Search Results - (( frames loading optimization algorithm ) OR ( java expectation optimisation algorithm ))

Optimization of Off-Centre bracing system using Genetic Algorithm by Yazdi, H.A. Mosalman, Ramli Sulong, Nor Hafizah

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Single Machine Connected Infinite Bus system tuning coordination control using biogeography-based optimization algorithm by Kasilingam G., Pasupuleti J., Bharatiraja C., Adedayo Y.

“…In this paper, the design of hybrid coordinated damping controller (power system stabilizer (PSS) and proportional integral derivative (PID) controller) is articulated as an optimization problem. The objective function J is framed using Integral square error (ISE) and the optimal parameters can be obtained by minimizing the objective function using the proposed Biogeography Based Optimization (BBO) algorithm. …”

Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture by Ibrahim, Abeer A.Z., Hashim, Fazirulhisyam, Sali, Aduwati, Noordin, Nor K., Navaie, Keivan, Fadul, Saber M.E.

“…A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. …”
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Shunt active power filter with modified synchronous reference frame technique and fuzzy logic current controller for harmonic mitigation by Suleiman, Musa

“…From both results, the proposed algorithms show good performances when compared with the conventional algorithms. …”
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Critical insight for MAPReduce optimization in Hadoop by Khan, Burhan ul Islam, Olanrewaju, Rashidah Funke, Altaf, Hunain, Shah, Asadullah

“…Apache Hadoop is one the most used cloud frame work for cloud infrastructure. The predominant philosophy behind Hadoop optimization is the optimization of MapReduce, which is a dominant programming platform effective in bringing a=bout many functional enhancements as per scheduling algorithms developed and implemented. …”
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Success history moth flow optimization for multi-goal generation dispatching with nonlinear cost functions by Alam, Mohammad Khurshed, Mohd Herwan, Sulaiman, Sayem, Md. Shaoran, Ringku, Md Mahfuzer Akter, Imtiaz, Shahriar, Khan, Rahat

“…This SHMFO the valve-point loading problem is a multi-objective nonlinear optimization problem including generator capacity limits and power balance. …”
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A Fast Scheduling Algorithm for WDM Optical Networks by Cheah, Cheng Lai

“…Two variations of implementation of the scheduling algorithm have been proposed, namely the Variable Frame Size (VFS) and Limited Frame Size (LFS) schemes. …”
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A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources by Ahmadipour M., Ali Z., Ramachandaramurthy V.K., Ridha H.M.

Novel Architecture for Transactive Energy Management Systems with Various Market Clearing Strategies by Arun, S.L., Bingi, K., Vijaya Priya, R., Jacob Raglend, I., Hanumantha Rao, B.

“…Further, an optimal load scheduling algorithm is developed at the end-users' premises to improve the profits in the LEM. …”
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Optimal Charging Schedule Coordination of Electric Vehicles in Smart Grid by Zalnidzam, Wan Iqmal Faezy Wan, Mohamad, Hasmaini, Salim, Nur Ashida, Mokhlis, Hazlie, Yasin, Zuhaila Mat

“…The performance of the schedule algorithm developed using particle swarm optimization (PSO) technique is evaluated at the IEEE-33 Bus radial distribution system in a set time frame of charging period. …”
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Machine Learning-Enabled Communication Approach for the Internet of Medical Things by Rahim Khan, Abdullah Ghani, Samia Allaoua Chelloug, Mohammed Amin, Aamir Saeed, Jason Teo

“…For this purpose, the proposed scheme utilises a well-known technique, i.e., Kruskal’s algorithm, to find an optimal path from source to destination wearable devices. …”
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Development of an integrated scheduling model for handling equipment in automated port container terminals by Sadeghian, Syed Hamidreza

“…In addition, results proved that the modified meta-heuristic algorithm is able to find near optimal solutions and on average, the solutions found by the GA algorithm are only 0.2% worse than the optimal solutions and in the worst case in the test cases this difference is 2.3% which is acceptable. …”
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