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1
Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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Thesis -
2
Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line
Published 2010“…Conclusion/Recommendations: The results obtained from intelligence based genetic algorithm were used as an initial point for fine-tuning by simulated annealing to increase the quality of solution. …”
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3
GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
Published 2020“…This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. …”
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4
Parameter-Less Simulated Kalman Filter
Published 2017“…Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. …”
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5
Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems
Published 2021“…These algorithms can influence the convergence to find an efficient optimal solution. …”
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6
Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization. …”
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Monograph -
7
A harmony search-based learning algorithm for epileptic seizure prediction
Published 2016“…The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. …”
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8
Finite impulse response optimizers for solving optimization problems
Published 2019“…In this work, three new estimation-based metaheuristic algorithms are introduced. The first algorithm is a single-agent-based algorithm, named Single-agent FIR optimizer (SAFIRO). …”
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9
Finite impulse response optimizers for solving optimization problems
Published 2019“…In this work, three new estimation-based metaheuristic algorithms are introduced. The first algorithm is a single-agent-based algorithm, named Single-agent FIR optimizer (SAFIRO). …”
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10
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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11
A branch and bound and simulated annealing approach for job shop scheduling
Published 2004“…In the branch and bound approach, the job shop scheduling problem is represented by a disjunctive graph, then the optimal schedule is obtained using the branch and bound algorithm while simulated annealing is a local search based algorithm which will slightly perturb the initial feasible solution to decrease the makespan. …”
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12
Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences
Published 2021“…To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. …”
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13
Improved cuckoo search based neural network learning algorithms for data classification
Published 2014“…This research proposed an improved CS called hybrid Accelerated Cuckoo Particle Swarm Optimization algorithm (HACPSO) with Accelerated particle Swarm Optimization (APSO) algorithm. …”
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14
Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
Published 2021“…The proposed DNN algorithm is structured to incorporate initial/boundary conditions in cylindrical coordinates and approximate the solution without the aid of any simulated or training data. …”
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15
Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…Meta-heuristic algorithms have widely used in HPAs due to their global optimization ability. …”
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16
Vehicle pick-up and drop-off schedule optimization in a university setting
Published 2024“…A simulated annealing-based multi-directional iterative local search algorithm is employed for solution optimization. …”
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17
Cubic spline based particle swarm optimization for microstrip antennas
Published 2011“…This paper presents an optimization technique for microstrip antennas using cubic spline based particle swarm optimization (CSPSO). …”
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18
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The new proposed method (MBPSO+MKN+GK) Gustafson- Kessel algorithm (GK)integrated with modified of Kohonen Network algorithm (MKN)and modified binary particle swarm optimization (MBPSO) was used to classify the credit scoring data. …”
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Thesis -
19
Resource management in grid computing using ant colony optimization
Published 2011“…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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Monograph -
20
Energy and spectral efficiency balancing algorithm for energy saving in LTE downlinks
Published 2021“…The scheme is formulated as a Multi-objective Optimization Problem (MOP) and its Pareto optimal solution is examined. …”
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