Search Results - (( java implication _ algorithm ) OR ( (parameter OR parameters) optimization strategy algorithm ))
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On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
Published 2015“…Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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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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A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites
Published 2019“…Owing to its proven performance in many other optimization problems, the adoption of the parameter-free Teaching Learning-based Optimization (TLBO) algorithm as a new t-way strategy is deemed useful. …”
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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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Undergraduates Project Papers -
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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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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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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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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…This research focuses on the use of binaryencoded genetic algorithm (GA) to implement efficient search strategies for the optimal architecture and training parameters of a multilayer feed-forward ANN. …”
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Migrating Birds Optimization based Strategies for Pairwise Testing
Published 2015“…For pairwise testing, test cases are designed to cover all possible pair combinations of input parameter values at least once. In this paper, we investigate the adoption of Migrating Birds Optimization (MBO) algorithm as a strategy to find an optimal solution for pairwise test data reduction. …”
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Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm
Published 2018“…In line with the emerging field called Search based Software Engineering, many recently developed t-way strategies have adopted meta-heuristic algorithms as the basis of their implementations such as Simulated Annealing, Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization, Harmony Search and Cuckoo Search, owing their superior performance in term of test size reduction as compared to general computational based strategies, such as General t-way, Test Vector Generator, In Parameter Order General, Jenny, and Automatic Efficient Test Generator. …”
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Robustness Analysis Of An Optimized Controller Via Particle Swarm Algorithm
Published 2017“…The finding shows that the SMC that utilized the PSO algorithm parameters are capable to produce smaller robustness index values, which demonstrated better robustness characteristic in confront with the variation of the system parameter.…”
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Input-output based relation combinatorial testing using whale optimization algorithm for generating near optimum number of test suite
Published 2025“…This study proposes a combinatorial testing method utilizing the Whale Optimization Algorithm (WOA). The study compares the performance of WOA with various existing strategies, such as Greedy, Density, TVG, Union, ParaOrder, ReqOrder, ITTDG, AURA, Java Algorithm (CTJ), TTSGA, and AFA. …”
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A new HMCR parameter of harmony search for better exploration
Published 2016“…Therefore, there is a need to improve the fine tuning of the parameters. This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. …”
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A new HMCR parameter of harmony search for better exploration
Published 2015“…Therefore, there is a need to improve the fine tuning of the parameters. This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. …”
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One Parameter at a time Combinatorial Testing Strategy Based on Harmony Search Algorithm OPAT-HS
Published 2018“…Even though there are some existing optimization algorithm based combinatorial testing strategies, that minimize the number of test cases, but most of these strategies based on one-test-at-a-time (OTAT) approach and none of them has adopted the one-parameter-at-a-time (OPAT) approach. …”
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Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer
Published 2024“…The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. …”
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