Search Results - (( evolution optimisation based algorithm ) OR ( using simulation a algorithm ))

Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design by Zuwairie, Ibrahim, Mohd Falfazli, Mat Jusof, Mohd Zaidi, Mohd Tumari

“…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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Reliability assessment of power system generation adequacy with wind power using population-based intelligent search methods by Kadhem, Athraa Ali

“…This study sought to examine the performance of three newly proposed techniques, for reliability assessment of the power systems, namely Disparity Evolution Genetic Algorithm (DEGA), Binary Particle Swarm Optimisation (BPSO), and Differential Evolution Optimization Algorithm (DEOA). …”
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Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

A self‐configured link adaptation for green LTE downlink transmission by Salman, Mustafa Ismael, Ng, Chee Kyun, Noordin, Nor Kamariah, Mohd Ali, Borhanuddin, Sali, Aduwati

“…Then, a self‐configured link adaptation (SCLA) algorithm is developed to ensure that the priority weights related to EE and SE are adapted according to network load with the use of real‐time cross‐layer optimization. …”
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A competitive co-evolutionary approach for the nurse scheduling problem by Mohamad Nazri, Maizatul Farhana, Mohd Yusoh, Zeratul Izzah, Basiron, Halizah, Daud, Azlina

“…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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B-spline curve fitting with different parameterization methods by Kheng, Jia Shen

“…After generating control points, distance between the generated and original data points is used to identify the error of the algorithm. Later, genetic algorithm and differential evolution optimization are used to optimise the error of the curve. …”
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Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems by Azad, Saiful, Mahmud, Mufti, Kamal Zuhairi, Zamli, Kaiser, M. Shamim, Jahan, Sobhana, Razzaque, Md Abdur

“…In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. …”
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Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation by Chang, Jan Voon

“…The main objective of this research is to validate the NIRR algorithm by developing a comprehensive simulation model using Discrete Event Simulation (DES). …”
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Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation by Chang, Jan Voon, Ahmad, Idawaty

“…The main objective of this research is to validate the NIRR algorithm by developing a comprehensive simulation model using Discrete Event Simulation (DES). …”
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1D Multigrid Solver For Finite Element Method by Azhar, Mohamad Amiruddin

“…The new algorithm also has been tested using time simulation. …”
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A fault syndromes simulator for random access memories by Wan Hasan, Wan Zuha, Abdul Halim, Izhal, Mohd Sidek, Roslina, Othman, Masuri

“…This work is focused on incorporating the March based test algorithm using a software simulator tool for implementing a fast and systematic memory testing algorithm. …”
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Simulation Of Path Optimisation Algorithms by Sim, Choon Yeee

“…Each result of the simulations is included with a discussion of the similarities and difference of the results of each algorithm. …”
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An efficient fault syndromes simulator for SRAM memories by Wan Hasan, Wan Zuha, Abdul Halin, Izhal, Mohd Sidek, Roslina, Othman, Masuri

“…This work is focused on incorporating the March based test algorithm using a software simulator tool for implementing a fast and systematic memory testing algorithm. …”
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Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja by Nurul Hani , Nortaja

“…The steps to calculate a shortest path using A • algorithm is shown by using appropriate examples and related figures. …”
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A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application by Zainal Abidin, Zulkifli

“…Present paper intends to provide a detailed description of a new bio-inspired Metaheuristic Algorithm. …”
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Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

“…In principle, the optimization problem is regarded as a state estimation process. Each agent acts as a Kalman filter and finds solution to the optimization problem using a standard Kalman Filter framework which comprises of prediction, simulated measurement, and estimation phase that uses the best-so-far solution as a reference. …”
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