Search Results - (( evolution optimization parallel algorithm ) OR ( variable optimization strategy algorithm ))
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1
Optimization of extractive Automatic Text Summarization using Decomposition-based Multi-objective Differential Evolution and parallelization
Published 2024“…The central challenge in Automatic Text Summarization (ATS) is efficiently generating machine-generated text summaries through optimization algorithms, a critical component for systems dealing with textual information processing. …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
Published 2020“…To evaluate the PMT’s performance and adaptability, the PMT was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. …”
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3
PMT: opposition-based learning technique for enhancing meta-heuristic performance
Published 2019“…To evaluate the PMT's performance and adaptability, the PMT has been applied to four contemporary meta-heuristic algorithms, differential evolution (DE), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA), to solve 15 well-known benchmark functions. …”
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OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM
Published 2011“…In this thesis, a new optimization GA-based algorithm that emanates from modification of conventional GA to reduce the iterations number and the duration time, namely, semi-parallel operation genetic algorithm (SPOGA) is proposed. …”
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Application Of Genetic Algorithms For Robust Parameter Optimization
Published 2010“…Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. …”
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation
Published 2018“…This study presented the most recent variable interaction strength (VS) CT strategy using an enhanced variant in the greedy algorithm. …”
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Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm
Published 2023Article -
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An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time
Published 2015“…This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). …”
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Multi-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problems
Published 2015“…In this paper, a new design strategy is presented where variable operating conditions, which better represent real-world problems, are considered. …”
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A swarm intelligent approach for multi-objective optimization of compact heat exchangers
Published 2023Article -
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Harmony search-based robust optimal controller with prior defined structure
Published 2013“…In this approach, a combination of interacting two levels HS optimization algorithm is presented. In the first level, a new method for analytical formulation of integral square error cost function based on controller variables is elaborated for performance evaluation purposes by the proposed optimization algorithm. …”
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CTJ: Input-output based relation combinatorial testing strategy using Jaya algorithm
Published 2019“…To overcome the combinatorial optimization problem, Jaya algorithm is proposed to apply in this project since metaheuristic algorithm is fast in optimization and this strategy is named as CTJ. …”
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Undergraduates Project Papers -
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Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
Published 2024“…Improved mean fitness function values were also revealed in the TRS (11.63%) and EMPS (69.63%) assessments, surpassing the conventional algorithm. This proposed algorithm produced solutions with superior accuracy and consistency compared to various established metaheuristic strategies, including particle swarm optimizer, grey wolf optimizer, multi-verse optimizer, AOA, and a hybrid optimizer (average multi-verse optimizer-sine-cosine algorithm).…”
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Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis
Published 2014“…To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. …”
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Development of Genetic Algorithm Procedure for Sequencing Problem in Mixed-Model Assembly Lines
Published 2009“…It confirms that the proposed genetic algorithm procedure is able to tackle the problem complexity and reach to optimal solutions in different production strategies. …”
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Automatic control of flotation process using computer vision
Published 2015“…In addition, several algorithms were implemented to measure the other important image variables such as froth velocity,froth color and bubble collapse rate. …”
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Single and Multiple variables control using Tree Physiology Optimization
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A memory optimal BFGS neural network training algorithm
Published 2002“…This paper considers the implementation of a novel memory optimal neural network training algorithm which maximises performance in relation to available memory. …”
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