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121
A new tabu search algorithm for the long-term hydro scheduling problem
Published 2002“…The algorithm is based on using the short-term memory of the tabu search (TS) approach to solve the nonlinear optimization problem in continuous variables of the LTHSP. …”
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122
A decoding algorithm for I-Q space-time coded systems in fadingenvironments
Published 2000“…The decoding problem of I-Q ST codes is formulated. The use of the super-trellis to solve the decoding problem results in an increased decoding complexity. …”
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123
A New Genetic-Based Tabu Search Algorithm For Unit Commitment Problem
Published 2020“…This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. …”
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124
Integrating Genetic Algorithms, Tabu Search, And Simulated Annealing For The Unit Commitment Problem
Published 2020“…This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. …”
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125
Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning
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doctoralThesis -
126
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129
A Dynamic Programming Algorithm For Days-Off Scheduling With Sequence Dependent Labor Costs
Published 2020“…This paper presents a dynamic programming (DP) algorithm for solving a labor scheduling problem with several realistic days-off scheduling constraints and a cost structure that depends on the work sequence for each employee. …”
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130
Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands
Published 2020“…A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. …”
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131
A parallel search tree algorithm for vertex cover on graphical processing units. (c2013)
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masterThesis -
132
A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System
Published 2021“…In the first phase, a genetic algorithm (GA) with a variable population size is used to update the deployment of SPs. …”
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133
A Parallel Genetic Algorithm for the Geometrically Constrained Site Layout Problem with Unequal-Size Facilities
Published 2010“…Parallel genetic algorithms techniques have been used in a variety of computer engineering and science areas. …”
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134
Modeling and Control of a Thermally Driven MEMS Actuator for RF Applications
Published 2017Get full text
doctoralThesis -
135
Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem
Published 2024“…In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. …”
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136
Fuzzy Logic Adaptive Crow Search Algorithm for MPPT of a Partially Shaded Photovoltaic System
Published 2024“…<p dir="ltr">The arbitrary selection of the Crow Search Algorithm (CSA) parameters, the Awareness Probability (AP) and the Flight Length (fl) results in poor convergence performance and efficiency even if the CSA performs well when solving global optimization problems. …”
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137
Revolutionizing sustainable supply chain management: A review of metaheuristics
Published 2023“…It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. …”
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138
Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Published 2024“…Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. …”
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139
Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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