-
301
Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle
Published 2025“…Furthermore, the reduction in sideslip angle, excellent traction through minimizing tire slip ratio, avoiding oversteering and understeering, and maintaining an acceptable range of energy optimization are demonstrated for DRL controllers, especially for the TD3 and CL TD3 algorithms.…”
-
302
An Auction-Based Scheduling Approach for Minimizing Latency in Fog Computing Using 5G Infrastructure
Published 2020Get full text
doctoralThesis -
303
-
304
-
305
-
306
-
307
A fix and optimize method based approximate dynamic programming approach for the strategic fleet sizing and delivery planning problem
Published 2024“…In this study, we suggest an approximate Dynamic Programming algorithm, with a look ahead strategy, that uses the fix and optimize method as the imbedded heuristic for solving integrated fleet composition and replenishment planning problem. …”
-
308
Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO
Published 2022“…The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. …”
-
309
Enhanced DC Microgrid Protection: a Neural Network and Wavelet Transform Approach
Published 2024Get full text
doctoralThesis -
310
-
311
-
312
An optimization approach to increasing sustainability and enhancing resilience against environmental constraints in LNG supply chains: A Qatar case study
Published 2022“…The developed model, which is implemented using the Binary Particle Swarm Optimization algorithm subjected to economic and environmental objectives within an overarching strategic aim for sustainability and resilience. …”
-
313
Loss Model Control for Efficiency Optimization and Advanced Sliding Mode Controllers with Chattering Attenuation for Five-Phase Induction Motor Drive
Published 2024“…Secondly, this paper also proposes a Loss Model Controller (LMC) for FPIM energy optimization. Thus, the suggested LMC chooses the optimal flux magnitude required by the FPIM for each applied load torque, which consequently reduces the losses and the FPIM efficiency. …”
-
314
Dynamic Cyber Resilience of Interdependent Critical Information Infrastructures
Published 2021“…The technology stack was also enhanced with three new algorithms and five protocols. The proposed solution was optimized using the iterative four-objective cycle based on previous primary phase results. …”
Get full text
-
315
Incremental Genetic Algorithm
Published 2006“…Classical Genetic Algorithms (CGA) are known to find good sub-optimal solutions for complex and intractable optimization problems. …”
Get full text
Get full text
article -
316
-
317
A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
Published 2023“…We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm, gravitational search algorithm, African vultures optimization, and the Aquila Optimizer using well-known unimodal, multimodal benchmark functions. …”
Get full text
-
318
Blood Glucose Regulation Modelling and Intelligent Control
Published 2024Get full text
doctoralThesis -
319
Metaheuristic Optimization‐Based Sliding Mode Control With Modified Perturb and Observe for Controlling MPPT of a PV Interfaced Grid Connected System
Published 2025“…Also, this article introduces the meta‐heuristic algorithm mountain gazelle optimization (MGO), which is incorporated to optimize the parameters of the sliding mode controller (SMC) to ensure the solar PV (SPV) MPP extraction. …”
-
320
Topics in graph algorithms
Published 2003“…Coping with computational intractability has inspired the development of a variety of algorithmic techniques. The main challenge has usually been the design of polynomial time algorithms for NP-complete problems in a way that guarantees some, often worst-case, satisfactory performance when compared to exact (optimal) solutions. …”
Get full text
Get full text
Get full text
masterThesis