Search Results - (( basic optimization method algorithm ) OR ( simulation optimization system algorithm ))*
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1
Particle swarm optimization (PSO) for CNC route problem
Published 2002“…We often see many of the method of Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing Algorithm (SAA) and PSO are used for any optimization problems. …”
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Undergraduates Project Papers -
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Voting algorithms for large scale fault-tolerant systems
Published 2011“…In a nut shell, we tried to introduce voting algorithms and structures suitable for large scale fault-tolerant systems which have optimal and proper time complexity (in parallel voting algorithms) and more reliability and availability (in enhanced m-out-of-n voting algorithm) compared to the basic types.…”
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3
Genetic algorithm based method for optimal location placement of flexible ac transmission system devices for voltage profile improvement
Published 2011“…The modeling of power systems, FACTS devices and genetic algorithms are performed through MATLAB/PSAT simulation. …”
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4
Ant colony optimization for solving economic dispatch of power system: article / Muhammad Shukri Che Hashim
Published 2009“…An economic dispatch problem, consisting of six generating units is applied to compare the performance of the proposed method with those of genetic algorithm (GA) and simulated annealing (SA). …”
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Ant colony optimization for solving economic dispatch of power system / Muhammad Shukri Che Hashim
Published 2009“…An economic dispatch problem, consisting of six generating units is applied to compare the performance of the proposed method with those of genetic algorithm (GA) and simulated annealing (SA). …”
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6
Tree physiology optimization on SISO and MIMO PID control tuning
Published 2018“…This concept may improve the search mechanism with a better trade-off between diversification and intensification search. A simulation of SISO control system and an industrial application of MIMO control are applied to demonstrate the effectiveness of the proposed algorithm and compared with other optimization methods such as particle swarm optimization, Zieglerâ��Nichols, Tyreusâ��Luyben and Chienâ��Hronesâ��Reswick methods. …”
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Tree physiology optimization on SISO and MIMO PID control tuning
Published 2018“…This concept may improve the search mechanism with a better trade-off between diversification and intensification search. A simulation of SISO control system and an industrial application of MIMO control are applied to demonstrate the effectiveness of the proposed algorithm and compared with other optimization methods such as particle swarm optimization, Zieglerâ��Nichols, Tyreusâ��Luyben and Chienâ��Hronesâ��Reswick methods. …”
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Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
Published 2025“…To solve the optimization planning model, a hybrid optimization algorithm is proposed, combining the non-dominating sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO). …”
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INTELLIGENT OPTIMIZATION OF INTERLINE POWER FLOW CONTROLLER IN TRANSMISSION SYSTEM
Published 2010“…The optimal parameters are derived to minimize the transmission line losses using three intelligent optimization techniques, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). …”
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10
A basic study on hybrid systems for small race car to improve dynamic performance using lap time simulation
“…The realization of the proposed hybrid system requires independent control algorithms for the two power systems, engine and electric motor, that take into consideration the state of the vehicle and the driver’s input; this system can be assumed to be a servo model system with multiple inputs and outputs and analyzed to obtain the optimal operation algorithm. …”
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Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
Published 2010“…The design of the APFLC is initially started with the designation of Basic FLC with two input and single output system. …”
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12
Intelligently tuned weights based robust H∞ controller design for pneumatic servo actuator system with parametric uncertainty
Published 2011“…The PSO algorithm is used to minimize the infinity norm of the transfer functions matrix of the nominal closed loop system to obtain the optimal parameters of the weighting function. …”
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Optimal resource allocation for NOMA wireless networks
Published 2022“…The major goal is to maximize the users’ maximum weighted sum rate. The suggested algorithm’s most notable feature is that it converges to the global optimal solution. …”
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14
Parametric modelling of a TRMS using dynamic spread factor particle swarm optimisation
Published 2009“…The proposed method formulates a modified inertia weight algorithm by using a dynamic spread factor (SF). …”
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Proceeding Paper -
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Potential of data minimization techniques approach in improving DNA sequences alignment and comparison / Syed Abdul Mutalib Al Junid … [et al.]
Published 2012“…This study will initially involve algorithm modification, mathematical formulation, logic design and simulation for data minimization and existing DNA sequences alignment algorithm. …”
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Research Reports -
17
An Environmentally Energy Dispatch Using New Meta Heuristic Evolutionary Programming
Published 2018“…Basically,one important issue in the power system network is to provide the optimal Economic Load Dispatch (ELD) solution in order to guarantee the sustainable consumer load demand.However,today ELD solution is essential to include together with the environmental aspect and known as Environmental Economic Load Dispatch (EELD).For that reason, many researchers continue in the development of new simulation tool specifically to overcome the EELD problems.Therefore,this study prepared an improved hybrid metaheuristic technique named as New Meta Heuristic Evolutionary Programming (NMEP) to provide the best possible solution in solving the identified single objective and multi objective functions for EELD solution.This new technique a merging cloning strategy that involved in an Artificial Immune System (AIS) algorithm into algorithm of Meta Heuristic Evolutionary Programming (Meta-EP).The development of NMEP technique is to minimize total cost,reduce the total emission during generator operation through the common formula in EELD and lowest total system loss.Besides that,all mentioned objective functions were also optimized together simultaneously that formulated using the weighted sum method before had been executed on the multi objective NMEP or called MONMEP.Both individual and multi objective NMEP techniques performance were verified among other two common heuristic methods known as AIS and Meta-EP techniques.In addition,the best possible solution defined using the aggregate function method.Through this method,the selection of the best MOEELD solution became effortless as compared with MO individually that required compare two or more objective function in one time manually.Among those three optimization techniques the lowest total aggregate values mostly resulted via the NMEP technique.Based upon that,the proposed technique is proving as the outstanding method compared with Meta-EP and AIS techniques in solving the EELD problem for both standard IEEE 26 bus and 57 bus systems.…”
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Optimal planning and design of hybrid renewable energy system for rural healthcare facilities / Olatomiwa Lanre Joseph
Published 2016“…Then, utilization of cost-effective optimization algorithm for optimal sizing of the energy resources and other system components with accurate mathematical models for energy management of the entire hybrid system. …”
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19
Even-odd scheduling based energy efficient routing for wireless sensor network (WSN) / Muhammad Zafar Iqbal Khan
Published 2022“…Using the simulation software, it was observed that the alive nodes are higher in numbers i.e. 189 nodes after 500 rounds for the proposed routing algorithm compared to the previous methods which had only 150 alive nodes. …”
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20
A calibration framework for swarming ASVs' system design
Published 2012“…Swimming pool provides as a controlled calibration framework for the proposed swarming algorithm. The performance of the system is determined by firstly, its capability to allow the various robots to communicate amongst themselves in order to reach the desired location and secondly, the use of optimization in its searching strategy. …”
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