Search Results - (( evolution optimisation based algorithm ) OR ( program implementation clustering algorithm ))

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.

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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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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Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim by Che Ibrahim, Mohd Erman Safawie

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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

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Detection of the spread of Covid-19 in Indonesia using K-Means Clustering Algorithm / Mohammad Yazdi Pusadan ... [et al.] by Pusadan, Mohammad Yazdi, Rabbani, Mohammad Abied, Ardiansyah, Rizka, Ngemba, Hajra Rasmita

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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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Application-Programming Interface (API) for Song Recognition Systems by Murtadha Arif Sahbudin, Chakib Chaouch, Salvatore Serrano

Implementation of Hybrid Indexing, Clustering and Classification Methods to Enhance Rural Development Programme in South Sulawesi by Muhammad, Faisal

“…Village development and assistance program is a strategy implemented to accelerate socio cultural development to increase the capacity of village governance and more organized administration. …”
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Parallel Implementation of Two Level Barotropic Models Applied to the Weather Prediction Problem by Bahri, Susila

“…Then the parallel algorithms are constructed and run using the Beowulf Cluster machine. …”
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A coverage path planning approach for autonomous radiation mapping with a mobile robot by Abd Rahman N.A., Sahari K.S.M., Hamid N.A., Hou Y.C.

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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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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Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

“…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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Feature clustering for pso-based feature construction on high-dimensional data by Swesi, Idheba Mohamad Ali Omer, Abu Bakar, Azuraliza

“…The Redundancy-Based Feature Clustering (RFC) algorithm was applied to choose the most informative features from the original data, while PSO was used to construct new features from those selected by RFC. …”
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An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

“…Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. …”
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Enhancement of Ant System Algorithm for Course Timetabling Problem by Djamarus, Djasli

“…In order to develop a new algorithm for the course scheduling problem, this research follows the experimental research methodology that consist of problem analysis, designing algorithm, implementing algorithm as a computer program in order to examine the results, analyzing the results, and if necessary improving the algorithm by doing all those activities over and over again. …”
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