Three-Dimensional UAV Path Planning Based on Multi-Strategy Integrated Artificial Protozoa Optimizer by Qingbin Sun, Xitai Na, Zhihui Feng, Shiji Hai, Jinshuo Shi

“…First, the tent map and refractive opposition-based learning (ROBL) are employed to enhance the diversity and quality of the initial population. Second, in the algorithm’s autotrophic foraging stage, we design a dynamic optimal leadership mechanism, which accelerates the convergence speed while ensuring robust exploration capability. …”
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Machine Learning G-Code Optimization by Héctor Lasluisa-Naranjo, David Rivas-Lalaleo, Joaquín Vaquero-López, Christian Cruz-Moposita

“…This research applies the K-means machine learning clustering algorithm to optimize G-code parameters such as extruder and heated bed temperature, deposition speed, and flow control. …”
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Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid by Sri Harish Nandigam, Krishna Mohan Reddy Pothireddy, K. Nageswara Rao, Surender Reddy Salkuti

“…The ASFO algorithm offers robust global search capabilities to explore complex search spaces, while GDO safeguards precise local convergence to optimize the dispatch schedule and energy cost and maximize renewable energy utilization. …”
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Multi-Factor Optimization and Failure-Tolerant Design of Cable-Driven Parallel Manipulators in Deep-Sea Robotics by Asim Ghaffar, Muhammad Zia Ur Rahman, Victor Leiva, Cecilia Castro, Carlos Martin-Barreiro

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Advancing Optimization Strategies in the Food Industry: From Traditional Approaches to Multi-Objective and Technology-Integrated Solutions by Esteban Arteaga-Cabrera, César Ramírez-Márquez, Eduardo Sánchez-Ramírez, Juan Gabriel Segovia-Hernández, Oswaldo Osorio-Mora, Julián Andrés Gómez-Salazar

“…Advanced methods, including evolutionary algorithms and comprehensive modeling frameworks, enable the simultaneous optimization of multiple variables, offering robust solutions to complex challenges. …”
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Topology Knapsack Problem for Geometry Optimization by Hsiao-Hui Li, Po-Chun Chang, Yuan-Hsun Liao

“…While several variants have been developed, including the 0/1, fractional, and multi-dimensional knapsack problems, no existing model simultaneously optimizes multiple attributes like rarity, importance, and preference alongside the weight and volume of items. …”
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A bi-level optimization model for PET bottle recycling within a circular economy supply chain by Carlos Corpus, Ali Zahedi, Eva Selene Hernández-Gress, José-Fernando Camacho-Vallejo

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Integration of Distributed Energy Resources in Unbalanced Networks Using a Generalized Normal Distribution Optimizer by Laura Sofía Avellaneda-Gómez, Brandon Cortés-Caicedo, Oscar Danilo Montoya, Jesús M. López-Lezama

“…A leader–followeroptimization framework is adopted, where the leader stage utilizes the Generalized Normal Distribution Optimization (GNDO) algorithm to generate candidate solutions, while the follower stage conducts power flow calculations through successive approximation to assess the objective value. …”
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Enhanced multi-objective Evolutionary Mating Algorithm with improved crowding distance and Levy flight for optimizing comfort index and energy consumption in smart buildings by Muhammad Naim Bin Nordin, Mohd Herwan Sulaiman, Nor Farizan Zakaria, Zuriani Mustaffa

“…While current Evolutionary Mating Algorithms (EMA) primarily focus on single-objective optimization and rely on weighted functions for handling multiple objectives, such approaches prove impractical for the complex trade-offs between comfort index and energy efficiency. …”
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An efficient binary spider wasp optimizer for multi-dimensional knapsack instances: experimental validation and analysis by Mohamed Abdel-Basset, Reda Mohamed, Karam M. Sallam, Ibrahim Alrashdi, Ibrahim A. Hameed

“…The usefulness and efficiency of the proposed algorithms are also demonstrated by comparing both of them to several metaheuristic optimizers in terms of some performance criteria. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

“… Proposed here is a speech-based diagnostic framework for detecting Parkinson's disease that utilizes a Long Short-Term Memory neural network and the Grasshopper Optimization Algorithm. The framework aims to improve the detection of PD while ensuring accurate and efficient classification of speech-based signals. …”
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Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm by Aseel Bennagi, Obaida AlHousrya, Daniel T. Cotfas, Petru A. Cotfas

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Constrained Bayesian Optimization: A Review by Sasan Amini, Inneke Vannieuwenhuyse, Alejandro Morales-Hernandez

“…Though it has been widely used to solve various optimization tasks, most of the literature has focused on unconstrained settings, while many real-world problems are characterized by constraints. …”
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GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION by Ayush Deshpande, Rohan Gore, Sankirti Shiravale, Shubham Chougule, Samarth Bahirgonde

“…Portfolio optimization is a critical aspect of investment strategy, which aims to maximize returns while minimizing risk. …”
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Smart village construction and college students’ skills entrepreneurship path based on LSTM and simulated annealing algorithm by Xueli Dong

“…In addition, the LSTM is suitable for processing time series data and can effectively analyze the development and changes of smart rural construction, while the simulated annealing algorithm can efficiently find a near-optimal solution to optimize the entrepreneurial path. …”
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Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease by Tiantian Bai, Mengru Xu, Taotao Zhang, Xianjie Jia, Fuzhi Wang, Xiuling Jiang, Xing Wei

“…Subsequently, the selected feature subsets were integrated into ten classification models, and a comprehensive weighted evaluation was performed based on the accuracy, precision, recall, F1 score, and AUC value of the model to determine the optimal model configuration. The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25; while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. …”
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A novel conceptual design approach for autonomous underwater helicopter based on multidisciplinary collaborative optimization by Yuan-Jie Chen, Haocai Huang

“…Additionally, a novel approximation model is introduced, combining the Logistic-Tent chaos sparrow search algorithm (LTC-SSA) with the backpropagation neural network (BPNN) algorithm. …”
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A Ultra-Low Cost and Accurate AMC Algorithm and Its Hardware Implementation by Yuqin Zhao, Tiantai Deng, Bill Gavin, Edward A. Ball, Luke Seed

“…In this design, the CAMC algorithm is optimized to fit the FPGA characteristics to further improve the performance, and the computing demands of which could be saved over 94% compared with other state-of-the-art designs. …”
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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 by Yang Li, Guoen Zhou, Jiaqi Xue, Junwei Yang, Shi Yin

“…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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Comparative performance evaluation of economic load dispatch using metaheuristic techniques: A practical case study for Bangladesh by Sumaiya Janefar, Prangon Chowdhury, Rahbaar Yeassin, Mahmudul Hasan, Nahid-Ur-Rahman Chowdhury

“…The Economic Load Dispatch (ELD) problem is a critical optimization challenge in power systems, aiming to minimize operational costs while meeting power demand. …”
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