A new bi-objective model of the urban public transportation hub network design under uncertainty by Firoozeh Kaveh (14150877)

“…Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. …”

Power system stability enhancement via coordinated design of a PSS and an SVC-based controller by Abido, M.A.

“…The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. …”
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Coordinated design of robust excitation and TCSC-based damping controllers by Abido, M.A.

“…The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. …”
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Analysis and Assessment of STATCOM-Based Damping Stabilizers for Power System Stability Enhancement by Abido, M. A.

“…Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. …”
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Nested ensemble selection: An effective hybrid feature selection method by Kamalov, Firuz

“…Numerical experiments on synthetic and real-life data demonstrate the effectiveness of the proposed method. The NES algorithm achieves perfect precision on the synthetic data and near optimal accuracy on the real-life data. …”
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Iterative heuristics for multiobjective VLSI standard cellplacement by Sait, Sadiq M.

“…We employ two iterative heuristics for the optimization of VLSI standard cell placement. These heuristics are based on genetic algorithms (GA) and tabu search (TS) respectively. …”
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EVOLUTIONARY HEURISTICS FOR MULTIOBJECTIVE VLSI NETLIST BI-PARTITIONING by Sait, Sadiq M.

“…These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) [sadiq et al., 1999] respectively. …”
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Practical single node failure recovery using fractional repetition codes in data centers by Itani, May

“…Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. …”
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Inferential sensing techniques in industrial applications by Shakil,, Muhammad

“…System delays are obtained by approximating the model by a linear model. Genetic algorithm, which is a heuristic optimization technique, is used to ¯nd the system delays of the linear model, which are used in dynamical neural network model. …”
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Vibration suppression in a cantilever beam using a string-type vibration absorber by Issa, Jimmy S.

“…In the first, the spring stiffness, the position of the second attachment point of the string and a preliminary damping constant are calculated using a genetic algorithm approach where the objective function is the maximum displacement on the beam. …”
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Parameterization and compensation of friction forces using geneticalgorithms by Al-Duwaish, H.N.

“…A PI controller with parameters optimized using genetic algorithms is used to control the position of a DC motor with friction. …”
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AI-Augmented Metasurface Synthesis for Dynamic Beam Steering in Reconfigurable Antenna Arrays by Bostani, Ali

“…As compared to the conventional heuristic methods, for example, genetic algorithms (GA), particle swarm optimization (PSO), the approach based on DRLs has rapid policy convergence, has relatively less computational latency, and is autonomous to adapt to dynamic wireless conditions. …”
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Virtual Inertia Support in Power Systems for High Penetration of Renewables—Overview of Categorization, Comparison, and Evaluation of Control Techniques by Naruttam Kumar Roy (16904544)

“…Integrating intelligent methods, such as fuzzy logic, genetic algorithm, non-convex optimization, and heuristic optimization, signify intelligent control methods. …”

A new variable structure DC motor controller using geneticalgorithms by Al-Hamouz, Z.M.

“…This paper presents a new application of the genetic algorithm for the selection of the variable structure controller (VSC) feedback gains and switching vector for a separately excited DC motor. …”
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Design of a vibration absorber for harmonically forced damped systems by Issa, Jimmy

“…Two different numerical approaches are used in solving the problem; the first is based on the genetic algorithm technique and the second on the downhill simplex method. …”
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Investigating the Impact of Skylights and Atrium Configurations on Visual Comfort and Daylight Performance in Dubai Shopping Malls by SANAD, AYMAN ADEL AHMED

“…Annual simulations are used to assess seasonal variations, while sensitivity analysis identifies key parameters. A genetic algorithm and multi-objective optimisation (MOO) simulations are used to generate optimal configurations, summarised in the form of a Pareto front selection criteria guide the choice of the optimum solution, which is then applied and analysed in a case study. …”
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Practical Multiple Node Failure Recovery in Distributed Storage Systems by Itani, M.

“…We allocate newcomers to nodes with minimal computations and without changing the original optimized plan. The problem is solved using genetic algorithms that search within the feasible solution space. …”
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Improving passenger satisfaction at Kuwait International Airport by using multi-objective optimisation by Alsaber, Ahmad

“…The proposed methods employ optimisation techniques such as mixed integer goal programming (MIGP) and genetic algorithm (GA) to offer a comparative multi-solution outcome towards optimising the passenger quality of services at airport departure check-in. …”
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A Dual Vibration Absorber for Vibration Suppression of Harmonically Forced Systems by El Khoury, Elie

“…Then, a numerical technique based on both the genetic algorithm and the search simplex method is used to calculate the optimal system parameters. …”
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Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information by M. Ghoniem, Rania

“…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …”
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