Search Results - (( software optimization _ algorithm ) OR ( using optimization using algorithm ))
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A comparative study on testing optimization techniques with combinatorial interaction testing for optimizing software product line testing
Published 2025“…An experiment has been conducted on the most commonly used optimization algorithms in previous studies. …”
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2
An adaptive flower pollination algorithm for minimizing software testing redundancy
Published 2017“…Comparison shows that our algorithm performs slightly better than the existing algorithms and thus, the proposed algorithm can potentially be used by researchers and test engineers to obtain optimal test suite requiring the minimum time for software testing.…”
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3
Particle swarm optimization (PSO) for CNC route problem
Published 2002“…We often see many of the method of Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing Algorithm (SAA) and PSO are used for any optimization problems. …”
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Undergraduates Project Papers -
4
Optimization of mycelium growth using genetic algorithm for multi-objective functions
Published 2019“…Trade-off between the objectives, exist for the optimization process. To solve this issues, multi-objective genetic algorithm was chosen as the methodology for this project, specifically using NSGA-ii algorithm. …”
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5
Development of New Genetic Algorithm Software for Blow Mould Process
Published 2008“…The approach is based on newly development of Genetic Algorithm software. In this work, the objectives were to optimized parameters with newly develop software and compare with statistical software. …”
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Successor selection for Ant Colony Optimization technique algorithm / Muhammad Iskandar Isman
Published 2017“…Therefore, in this research, will be use Ant Colony Optimization (ACO) algorithm as an optimize technique that provide a shortest path of defining a successor that is their highest value of criteria. …”
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Towards Software Product Lines Optimization Using Evolutionary Algorithms
Published 2019“…This research provides a framework to compare the performance of different multi-objective Evolutionary Algorithms in software product line context. We report on the problem encoding, variation operators and different types of algorithms: Indicator Based Evolutionary Algorithm (IBEA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) and Strength Pareto Evolutionary algorithm II (SPEA-II). …”
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Proceeding Paper -
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Maintain optimal configurations for large configurable systems using multi-objective optimization
Published 2022“…The proposed approach is also used to generate the optimized test suites with the help of different multi-objective Evolutionary Algorithms (MOEAs).…”
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Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem
Published 2023“…The evolutionary algorithm has been extensively used to solve a range of combinatorial optimization problems. …”
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Algorithm development for optimization of a refrigeration system
Published 2010“…This thesis deals with algorithm development for optimization of a refrigeration system. …”
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11
Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)
Published 2008“…The paper conducts design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multi-objective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. …”
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12
Solving multi-task optimization problems using the sine cosine algorithm
Published 2022“…Often, optimization problems are solved using metaheuristic algorithms which provide good enough solution within reasonable execution time and limited resources. …”
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13
African Buffalo Optimization
Published 2016“…This is an introductory paper to the newly-designed African Buffalo Optimization (ABO) algorithm for solving combinatorial and other optimization problems. …”
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Solving 0/1 Knapsack Problem using Opposition-based Whale Optimization Algorithm (OWOA)
Published 2019“…Practically, as many metaheuristic algorithms, Whale Optimization Algorithm (WOA) may fail in local optimum solution. …”
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15
Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers
Published 2022“…Thus, seven metaheuristic algorithms: Barnacles Mating Optimizer (BMO), Marine Predators Algorithm (MPA), Moth–Flame Optimization (MFO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching–Learning-Based Optimization (TLBO) and Heap-Based Optimizer (HBO) are used to solve two objective functions: power loss and cost minimizations. …”
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MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…A three-dimensional model consisting of the drilling process and pocket milling process was developed using Solidworks software. Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…A three-dimensional model consisting of the drilling process and pocket milling process was developed using Solidworks software. Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…A three-dimensional model consisting of the drilling process and pocket milling process was developed using Solidworks software. Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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19
MINIMIZATION OF MACHINING PROCESS SEQUENCE BASED ON ANT COLONY ALGORITHM AND CONVENTIONAL METHOD
Published 2023“…A three-dimensional model consisting of the drilling process and pocket milling process was developed using Solidworks software. Matlab software was used to develop the algorithm based on Ant Colony, which was then used to optimize the process sequence. …”
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A near-optimal centroids initialization in K-means algorithm using bees algorithm
Published 2009“…This creates problem for novice users especially to those who have no or little knowledge on the data.Trial-error attempt might be one of the possible preference to deal with this issue.In this paper, an optimization algorithm inspired from the bees foraging activities is used to locate near-optimal centroid of a given data set.Result shows that propose approached prove it robustness and competence in finding a near optimal centroid on both synthetic and real data sets.…”
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