Search Results - (( using function _ algorithm ) OR ( using optimization ((method algorithm) OR (max algorithm)) ))
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1
Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…We introduced two new approaches to normalization techniques to enhance the K-Means algorithms. This is to remedy the problem of using the existing Min-Max (MM) and Decimal Scaling (DS) techniques, which have overflow weakness. …”
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Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network
Published 2024journal::journal article -
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Metaheuristic optimization techniques for localization in outdoor wireless sensor networks: a comprehensive review
Published 2025“…This paper serves as a comprehensive background on localization algorithms and methods used in wireless sensor networks, offering insights for researchers to develop efficient localization algorithms tailored to specific application requirements in diverse work environments.…”
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4
Maximum 2-satisfiability in radial basis function neural network
Published 2020“…This paper presents a new paradigm in using MAX2SAT by implementing in Radial Basis Function Neural Network (RBFNN). …”
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Improving neural networks training using experiment design approach
Published 2005“…This project involves the use of Neural Networks (NN) for function approximation. …”
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6
A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
Published 2018“…Many-objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well-known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum-behaved bat algorithm. …”
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A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function
Published 2018“…Many-objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well-known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum-behaved bat algorithm. …”
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8
Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations
Published 2022“…Meta-heuristic optimizations, namely a genetic algorithm (GA) and a whale optimization algorithm (WOA), are applied to optimize the designed control system. …”
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Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin
Published 2014“…In this paper, an efficient bandwidth allocation algorithm for the uplink traffic in mobile WiMAX is proposed. …”
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Performance comparison of differential evolution and particle swarm optimization in constrained optimization
Published 2012“…This paper presents a comparative study for min-max constrained optimization using PSO and DE. Here, the constrained optimization is represented by some selected standard benchmark functions. …”
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Optimization of mycelium growth using genetic algorithm for multi-objective functions
Published 2019“…Mathematical optimization was typical use for such problem, in which it was supposed to maximizing or minimizing a function. …”
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Undergraduates Project Papers -
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Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions
Published 2018“…The second technique is to solve bi-objective functions by using the BOBAT algorithm. The third technique is an integration of BOGSA with BOBAT to produce a BOGSBAT algorithm. …”
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13
Development and applications of metaheuristic algorithms in engineering design and structural optimization / Ali Sadollah
Published 2013“…Optimization of several truss structures (2D and 3D) with discrete variables were carried out using the proposed methods and the results and computational performances were compared with several well-known metaheuristic algorithms. …”
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14
Reactive max-min ant system: An experimental analysis of the combination with K-OPT local searches
Published 2015“…Ant colony optimization (ACO) is a stochastic search method for solving NP-hard problems. …”
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Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy
Published 2014“…For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. …”
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Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. …”
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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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Ant colony optimization in dynamic environments
Published 2010“…Apart from the size of the optimization problem, how the swapping interval affects the dynamic optimization by the ant algorithms is also investigated. …”
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Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller
Published 2010“…GA have been successfully applied to solve many optimization problems. This research proposes a method that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in solving the problems. …”
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An efficient method for determining all the extreme points of function with one variable
Published 2014“…The algorithm is the combination of the filled function's algorithm and Inner Iteration algorithm, called IRH's algorithm, in which Inner Iteration algorithm works at the domain which is usually ignored by filled function's algorithm.…”
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