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Relevant bibliographies by topics / Operator Execution Algorithms

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Journal articles

Academic literature on the topic 'Operator Execution Algorithms'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Operator Execution Algorithms"

1

MARION-POTY, VIRGINIE, and SERGE MIGUET. "DATA ALLOCATION STRATEGIES FOR PARALLEL IMAGE PROCESSING ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 04 (1995): 615–34. http://dx.doi.org/10.1142/s0218001495000237.

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This paper discusses several data allocation strategies used for the parallel implementation of basic imaging operators. It shows that depending on the operator (sequential or parallel, with regular or irregular execution time), the image data must be partitioned in very different manners: The square sub-domains are best adapted for minimizing the communication volume, but rectangles can perform better when we take into account the time for constructing messages. Block allocations are well adapted for inherently parallel operators since they minimize interprocessor interactions, but in the cas

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Guo, Wenyan, Mingfei Wu, Fang Dai, and Yufan Qiang. "Improved Environmental Stimulus and Biological Competition Tactics Interactive Artificial Ecological Optimization Algorithm for Clustering." Biomimetics 8, no. 2 (2023): 242. http://dx.doi.org/10.3390/biomimetics8020242.

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An interactive artificial ecological optimization algorithm (SIAEO) based on environmental stimulus and a competition mechanism was devised to find the solution to a complex calculation, which can often become bogged down in local optimum because of the sequential execution of consumption and decomposition stages in the artificial ecological optimization algorithm. Firstly, the environmental stimulus defined by population diversity makes the population interactively execute the consumption operator and decomposition operator to abate the inhomogeneity of the algorithm. Secondly, the three diff

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3

Nobre, Tiago, Silvia Regina Vergilio, and Aurora Pozo. "Reducing Interface Mutation Costs with Multiobjective Optimization Algorithms." International Journal of Natural Computing Research 3, no. 3 (2012): 21–40. http://dx.doi.org/10.4018/jncr.2012070102.

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To reduce mutation test costs, different strategies were proposed to find a set of essential operators that generates a reduced number of mutants without decreasing the mutation score. However, the operator selection is influenced by other factors, such as: number of test data, execution time, number of revealed faults, etc. In fact this is a multiobjective problem. For that, different good solutions exist. To properly deal with this problem, a selection strategy based on multiobjective algorithms was proposed and investigated for unit testing. This work explores the use of such strategy in th

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4

Lougmiri, Zekri. "A New Progressive Method for Computing Skyline Queries." Journal of Information Technology Research 10, no. 3 (2017): 1–21. http://dx.doi.org/10.4018/jitr.2017070101.

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Skyline queries are important in many fields, especially for decision making. In this context, objects or tuples of databases are defined according to some numerical and non numerical attributes. The skyline operator acts on the numerical ones. The algorithms that implements this skyline operator are genrally of progressive or non progressive. The progressive ones return the skyline operator during its execution while non preogressive alogrithms return the result at the end of its execution. This paper presents a new progressive algorithm for computing the skyline points. This algorithm is bas

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5

Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION." International Journal of Research -GRANTHAALAYAH 6, no. 6 (2018): 346–56. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1379.

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This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the

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Khamprapai, Wanida, Cheng-Fa Tsai, and Paohsi Wang. "Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases." Applied Sciences 10, no. 20 (2020): 7264. http://dx.doi.org/10.3390/app10207264.

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Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study,

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7

Ebrahimi, Maryam, Parviz Dinari, Mohamad Samaei, Rouhollah Sohrabi, and Soheil Sherafatianfini. "A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty." Discrete Dynamics in Nature and Society 2022 (September 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/2648936.

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Production system design has lots of restrictions and complex assumptions that cause difficulty in decision making. One of the most important of them is the complexity of the relationship between man and machine. In this regard, operator learning is recognized as an effective element in completing tasks in the production system. In this research, a mathematical model for scheduling the parallel machines in terms of job degradation and operator learning is presented. As one of the most important assumptions, the sequence-dependent setup time is of concern. In other words, jobs are processed seq

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García, José, Gino Astorga, and Víctor Yepes. "An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics." Mathematics 9, no. 3 (2021): 225. http://dx.doi.org/10.3390/math9030225.

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The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the

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9

Fan, Yi, Minghui Cai, Naiqi Li, and Yongmei Liu. "A First-Order Interpreter for Knowledge-Based Golog with Sensing based on Exact Progression and Limited Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 734–42. http://dx.doi.org/10.1609/aaai.v26i1.8230.

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While founded on the situation calculus, current implementations of Golog are mainly based on the closed-world assumption or its dynamic versions or the domain closure assumption. Also, they are almost exclusively based on regression. In this paper, we propose a first-order interpreter for knowledge-based Golog with sensing based on exact progression and limited reasoning. We assume infinitely many unique names and handle first-order disjunctive information in the form of the so-called proper+ KBs. Our implementation is based on the progression and limited reasoning algorithms for proper+ KBs

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10

GURMAN, Ivan, Pavlo MOTORNYI, Viktor CHESHUN, Andrii DZHULIY, and Vitalii CHORNENKYI. "ALGORITHM OF SYNTHESIS OF DISCRETE TESTS BASED ON EVOLUTIONARY THEORIES." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (2022): 40–43. http://dx.doi.org/10.31891/2307-5732-2022-309-3-40-43.

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The complexity of modern digital components and devices as objects of diagnosis, in most cases, makes it impossible for experts to synthesize tests manually in the time allowed to solve the problem of organizing diagnostic tests. This determines the relevance of the development and implementation of intelligent methods and tools for automated synthesis of test sequences. One of the promising options for the synthesis and optimization of discrete tests is the solution of this problem based on the theory of evolution or genetic algorithms. The paper describes an algorithm for the synthesis of di

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