Balancing Human Mobility and Health Care Coverage in Sentinel Surveillance of Brazilian Indigenous Areas: Mathematical Optimization Approach by Juliane Fonseca Oliveira, Adriano O Vasconcelos, Andrêza L Alencar, Maria Célia S L Cunha, Izabel Marcilio, Manoel Barral-Netto, Pablo Ivan P Ramos

“…To optimize sentinel site selection, we implemented a linear optimization algorithm that maximizes (1) Indigenous region representation and (2) human mobility coverage. …”
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Adaptive Trajectory Optimization for UAV-IRS Systems in 6G Thz Networks Using Multi Agent-DRL by Elmadina Nahla Nur, Saeed Rashid A., Saeid Elsadig, Awouda Ala Eldin, Mujlid Hana M., Elshafie Hashim

“…Given the non-convex and NP-hard nature of the problem, traditional optimization methods are insufficient. To tackle this, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which provides an efficient, low-complexity solution for trajectory optimization. …”
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Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning by Nikolaos A. Fountas, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis

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Leveraging ensemble learning-based stock preselection with multiobjective investment optimization for stepwise decision-supported portfolio management by Jui-Sheng Chou, Tran-Bao-Quyen Pham

“…This stage leverages a newly developed algorithm tailored to address the expanded mean–variance problem, effectively decomposing and optimizing it while balancing multiple objectives. …”
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System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques by S. Sakthiya Ram, C. Kumar, R. Saravanakumar, David Banjerdpongchai

“…After validating the resulting dynamic model, various PID controllers are designed using standard metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). …”
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An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence by Stephen Oladipo, Yanxia Sun, Oluwatobi Adeleke

“…The model input variables are wind speed, temperature, and humidity, with the output being the equivalent energy consumption for the student housing. While the particle swarm optimization (PSO) technique is versatile and widely used, it falls short by exhibiting premature convergence. …”
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Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy by Abdelhadi Amahrouch, Youssef Saadi, Said El Kafhali

“…To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. …”
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Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System by Nu Wen, Ying Zhou, Yang Wang, Ye Zheng, Yong Fan, Yang Liu, Yankun Wang, Minmin Li

“…To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. …”
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Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation by Oladimeji Ibrahim, Mohd Junaidi Abdul Aziz, Razman Ayop, Ahmed Tijjani Dahiru, Wen Yao Low, Mohd Herwan Sulaiman, Temitope Ibrahim Amosa

“…The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. …”
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Real-Time Energy Management in Microgrids: Integrating T-Cell Optimization, Droop Control, and HIL Validation with OPAL-RT by Achraf Boukaibat, Nissrine Krami, Youssef Rochdi, Yassir El Bakkali, Mohamed Laamim, Abdelilah Rochd

“…The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). …”
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A Novel Vibration Suppression Method for Welding Robots Based on Welding Pool Instability Evaluation and Trajectory Optimization by Mingtian Ma, Hong Lu, Yongquan Zhang, Zidong Wu, He Huang, Xujie Yuan, Xu Feng, Zhi Liu, Zhangjie Li

“…The proposed vibration suppression algorithm is based on the optimization of the welding trajectory. …”
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A multi-user multi-operator computing pricing method for Internet of things based on bi-level optimization by Wei Jiang, Huiqiang Wang, Bingyang Li, Haibin Lv, Qingchuan Meng

“…The problem is divided into three phases and modeled as a two-level optimization problem. While an operator’s goal is to minimize the loss of his interests, the user’s goal is to minimize the computation cost (energy consumption and price). …”
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Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition by Haodong Chi, Huiyuan Chen

“…Furthermore, considering the instability of classification caused by the empirical setting of hidden layer nodes in the extreme learning machine (ELM), a multi-strategy improved sparrow search algorithm is proposed to optimize ELM parameters. This algorithm integrates an adaptive Levy flight mechanism and dynamic reverse learning. …”
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AlphaRouter: Bridging the Gap Between Reinforcement Learning and Optimization for Vehicle Routing with Monte Carlo Tree Searches by Won-Jun Kim, Junho Jeong, Taeyeong Kim, Kichun Lee

“…However, an inherent gap exists between computationally driven DRL and optimization-based heuristics. While a DRL algorithm for a certain problem is able to solve several similar problem instances, traditional optimization algorithms focus on optimizing solutions to one specific problem instance. …”
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A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment by Yilun Liu, Xiaoming Li

“…The simulation results show that the proposed MGWO has better optimization performance. When combined with a situation assessment mechanism, it realizes dynamic obstacle avoidance while keeping the path length as short as possible.…”
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Process parameter optimization of laser beam machining for AISI -P20 mold steel using ANFIS method by Abdullah Eaysin, Sarower Kabir, Ebru Gunister, Nur Jahan, Amir Hamza, Muhammad Ali Zinnah, Adib Bin Rashid

“…The ANFIS model, developed and analyzed using MATLAB, successfully predicted response parameters and was experimentally validated, showing improved predictions over actual measurements. The Brute Force algorithm identified the minimum combination for an optimal parameter set. …”
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Advanced Hybrid Brain Tumor Segmentation in MRI: Elephant Herding Optimization Combined with Entropy-Guided Fuzzy Clustering by Baiju Karun, Arunprasath Thiyagarajan, Pallikonda Rajasekaran Murugan, Natarajan Jeyaprakash, Kottaimalai Ramaraj, Rakhee Makreri

“…EHO is utilized to optimize the clustering process, enhancing the algorithm’s ability to delineate tumor boundaries, while entropy-based fuzzy clustering accounts for intensity inhomogeneity and diverse tumor characteristics, promoting more consistent and reliable segmentation results. …”
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A Path-Driven Fluid Routing and Scheduling Method for Continuous-Flow Microfluidic Biochips with Delay Time Optimization by Zhisheng Chen, Bowen Liu, Hongjin Su, Zhen Chen, Genggeng Liu, Xing Huang

“…This paper proposes an integrated path-driven methodology that jointly optimizes routing and application mapping. For routing, we develop a hybrid particle swarm optimization algorithm that incorporates conflict awareness and channel utilization strategies. …”
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Low-carbon and economic optimization of a source-load-storage system based on Stackelberg game and chance constraints by Xu Ke, Liu Chang, Xu Weiting, Zeng Jian, Shao Fan

“…Finally, the model is solved using an improved particle swarm algorithm combined with the Gurobi optimization tool. …”
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Network-, Cost-, and Renewable-Aware Ant Colony Optimization for Energy-Efficient Virtual Machine Placement in Cloud Datacenters by Ali Mohammad Baydoun, Ahmed Sherif Zekri

“…This paper proposes NCRA-DP-ACO (Network-, Cost-, and Renewable-Aware Ant Colony Optimization with Dynamic Power Usage Effectiveness (PUE)), a bio-inspired metaheuristic that optimizes VM placement across geographically distributed datacenters. …”
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