APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Therefore, it is reasonable to model the problem of improving network scalable degree as minimizing network coupling degree,and it is feasible to improve network scalable degree by reducing network coupling degree.2)The upper limit of computational complexity of the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math></inline-formula>,while that of directly solving the optimization problem is<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math></inline-formula>.3)For theoretical analysis of the network scalable degree,take Fig.3 as an example.If AP2 changes,12 APs in Fig. 3(a)are affected and the network scalable degree is η₂=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η₂=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional strategy,the network scalable degree is improved by 9.59% with 4.43% user rate loss.Compared with the strategy in[10],the network scalable degree is improved by 22.15% with 4.99% user rate loss. 5) The algorithm parameters, the threshold β₀of overlap rate and the upper limit number N₀of AP associated, effect the performance.As shown in Fig.6,with β₀or N₀decreases,η increases and the total user rate decreases. …”
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Integrated Optimization of Pipe Routing and Clamp Layout for Aeroengine Using Improved MOALO by Qiang Liu, Zhi Tang, Huijuan Liu, Jiapeng Yu, Hui Ma, Yonghua Yang

“…To this end, the MOALO (multiobjective ant lion optimizer) algorithm is modified by introducing the levy flight strategy to improve the global search performance and convergence speed, and it is further used as a basic computation tool. …”
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An optimized multi-task contrastive learning framework for HIFU lesion detection and segmentation by Matineh Zavar, Hamid Reza Ghaffari, Hamid Tabatabaee

“…By employing a genetic algorithm, OMCLF explores and optimizes augmentation techniques suitable for medical data, avoiding distortions that could compromise diagnostic accuracy. …”
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Regularized K-Means Clustering via Fully Corrective Frank-Wolfe Optimization by Ahmed Yacoub Yousif, Basad Al-Sarray

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Detection and Optimization of Photovoltaic Arrays’ Tilt Angles Using Remote Sensing Data by Niko Lukač, Sebastijan Seme, Klemen Sredenšek, Gorazd Štumberger, Domen Mongus, Borut Žalik, Marko Bizjak

“…The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. …”
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Optimizing Concrete Mix Design for Cost and Carbon Reduction Using Machine Learning by Angga T. Yudhistira, Arief S. B. Nugroho, Iman Satyarno, Tantri N. Handayani, Malindu Sandanayake, Rimba Erlangga, Jonathan Lianto, Alfa Rosyid Ernanto

“…XGBoost Machine Learning Algorithm is used to make predictions, and PSO is used to obtain the optimal mixture. …”
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Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies by Diego Calderon, Mohammad Najafi

“…This article introduces a unified framework and methods for optimally selecting condition monitoring technologies while locating their deployment at the most vulnerable pipe segments. …”
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Multi-Objective Machine Learning Optimization of Cylindrical TPMS Lattices for Bone Implants by Mansoureh Rezapourian, Ali Cheloee Darabi, Mohammadreza Khoshbin, Irina Hussainova

“…To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). …”
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Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…This review synthesizes key advancements in biofuel optimization, highlighting the use of techniques such as Artificial Neural Networks (ANN), Genetic Algorithms (GA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Response Surface Methodology (RSM). …”
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Robust outdoor trajectory mapping using CNN features and loop closure optimization by Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Tarique Rafique Memon, Azam Rafique Memon

“…To counteract accumulated drift, a hybrid optimization strategy is proposed: Bag of Words (BoW)-based loop closure detection identifies revisit locations, while an algorithm is developed to optimize the map. …”
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The Structural Design and Optimization of a Railway Fastener Nut Disassembly and Assembly Machine by Xiangang Cao, Guoyin Chen, Mengzhen Zuo, Jiasong Zang, Peng Wang, Xudong Wu

“…The performance of the nut assembly and disassembly mechanism was optimized based on the Kriging model and the Non-dominated Sorting Genetic Algorithm (NSGA-II). …”
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Capacity Optimization Configuration of a Bidirectional Reversible Centralized Electrohydrogen Coupling System by Xing FENG, Wei YANG, Anan ZHANG, Xi ZHANG, Qian LI, Xianzhang LEI

“…The solution is solved by combining particle swarm optimization algorithm and CPLEX solver. Finally, through case analysis, it was verified that the addition of RSOC improved the system's economic and environmental benefits. …”
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EFFICACY OF OPTIMIZED REMINERALIZING THERAPY IN POST-COVID-19 PATIENTS: EVALUATION OF RESULTS by N.M. Savielieva, M.E. Diasamidze

“…To correct the identified disorders and prevent the occurrence and development of carious lesions, we applied an improved algorithm of prophylaxis of dental enamel diseases using remineralizing therapy. …”
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Development and optimization of an electrohydrodynamic dehydrator using ANN-GA for improved energy performance by Chakrit Suvanjumrat, Klar Kongsarai, Piyamon Phong-arom, Namnguen Chumphong, Machimontorn Promtong, Jetsadaporn Priyadumkol

“…Furthermore, an integrated ANN–genetic algorithm (ANN-GA) approach was established to optimize the drying parameters, aiming to maximize drying kinetics and minimize SEC. …”
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Predicting compressive strength of concrete at elevated temperatures and optimizing its mixture proportions by Jinjun Xu, Han Wang, Wenjun Wu, Lang Lin, Yong Yu

“…The Cuckoo search algorithm was then employed to optimize mix designs, balancing high-temperature strength, cost and sustainability. …”
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Optimizing Rural Connectivity Through Hybrid Wireless-Wireline and Satellite Network Integration by Fareha Nizam, Teong Chee Chuah, Ying Loong Lee

“…In the user association scheme, the user association problem is formulated as a combinatorial optimization problem and solved using a greedy algorithm with a fairness-throughput trade-off that efficiently allocates resources based on network conditions and service priorities. …”
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Thermal analysis and multi-objective optimization of equal-area microfluidic cooling systems by Yucheng Wang, Antong Bi, Yue Yao, Weiting Chen, Kaiyu Chen, Shenxin Yu, Jiangang Yu, Chao Wang, Wei Li, Shaoxi Wang

“…Moreover, An orthogonal experimental design combined with a multi-objective optimization algorithm was applied to minimize both the average heat source temperature and pressure drop. …”
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Cloud-edge hybrid deep learning framework for scalable IoT resource optimization by Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, Anjani Kumar Rai, S. M. Padmaja, Khan Vajid Nabilal, Vimal Kumar, Roobaea Alroobaea, Hamed Alsufyani

“…DQN facilitates the formulation of optimal resource allocation strategies in intricate and unpredictable environments. …”
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Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates by Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang, Xiaojun Yao, Chang-Yu Hsieh, Tingjun Hou

“…Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. …”
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Differential Evolution Optimized Non-Orthogonal Multiple Access for Sum Rate Maximization by Dipinkrishnan Rayaroth, Vinoth Babu Kumaravelu, Helen Sheeba John Kennedy, Kalapraveen Bagadi, Francisco R. Castillo Soria

“…To maximize the attainable sum rate of individual users and minimize outage, the power allocation (PA) factors must be optimized. In the proposed work, a differential evolution (DE) algorithm is implemented to optimize the power factors assigned to users. …”
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