstly, MSCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that MSCA can improve cluster stability and reduce clustering overheads.

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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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Zhang, Zhao Jun, and Zu Ren Feng. "Quantifying Ordinal Performance of Ant Colony Optimization." Applied Mechanics and Materials 378 (August 2013): 387–93. http://dx.doi.org/10.4028/www.scientific.net/amm.378.387.

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In contrast to many successful applications of ant colony optimization, the theoretical foundation is rather weak. It greatly limits the application in practical problems. One problem, called solution quality evaluation, is how to quantify the performance of the algorithm. It is hardly solved by theoretical methods. Experimental analysis method based on the analysis of search space and characteristic of algorithm itself is proposed in this paper. As algorithm runs, it would produce a large number of feasible solutions. After preprocessing, they were clustered according to distance. Then, good enough set was partitioned by the results of clustering. Last, evaluation result of ordinal performance was got by using relative knowledge of statistics. As the method only uses feasible solution produced by optimization algorithm, it is independent to specific algorithm. Therefore, the proposed method can be adopted by other intelligent optimization algorithms. The method is demonstrated through traveling salesman problem.

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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 (February 25, 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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LI, KEQIN. "PERFORMANCE ANALYSIS AND EVALUATION OF RANDOM WALK ALGORITHMS ON WIRELESS NETWORKS." International Journal of Foundations of Computer Science 23, no. 04 (June 2012): 779–802. http://dx.doi.org/10.1142/s0129054112400369.

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We propose a model of dynamically evolving random networks and give an analytical result of the cover time of the simple random walk algorithm on a dynamic random symmetric planar point graph. Our dynamic network model considers random node distribution and random node mobility. We analyze the cover time of the parallel random walk algorithm on a complete network and show by numerical data that k parallel random walks reduce the cover time by almost a factor of k. We present simulation results for four random walk algorithms on random asymmetric planar point graphs. These algorithms include the simple random walk algorithm, the intelligent random walk algorithm, the parallel random walk algorithm, and the parallel intelligent random walk algorithm. Our random network model considers random node distribution and random battery transmission power. Performance measures include normalized cover time, probability distribution of the length of random walks, and load distribution.

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