Search Results - (( parallel optimization approach algorithm ) OR ( probable distribution function algorithm ))*

Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures by Koohi, Sina Zangbari

“…Any optimization algorithm is suitable for only a specific domain of optimization problems. …”
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Parallel metaheuristic algorithm for route planning using CUDA by Looi, Daniel Jun Jie

“…Area of Study: Massively Parallel Computing, Combinatorial Optimization Keywords: Parallel Metaheuristic Algorithm, Travelling Salesman Problem, CUDA, GPU, Genetic Algorithm…”
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Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function by Hammash, Nayif Mohammed

“…This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. …”
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A GPGPU Approach to Accelerate Ant Swarm Optimization Rough Reducts (ASORR) Algorithm by Udayanti, Erika Devi, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah, Nugroho, Fajar Agung

“…For the complex matrix calculation in a single cpu, it will take a long computing time to build the discernibility matrix whereas the execution time of an algorithm is needed to be considered. This paper proposed an parallel approach to accelerate the execution time of ASORR algorithm which is utilizing GPGPU that supports high speed parallel computing. …”
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An empirical study of density and distribution functions for ant swarm optimized rough reducts by Pratiwi, Lustiana, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah

“…To describe relative probability of different random variables, Probability Density Function (PDF) and the Cumulative Density Function (CDF) are capable to specify its own characterization of Gaussian distributions. …”
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Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems by Uvaraja, Vikneswary

“…Additionally, the MTS algorithm is also implemented in parallel computing to produce parallel MTS for generating comparable solutions in shorter computational times. …”
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A Comparative Performance Analysis of Gaussian Distribution Functions in Ant Swarm Optimized Rough Reducts by Pratiwi, Lustiana, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah

“…Coexistence, cooperation, and individual contribution to food searching by a particle (ant) as a swarm (ant) survival behavior, depict the common characteristics of both algorithms. Solution vector of ACO is presented by implementing density and distribution function to search for a better solution and to specify a probability functions for every particle (ant). …”
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Novel distributed algorithm for coalition formation for enhanced spectrum sensing in cognitive radio networks by Tahir, Mohammad, Habaebi, Mohamed Hadi, Islam, Md Rafiqul

“…The utility function is defined as the average probability of false alarm per cognitive radio user. …”
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Optimization of extractive Automatic Text Summarization using Decomposition-based Multi-objective Differential Evolution and parallelization by Hazmi Wahab, Muhammad Hafizul

“…These advancements collectively propel optimization-based ATS approaches closer to real-time applications where thousands of documents could be involved, demonstrating the versatility and efficiency of the proposed MODE/D algorithm across diverse computing architectures, including multicore and many core environments.…”
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Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line by Noroziroshan, Alireza, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Napsiah

“…This study presented an intelligence based genetic algorithm approach to optimize the considered problem objectives through reducing the problem complexity. …”
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Novel distributed algorithm for coalition formation in cognitive radio networks for throughput enhancement using matching theory by Tahir, Mohammad, Habaebi, Mohamed Hadi, Islam, Md Rafiqul

“…The utility function is defined as the average probability of false alarm per cognitive radio user. …”
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Probabilistic evaluation of wind power generation by Razali N.M.M., Misbah M.

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Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching by Low, Yeh Ching

“…The probability function of the counts is often complicated thus a method using numerical Laplace transform inversion for computing the probabilities and the renewal function is proposed. …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

“…Metaheuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. …”
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ROA-CONS: raccoon optimization job scheduling by Koohi, Sina Zangbari, Abdul Hamid, Nor Asilah Wati, Othman, Mohamed, Ibragimov, Gafurjan

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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Siddiqui, Sumrana, Sarkar, Rashel

“…Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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Cash-flow analysis of a wind turbine operator by Muhamad Razali N.M., Hashim A.H.

“…Two-parameter Weibull type probability density function (PDF) is used to model wind profile at two locations. …”

Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
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A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems by Ahmad, S.G., Liew, Chee Sun, Munir, E.U., Ang, Tan Fong, Khan, S.U.

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Parallel multiple tabu search for multiobjective Urban Transit Scheduling Problem by Uvaraja, Vikneswary, Lai, Soon Lee, Abd Rahmin, Nor Aliza, Hsin, Vonn Seow

“…A set covering model was then adopted to minimize the number of buses and drivers simultaneously. A parallel tabu search algorithm was proposed to solve the problem by modifying the initialization process and incorporating intensification and diversification approaches to guide the search effectively from the different feasible domain in finding optimal solutions with lesser computational effort. …”
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