A Chaotic Decomposition-Based Approach for Enhanced Multi-Objective Optimization by Javad Alikhani Koupaei, Mohammad Javad Ebadi

“…The evaluation metrics, including inverted generational distance (IGD), generational distance (GD), and spacing (SP), demonstrated that the proposed method achieves competitive optimization accuracy and efficiency. While maintaining computational feasibility, our approach provides a well-balanced trade-off between exploration and exploitation, leading to improved solution diversity and convergence stability. …”
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Optimizing Multi-Echelon Delivery Routes for Perishable Goods with Time Constraints by Manqiong Sun, Yang Xu, Feng Xiao, Hao Ji, Bing Su, Fei Bu

“…The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. …”
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Multi-objective multi-workflow task offloading based on evolutionary optimization by Teliekebieke Misha, Lisheng Sun, Zheng-yi Chai

“…Simulation results demonstrate that our algorithm optimally balances delay and energy consumption requirements compared to existing methods, while enhancing the diversity and convergence of non-dominated solutions.…”
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Optimizing PAPR performance for 6G OTFS waveform using adaptive genetic PTS method for Rician fading channels by Arun Kumar, Aziz Nanthaamornphong, Nishant Gaur

“…The proposed AGA-PTS method enhances conventional genetic algorithms by adaptively tuning the crossover and mutation rates according to the convergence behavior, leading to improved optimization efficiency and reduced signal distortion. …”
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Multi-UAV Trajectory Optimization Under Dynamic Threats: An Enhanced GWO Algorithm Integrating a Priori and Real-Time Data by Zihan Zhou, Yanhong Guo, Yitao Wang, Jingfan Lyu, Haoran Gong, Xin Ye, Yachao Li

“…Our research integrates a priori knowledge of threat zone locations, speeds, and directions with real-time data on the UAVs position relative to the threat zones to effectively manage dynamic threat zones, allowing UAVs to dynamically decide whether to navigate around or through these zones, thus significantly reducing trajectory costs. To further improve search efficiency and solution quality, strategies such as greedy initialization and K-means clustering are incorporated, enhancing the algorithms multi-objective optimization capabilities. …”
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UAV Path Optimization for WSN in Smart Agriculture by Katia Karam, Ali Mansour, Mohamad Khaldi, Benoit Clement, Mohammad Ammad-Uddin

“…The algorithm ensures that the UAV hovers at an optimal position within each sensor’s coverage area, minimizing unnecessary movements. …”
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Multi-objective layout optimization of hospital outpatient clinics based on NSGA II by Yanlin Zhao, Jiamei Gu

“…Abstract This study utilizes an improved NSGA-II algorithm to conduct a multi-objective optimization of the hospital outpatient department layout. …”
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Rock image classification based on improved EfficientNet by Kai Bai, Zhaoshuo Zhang, Siyi Jin, Shengsheng Dai

“…Second, the shuffle attention mechanism is integrated into the backbone network to enhance feature extraction while reducing model parameters. Finally, the Lion optimizer is employed to optimize the training process, improving both the accuracy and stability of the model. …”
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Hybrid Population-Based Hill Climbing Algorithm for Generating Highly Nonlinear S-boxes by Oleksandr Kuznetsov, Nikolay Poluyanenko, Kateryna Kuznetsova, Emanuele Frontoni, Marco Arnesano

“…Our approach demonstrates remarkable efficiency, requiring only 49,277 evaluations on average to generate such S-boxes, representing a 600-fold improvement over traditional simulated annealing methods and a 15-fold improvement over recent genetic algorithm variants. …”
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Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments by Deafallah Alsadie, Musleh Alsulami

“…In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). …”
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Evolutionary Optimization for the Classification of Small Molecules Regulating the Circadian Rhythm Period: A Reliable Assessment by Antonio Arauzo-Azofra, Jose Molina-Baena, Maria Luque-Rodriguez

“…We applied a genetic algorithm to optimize feature selection and classification performance. …”
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Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks by Umar Draz, Tariq Ali, Sana Yasin, Muhammad Hasanain Chaudary, Muhammad Ayaz, El-Hadi M. Aggoune, Isha Yasin

“…This framework is crucial for improving data delivery, optimizing Route REQuest (RREQ) and Routing Overhead (RO), while minimizing Average End-to-End (AE2E) delays and localization errors. …”
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Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints by Jinane Radi, Jesús Enrique Sierra-García, Matilde Santos, Carlos Armenta-Déu, Abdelouahed Djebli

“…The drag and lift coefficients are estimated, and a metaheuristic optimization technique, genetic algorithm, is applied to maximize the glide ratio while reducing the difference from the desired design parameters. …”
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Sliding Mode Thrust Control Strategy for Electromagnetic Energy-Feeding Shock Absorbers Based on an Improved Gray Wolf Optimizer by Wenqiang Zhang, Jiayu Lu, Wenqing Ge, Xiaoxuan Xie, Cao Tan, Huichao Zhang

“…Meanwhile, a position update strategy from the particle swarm optimization (PSO) algorithm is integrated into the gray wolf optimizer (GWO) to improve the global search ability and the balance of local exploitation. …”
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Hybrid RSA-ROA Scheduling Algorithm for Minimization of Power Loss and Improving the Renewable with Sustainable Energy Harvesting in Power System by Cuddapah Anitha, Virendra Swaroop Sangtani, Ajay Kumar Bansal, Mahaveerakannan R., R. Rajesh Sharma, Saravanan M. S.

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IoT driven healthcare monitoring with evolutionary optimization and game theory by Shitharth Selvarajan, Hariprasath Manoharan, Taher Al-Shehari, Nasser A. Alsadhan, Subav Singh

“…By incorporating two evolutionary algorithms, the proposed approach optimizes the state of action for each participant while reducing energy consumption and processing delay. …”
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Sparse Adaptive Optimization Based on Low Rank Decomposition for Image Defect Detection by Daihong Jiang, Zhixiang Chen, Sanyou Zhang, Yunfei Li, Lu Zhao

“…The proposed model enhances the structural expressiveness of the sparse component while preserving the global low-rank structure. To solve the resulting optimization problem efficiently, we develop a tailored algorithm based on operator splitting techniques, significantly improving computational efficiency without sacrificing accuracy. …”
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Joint Optimization of Caching and Recommendation with Performance Guarantee for Effective Content Delivery in IoT by Zhiyong Liu, Hong Shen, Hui Tian

“…Experimental results demonstrate that our algorithm dramatically improves the popular least-recently used (LRU) algorithm. …”
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Research on Multi-objective Shielding Intelligent Optimization Method Based on Non-dominated Sorting Genetic Algorithm NSGA-Ⅲ by WANG Mengqi, ZHENG Zheng, MEI Qiliang, PENG Chao, GAO Jing, ZHOU Yan

“…The multi-objective optimization algorithm outperforms traditional radiation shielding design methods in quickly finding a solution that meets the shielding requirements while simultaneously weighing multiple objectives, such as the dose rate, volume and weight of the shielding material. …”

A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batt... by Mehrdad Shoeibi, Anita Ershadi Oskouei, Masoud Kaveh

“…To address these limitations, reconfigurable intelligent surfaces (RISs) offer a promising solution by dynamically manipulating the wireless propagation environment to enhance signal strength and improve energy harvesting capabilities. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm that optimizes the phase shifts of RISs to maximize the network’s achievable rate while satisfying IoT devices’ energy harvesting constraints. …”
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