Search Results - (( data distribution search algorithms ) OR ( parameter simulation based algorithm ))

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

“…For its fast convergence and for its efficient search procedure, the self-adaptation is proposed in the parameters of the proposed hybrid algorithm. …”
Get full text
Get full text

The comparison of tardiness in scheduling techniques for task distribution using grid simulation tool by Stapa @ Mustapa, Muhamad Azhar

“…The goal of this project is to test on tardiness parameter in local search based algorithms. A good scheduling algorithm normally shows lower value of total tardiness and schedule time. …”
Get full text
Get full text
Get full text

An experience oriented-convergence improved gravitational search algorithm for minimum variance distortionless response beamforming optimum by Darzi S., Tiong S.K., Tariqul Islam M., Rezai Soleymanpour H., Kibria S.

Virtual power plant and microgrids controller for energy management based on optimization techniques by Abdolrasol M.G.M., Mohamed A., Hannan M.A.

Static and self-scalable filter range selection algorithms for peer-to-peer networks by Kweh, Yeah Lun

“…These algorithms are based on the statistical knowledge about the uniform distribution nature of the data and arranged in certain order in the file. …”
Get full text
Get full text

An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm by Abdolrasol M.G.M., Hannan M.A., Mohamed A., Amiruldin U.A.U., Abidin I.B.Z., Uddin M.N.

Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks by Wang Jie, Mohd. Kamal Mohd. Shah, Choong Wai Heng, Nahiyan Al-Azad

Get full text
Get full text
Get full text
Get full text

Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

“…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
Get full text
Get full text

Distributed joint power control, beamforming and spectrum leasing for cognitive two-way relay networks by Iranpanah, Havzhin

“…In the first part of the thesis, distributed power control and beamforming algorithm is proposed in which users operating in the underlay mode can strategically adapt their power levels and maximize their own utilities. …”
Get full text
Get full text

Optimal search algorithm in a big database using interpolation–extrapolation method by Kabir, M. Nomani, Alginahi, Yasser M., Ali, J., Abdel-Raheem, E.

“…Search using traditional binary-search algorithm can be accelerated by employing an interpolation search technique when the data is regularly distributed. …”
Get full text
Get full text
Get full text

A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application by Zainal Abidin, Zulkifli

“…The optimal simulation parameters can be used for the real application. …”
Get full text
Get full text
Get full text

Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

“…The simulation based on Genetic Algorithm are successful develop and the optimum parameters values are obtained from the simulation.…”
Get full text
Get full text

PSAT : a pairwise test data generation tool based on simulated annealing algorithm by Goh, Ghee Hau

“…This research is about the research on developing a Pairwise Test Data Generation Tool based on Simulated Annealing (SA) algorithm which named as PSAT. …”
Get full text
Get full text

Optimization of milling parameters using ant colony optimization by Mohd Saupi, Mohd Sauki

“…The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation.…”
Get full text
Get full text

The Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbed by Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Ahmad A., Abdullah F., Ahmad W.S.H.M.W.

Subjects:

Solving the vehicle routing problem with stochastic demands via hybrid genetic algorithm-tabu search by Ismail, Zuhaimy, Irhamah, Irhamah

“…The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. …”
Get full text
Get full text

Parameter-Less Simulated Kalman Filter by Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Saifudin, Razali

“…Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. …”
Get full text
Get full text
Get full text

GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE by MOHAMMED SHARIFF, NUR ATIQAH

“…The behavior of Genetic Algorithm (GA) where it generates and evolves the parameters towards a high-quality solution gives an advantage in obtaining ideal combination of parameters to fit in with the simulation. …”
Get full text
Get full text

Optimal network reconfiguration and intelligent service restoration prediction technique based on Cuckoo search spring algorithm / Mohamad Izwan Zainal by Zainal, Mohamad Izwan

“…Furthermore, the isolation line is tested on different section and load factor to recognize the improvement of optimal distribution network performance. These output data are analysed to present the optimal prediction output of service restoration using Cuckoo Search Spring – Least Square Support Vector Machine (CSS-LSSVM). …”
Get full text
Get full text

The effect of GA parameters on the performance of GA-based QoS routing algorithm by Yussof S., See O.H.

“…This paper presents the simulation result of the effect of three GA parameters which are maximum iterations, population size and mutation probability on the developed algorithm. � 2008 IEEE.…”