A multi-modal feature combination mechanism for identification of harmonic load in distribution networks based on artificial intelligence models by Renzeng Yang, Shuang Peng, Gang Yao

“…Secondly, using an optimization algorithm, the penalty parameter and the number of intrinsic mode functions in variational mode decomposition are fine-tuned to decompose the harmonic power sequence and extract intrinsic mode functions. …”
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RRMSE-enhanced weighted voting regressor for improved ensemble regression. by Shikun Chen, Wenlong Zheng

“…This uniform weighting approach doesn't consider that some models may perform better than others on different datasets, leaving room for improvement in optimizing ensemble performance. To overcome this limitation, we propose the RRMSE (Relative Root Mean Square Error) Voting Regressor, a new ensemble regression technique that assigns weights to each base model based on their relative error rates. …”
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Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings by Vinoth Kanna I, Raja Subramani, Maher Ali Rusho, Shubham Sharma, Ramachandran T, Abinash Mahapatro, Deepak Gupta, Jasmina Lozanovic

“…This research utilizes swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and hybrid PSO-ACO-to optimize energy efficiency and thermal comfort in smart building HVAC systems. …”
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Improvement of Methodological Tools for Business Analysis of the Effective Company’s Performance by N. S . Plaskova

“…The purpose of the study is to develop methods of analysis and algorithms for calculating the most important characteristics of assessing the performance of economic entities for internal management and external stakeholders based on a complementary approach to the use of classical methods of forming a piece of information and analytical base by clarifying the values of the indicators used in financial and management reporting and supplementing the list of analytical indicators. …”
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Optimal entanglement distribution within a multi-ring topology by B. C. Ciobanu, T. A. Calafeteanu, A. B. Popa, R. Tătăroiu, P. G. Popescu

“…We propose algorithms that solve the problem of entanglement distribution in terms of optimal time needed to satisfy a set of entanglement resupply requests with given network resources, as well as the problem of optimal resources in order to satisfy a set of entanglement resupply requests within a single entanglement transmission and measurement step. …”
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Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train by Chengda Yang, Kun Miao, Jieyuan Wang

“…Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. …”
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Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network by Qiyue Huang, Yuqing Zheng, Yuxuan Xu

“…In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. …”
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Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization by Haitao Xu, Pan Pu, Feng Duan

“…Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO), which is the traditional Ant Colony Optimization (ACO) fusing improved K-means and crossover operation. …”
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Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs by Kaifeng Geng, Chunming Ye, Zhen hua Dai, Li Liu

“…This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. …”
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Optimization of Multiperiod Mixed Train Schedule on High-Speed Railway by Wenliang Zhou, Junli Tian, Jin Qin, Lianbo Deng, TangJian Wei

“…And its heuristic algorithm is designed to optimize the multiperiod mixed train schedule beginning with generating an initial solution by scheduling all types of train type by type and then repeatedly improving their periodic schedules until the objective value cannot be reduced or the iteration number reaches its maximum. …”
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Optimization of transportation efficiency through fuzzy logic and LINGO software by Zahoor Ahmad Ganie, Zahid Gulzar Khaki, Tanveer Ahmad Tarray, Gazala Salam, Eid Sadun Alotaibi

“…This study significantly contributes to the development of optimization algorithms and transportation systems with a solid basis for accurate, privacy-preserving data collection and efficient decision-making.…”
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FONDUE—Fine-Tuned Optimization: Nurturing Data Usability & Efficiency by Valerie Restat, Indra Diestelkämper, Meike Klettke, Uta Störl

“…As an adaptive and easily extendable framework, FONDUE operates similarly to proven methods of database query optimization. Analogously, it consists of the following parts: Rule-based optimization, where the appropriate data cleaning algorithms are selected based on use case constraints, optimizer hints in the form of best practices, and cost-based optimization, where the costs are measured in terms of data quality. …”
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Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder by Nvgui LIN, Lanxiu HONG, Daoshan HUANG, Yang YI, Zhixuan LIU, Qifeng XU

“…Because the effective data characteristics are destroyed by the abnormal behaviors, the abnormal behaviors can be detected through comparing the difference between the reconstruction error and the detection threshold. To improve the feature extraction ability and the robustness of AE network, the sparse restrictions and the noise coding are introduced into the auto-encoder, and the hyper-parameters of AE network are optimized through the particle swarm optimization algorithm to improve the learning efficiency and generalization ability. …”
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A Heuristic Optical Flow Scheduling Algorithm for Low-Delay Vehicular Visible Light Communication by Zhengying Cai, Shumeng Lei, Jingyi Li, Chen Yu, Junyu Liu, Guoqiang Gong

“…The cost of this algorithm is very low, and it is suitable for deployment on edge computing platforms such as vehicles.…”
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Modelling and Chaotic Based Parameter Optimization of Sliding Mode Controller by Adnan Derdiyok, Sezgin Kaçar, Onur Demirel, Muhammed Salih Sarıkaya

“…The random structures of chaotic systems allow optimization algorithms to explore a broader solution space, thereby improving their performance. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…The final phase involves classifying the data using the Shallow hybrid quantum-classical convolutional neural network (SHQCNN) model. To improve the effectiveness of the classification approach, the hyperparameters present in the SHQCNN model are fine-tuned using the shuffled shepherd optimization algorithm (SSOA). …”
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Critical Component Overheating Monitoring Algorithm Integrating Temperature-sensing Patches and Computer Vision Trains by SHU Dong, ZHANG Beijia, YANG Hongtai

“…In the test on a real dataset, the improved algorithm achieves an accuracy rate of 99.30%.…”
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An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier–Stokes Equations by Jinlin Tang, Qiang Ma

“…This error indicator guides an AMR algorithm that combines longest-edge bisection with local Delaunay re-triangulation, ensuring optimal mesh adaptation to complex flow features such as boundary layers and vortices. …”
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DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…The approach integrates a Decision Tree (DT) algorithm for rapid initial classification of Primary User (PU) activity, followed by a Hill Climbing (HC) optimization algorithm that fine-tunes the detection based on a fitness function. …”
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Heuristic Algorithms for the Heterogeneous Vehicle Routing Problem With Time Windows, Customers Priority, Pickup and Delivery by Moayad Tanash, Rami As'Ad

“…Both heuristics possess a two-phase structure, where the first phase yields highly prudent initial solutions employing a Greedy Randomized Adaptive Search Procedure (GRASP) in the first heuristic, and Priority-Based Ant Colony Optimization (PBACO) in the second heuristic. As for the second phase, both heuristics embrace a common Variable Neighborhood Search (VNS) algorithm that explores seven different neighborhoods to improve upon the initial solutions. …”
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