Search Results - (( problem application based algorithm ) OR ( parameters optimization method algorithm ))
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1
Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri
Published 2020“…Based on the capabilities of the metaheuristic algorithms, this research is proposing the enhanced Gravitational Search Algorithm (eGSA) to solve the nano-process parameter optimization problem. …”
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2
Finite impulse response optimizers for solving optimization problems
Published 2019“…The classification of estimation-based metaheuristic algorithms has been introduced for solving optimization problems. …”
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3
Finite impulse response optimizers for solving optimization problems
Published 2019“…The classification of estimation-based metaheuristic algorithms has been introduced for solving optimization problems. …”
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4
Optimization of turning parameters using genetic algorithm method
Published 2008“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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5
Optimal power flow based on fuzzy linear programming and modified Jaya algorithms
Published 2017“…In addition, two novel Jaya-based methods namely, the modified Jaya (MJaya) algorithm and quasi-oppositional modified Jaya (QOMJaya) algorithm are proposed to solve different MOOPF problems. …”
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6
A firefly algorithm based hybrid method for structural topology optimization
Published 2020“…In this paper, a firefly algorithm based hybrid algorithm through retaining global convergence of firefly algorithm and ability to generate connected topologies of optimality criteria (OC) method is proposed as an alternative method to solve stress-based topology optimization problems. …”
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7
Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Published 2020“…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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8
Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem
Published 2023“…Hybridization in evolutionary algorithm mechanisms such as initialization methods, local searches, specific design operators, and self-adaptive parameters enhance the algorithm’s performance. …”
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9
Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm
Published 2023“…For optimal maintenance scheduling with low cost, high reliability, and low violation, the parameter values of the PACS algorithm were tuned using the Taguchi and Gray Relational Analysis (Taguchi-GRA) method through search-based approach. …”
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10
Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm
Published 2018“…However, to fully utilize the algorithm, the parameter of the algorithm need to be set properly. …”
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11
Optimal power flow using the Jaya algorithm
Published 2016“…Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. …”
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Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi
Published 2018“…In the next step, the parameter identification as an optimization problem is solved by Moth-flame optimization, which is a novel nature-inspired heuristic algorithm. …”
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13
Identification of continuous-time model of hammerstein system using modified multi-verse optimizer
Published 2021“…It has been successfully implemented and used in various areas such as machine learning applications, engineering applications, network applications, parameter control, and other similar applications to solve optimization problems. …”
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14
Development of optimization Alghorithm for uncertain non-linear dynamical system
Published 2004“…An algorithm that definitely can satisfy the objectives is the Dynamic Integrated Systems Optimization and Parameter Estimation (DISOPE) algorithm. …”
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15
Optimization of milling parameters using ant colony optimization
Published 2008“…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.…”
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16
A review of training methods of ANFIS for applications in business and economic
Published 2016“…Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. …”
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17
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Notably, the SPSA and GSPSA based methods only produced the optimal design parameter at the final iteration while it may keep a better design parameter during the tuning process if it has a memory feature. …”
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18
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Notably, the SPSA and GSPSA based methods only produced the optimal design parameter at the final iteration while it may keep a better design parameter during the tuning process if it has a memory feature. …”
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19
An application of backtracking search algorithm in designing power system stabilizers for large multi-machine system
Published 2023“…Damping; Eigenvalues and eigenfunctions; Electric power systems; Learning algorithms; Optimization; Particle swarm optimization (PSO); Problem solving; State space methods; Test facilities; Backtracking search algorithms; Multi machine power system; Power system damping; Power system oscillations; Power system stability; Power System Stabilizer; System stability; algorithm; Article; backtracking search algorithm; bacterial foraging optimization algorithm; machine; mathematical analysis; mathematical computing; mathematical parameters; particle swarm optimization; power supply; power system stabilizer; process optimization; statistical model…”
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Nature-inspired parameter controllers for ACO-based reactive search
Published 2015“…This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic.The sensitivity to parameters’ selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems.These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. …”
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