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641
ONBOARD FUEL PUMP FAULT DIAGNOSIS BASED ON IMPROVED SUPPORT VECTOR MACHINE AND EXPERIMENTAL RESEARCH
Published 2016-01-01“…Aiming at solving lacking of failure data and inefficiency,high-cost of now available fault diagnosis methods,a experimental platform of fuel transfer system is developed and a fault diagnosis method based on wavelet packet analysis and improved support vector machine( ISVM) is presented. …”
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642
Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province
Published 2025-01-01“…To address this limitation, this paper proposes a multi-source coordinated optimization strategy based on a bi-level programming model and an improved tent chaotic mapping-memory backtracking zebra optimization algorithm (TCM-MBZOA). …”
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Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements
Published 2025-08-01“…This study presents a novel prediction method using an extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) algorithm. …”
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A new approach for bin packing problem using knowledge reuse and improved heuristic
Published 2024-12-01“…The classic packing solution is a hybrid algorithm based on heuristic positioning and meta-heuristic sequencing, which has the problems of complex solving rules and high time cost. …”
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647
Simultaneous Optimal Network Reconfiguration, DG and Fixed/Switched Capacitor Banks Placement in Distribution Systems using Dedicated Genetic Algorithm
Published 2024-02-01“…As well, integration of distributed generation (DG) units and fixed/switched capacitor banks are effective options for operation cost reduction, reducing system losses, improving voltage profile and increasing voltage stability index in the distribution systems. …”
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648
Mexican axolotl optimization algorithm with a recalling enhanced recurrent neural network for modular multilevel inverter fed photovoltaic system
Published 2025-04-01“…The proposed MAO-RERNN control method integrates the Mexican Axolotl Optimization (MAO) algorithm with a Recalling-Enhanced Recurrent Neural Network (RERNN) to achieve optimal power conversion, improved stability, and reduced total harmonic distortion (THD). …”
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An Improved NSGA‐III With Hybrid Crossover Operator for Multi‐Objective Optimization of Complex Combined Cooling, Heating, and Power Systems
Published 2025-04-01“…The effectiveness of CCHP‐Plus is assessed using three key indicators: primary energy consumption, operational cost, and CO2 emissions. NSGAIII‐AC‐GM delivers a 20% reduction in operational costs and a 10% decrease in CO2 emissions, outperforming seven other algorithms in optimization efficiency on DTLZ and IMOP problems. …”
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Revolutionizing Electric Vehicle Charging Stations with Efficient Deep Q Networks Powered by Multimodal Bioinspired Analysis for Improved Performance
Published 2025-03-01“…These approaches rely on fixed models, often leading to inefficient energy use, higher operational costs, and increased traffic congestion. This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). …”
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653
Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm)
Published 2024-09-01“…Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization. …”
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Assessment of energy management and power quality improvement of hydrogen based microgrid system through novel PSO-MWWO technique
Published 2025-01-01“…The achieved results and numerical analysis affirm the superiority of the proposed technique compared to other traditional methods like mixed integer linear programming (MILP), HOMER, Variable mesh optimization (VMO), and Cataclysmic genetic algorithm in optimizing component sizing, renewable production, hydrogen production, reliability, cost effective, and overall efficacy. …”
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Real-Time Optimal Control Strategy for Multienergy Complementary Microgrid System Based on Double-Layer Nondominated Sorting Genetic Algorithm
Published 2020-01-01“…This model combines with the operation control strategy suitable for multienergy complementary microgrid system, considers the operation mode and equipment characteristics of the system, and uses a double-layer nondominated sorting genetic algorithm to optimize the operation of each equipment in the multienergy complementary system in real time, so as to reduce the operation cost of the system.…”
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Volt/VAr Regulation of the West Mediterranean Regional Electrical Grids Using SVC/STATCOM Devices With Neural Network Algorithms
Published 2025-02-01“…The modeled power system is optimized for the size and location of the FACTS devices by applying genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms to the selected busbars of the FACTS devices, a strategy designed to significantly reduce system losses. …”
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Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm
Published 2024-12-01“…The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. …”
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Impact of integrating type-1 distributed generation on distribution network using modified genetic algorithm and voltage stability index: a technical and cost–benefit analysis appr...
Published 2024-12-01“…The backward-forward sweep formed the backbone of the load flow, while the voltage stability index was used to select the suitable buses where type-1 DG was integrated for optimal performance. The criteria considered for performance evaluation were the cost of energy loss, payback time, active power loss, and network voltage profile improvement without violating the essential network constraints. …”
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