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Sub-route reversal repair mechanism and differential evolution for urban transit network design problem
Published 2017“…All proposed algorithms are executed using Python programming language, and the computational results show that the proposed algorithms improve the best-so-far results from the literature in most cases.…”
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Comparative analysis of spiral dynamic algorithm and artificial bee colony optimization for position control of flexible link manipulators
Published 2024“…Additionally, the spiral dynamics algorithm, which draws on principles from complex adaptive systems and human values, provides a framework for modelling system evolution. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…The third method is the hybridization of BPSO and Binary Differential Evolution, namely Binary Particle Swarm Optimization Differential Evolution (BPSODE). …”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…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. …”
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5
Evaluation of Search Result of Document Search Based GA (DSEGA)
Published 2004“…The space in searched in a directed, stochastic manner, and the method of searching borrows some ideas from evolution. …”
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Reservoir system modelling using nondominated sorting genetic algorithm in the framework of climate change
Published 2014“…In conclusion, this finding contributes toward the development of models using evolution algorithm and statistical methods for sustainable water resources planning and management in the context of future climate change…”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…Hence, this research has proposed three enhanced frameworks, namely, Optimized Gravitational-based (OGC), Density-Based Particle Swarm Optimization (DPSO), and Variance-based Differential Evolution with an Optional Crossover (VDEO) frameworks for data clustering. …”
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Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy
Published 2024“…The HM emerges as a dominant strategy, driven by the Multi-Objective Differential Evolution (MODE) algorithm under literature-based control parameter settings for the mathematical model. …”
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9
Evolution, design, and future trajectories on bipedal wheel-legged robot: A comprehensive review
Published 2023“…The analysis encompasses optimization techniques, sensor integration, machine learning, and adaptive control methods, evaluating their impact on robot capabilities. …”
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Reliability assessment of power system generation adequacy with wind power using population-based intelligent search methods
Published 2017“…This study sought to examine the performance of three newly proposed techniques, for reliability assessment of the power systems, namely Disparity Evolution Genetic Algorithm (DEGA), Binary Particle Swarm Optimisation (BPSO), and Differential Evolution Optimization Algorithm (DEOA). …”
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems
Published 2016“…The strength of the algorithm lies in the fact that it assists the evolution of a population of individuals who would thrive in the survival of the fittest towards the next generation. …”
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12
Optimal placement of unified power flow controller by dynamic implementation of system-variable-based voltage-stability indices to enhance voltage stability
Published 2016“…Furthermore, to verify the suitability of the explored locations, a comparative study has been conducted after placing UPFC in the present locations and other locations obtained using optimization techniques like particle swarm optimization (PSO), differential evolution (DE), genetic algorithm (GA), and bacteria foraging algorithm (BFA). …”
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EMG Feature Selection And Classification Using A Pbest-Guide Binary Particle Swarm Optimization
Published 2019“…In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. …”
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Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images
Published 2016“…The enhanced contrast image is further optimized by the particle swarm optimization algorithm. …”
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Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images
Published 2016“…The enhanced contrast image is further optimized by the particle swarm optimization algorithm. …”
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16
Optimal solution to the fractional knapsack problem for LTE overload-state scheduling
Published 2016“…System performance has been evaluated in terms of loss, delay and throughput in comparison with reference algorithms.…”
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Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation
Published 2018“…The proposed hybrid models include particle swarm optimization, genetic algorithm and differential evolution. …”
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Base pressure control through micro jets at supersonic Mach numbers using experimental and machine learning approach
Published 2026“…Ratios (10–1) were varied to evaluate their impact on flow evolution and base pressure. …”
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The superiority of feasible solutions-moth flame optimizer using valve point loading
Published 2024“…The MFO, Grey Wolf Optimizer (GWO), Success-history-based Parameter Adaptation Technique of Differential Evolution - Superiority of Feasible Solutions (SHADE-SF), and Superiority of Feasible Solutions-Moth Flame Optimizer (SF-MFO) algorithms are applied to address the OPF problem with two objective functions: (1) reducing energy production costs and (2) minimizing power losses. …”
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