Efficient Hybrid-Robust Approach for Cancer Biomarker Discovery Using Omics Data by Karima Sid, Soumia Zertal, Mohamed Batouche, Soumeya Zerabi

“…A novel hybrid framework was put forth for consideration, comprising three distinct stages: Clustering, Parallel Filtering, and Hybrid-Parallel Optimization. In each step, a combination of techniques and algorithms is used to improve the results in terms of stability and/or accuracy. …”
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Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling by He Wang, Weiquan Huang, Sindri Magnússon, Tony Lindgren, Chen Chen, Junyu Wu, Yanjie Song

“…It integrates Q-learning into an improved multi-objective grey wolf optimization framework, designing state-action combinations that balance exploration and exploitation. …”
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Advanced Queueing and Location-Allocation Strategies for Sustainable Food Supply Chain by Amirmohammad Paksaz, Hanieh Zareian Beinabadi, Babak Moradi, Mobina Mousapour Mamoudan, Amir Aghsami

“…<i>Methods:</i> The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. …”
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Shared energy storage planning based on the adjustable potential of data center based on visual IOT platform by Lei Su, Wanli Feng, Haoyu Ma, Mingjiang Wei, RuoShi Gu, Ziya Chen, Junda Qin

“…Within this framework, a room-level energy management model is designed, integrating adjustable potential for batch-computing workloads and air conditioning systems to optimize time-of-use power consumption. Based the two-stage stochastic optimization model, a improved L-shaped algorithm is proposed to solve the planning model effectively, reducing computational complexity through problem decomposition. …”
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Symbiotic Learning Grey Wolf Optimizer for Engineering and Power Flow Optimization Problems by Aala Kalananda Vamsi Krishna Reddy, Komanapalli Venkata Lakshmi Narayana

“…The proposed method improves the performance of the algorithm to effectively handle problems with larger dimensions while avoiding local entrapment, accelerates convergence, and improves the precision and accuracy of exploitation. …”
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Link Prediction in Social Networks Using the HTOA by Foad Asef, Vahid Majidnezhad, Mohammad-Reza Feizi-Derakhshi

“…This study introduces a novel approach to link prediction in social networks by employing the Heat Transfer Optimization Algorithm (HTOA) for topological feature selection. …”
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Stochastic Explicit Calibration Algorithm for Survival Models by Jeongho Park, Sangwook Kang, Gwangsu Kim

“…In addition, we highlight how improved calibration can improve downstream tasks, including optimizing resource allocation and improving patient care decisions. …”
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Cooperative routing algorithm based on game theory by Kun XIE, Shen-lin DUAN, Ji-gang WEN, Shi-ming HE

“…In order to improve the routing performance of selfish wireless network, a VMIMO coop-erative routing algorithm based on game theory was proposed. …”
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Cooperative routing algorithm based on game theory by Kun XIE, Shen-lin DUAN, Ji-gang WEN, Shi-ming HE

“…In order to improve the routing performance of selfish wireless network, a VMIMO coop-erative routing algorithm based on game theory was proposed. …”
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Subsampling Algorithms for Irregularly Spaced Autoregressive Models by Jiaqi Liu, Ziyang Wang, HaiYing Wang, Nalini Ravishanker

“…The effectiveness of these methods depends on their ability to identify and select data points that improve the estimation efficiency according to some optimality criteria. …”
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Multi-Objective optimization of a cascaded supercritical CO2 brayton cycle with ejector-enhanced transcritical CO2 and flash tank absorption refrigeration cycles by Tausif Elahi Khan, Sakibul Haque Sakib, Nazmus Sakib, Tanvir Hossain, M.Monjurul Ehsan, Yasin Khan

“…The FCAR subsystem contributes additional cooling capacity, while the ETCR subsystem enhances the chiller load by minimizing waste heat from the cooler. …”
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A Quantum-Inspired Optimization Strategy for Optimal Dispatch to Increase Heat and Power Efficiency by K. Vanitha, B. Jyothi, R. Seshu Kumar, V. S. Chandrika, Arvind R. Singh, R. M. Naidoo

“…Future research can further explore the application of quantum-inspired optimization algorithms in other energy systems and optimize the algorithm’s parameters to improve its performance.…”
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ADVCSO: Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization for Combinatorial Optimization Problems by Kunwei Wu, Liangshun Wang, Mingming Liu

“…This study proposes an Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization (ADVCSO) algorithm. First, to address the uneven initial solution distribution in the original algorithm, we design an elite perturbation initialization strategy based on good point sets, combining low-discrepancy sequences with Gaussian perturbations to significantly improve the search space coverage. …”
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Optimal Assembly Position and Multi-objective Trajectory Optimization for Dual Robotic Arms Collaboration by Wang Tianrui, Tao Ping

“…Finally, based on the optimal position of flexibility, the multi-objective particle swarm algorithm is used to optimize the trajectory with the objectives of time, energy consumption and impact; by performing simulation analysis in the Matlab robot simulation platform, the results show that, by applying the flexibility index to the trajectory planning, it can improve the operation efficiency of the robotic arm, while reducing energy consumption and impact loss, and maintain a high degree of operability during the whole motion process, effectively avoiding the randomness of the traditional manual teaching method, thus realizing the optimal flexible trajectory planning for collaborative assembly of dual robotic arms.…”
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Human Activity Recognition Using Graph Structures and Deep Neural Networks by Abed Al Raoof K. Bsoul

“…To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. …”
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Application of Three Neural Network Models in the Prediction ofStratospheric Wind Field by YUAN Junjie, LUO Rubin, LIAO Jun, YANG Zechuan, WANG Ning, LI Jun

“…The simulation results show that the neural network model can be applied to the prediction of stratospheric wind field and the BP neural networks optimized by genetic algorithm and particle swarm optimization can greatly improve the prediction accuracy of BP neural network, and the forecast wind speed is following the true value in a certain period of time. …”
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A solution to the Single-School school bus routing problem considering accessibility and economy by Zizhan Jiang, Jiying Wang, Siyuan Huang, Hua Daniel Xu

“…In the third stage, Dijkstra’s algorithm is combined with GIS to optimize the bus routes, with a focus on accessibility and economy.Experimental results demonstrate that the THGO method can efficiently find the global optimal solution while satisfying constraints such as time windows, bus capacity, and road directionality. …”
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Flexible Demand Side Management in Smart Cities: Integrating Diverse User Profiles and Multiple Objectives by Nuno Souza e Silva, Paulo Ferrão

“…The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. …”
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Advancing Pile-Bearing Capacity Prediction with Meta-Heuristic Enhanced Specific ANFIS strategys by Zhongmiao Dang

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A Robust Multi-Objective Evolutionary Framework for Artificial Island Construction Scheduling Under Dynamic Constraints by Tianju Zheng, Liping Sun, Mingwei Li, Guangyao Yuan, Shuqi Li

“…To effectively address the complexities of this model, we develop and employ the Multi-Objective Adaptive Cooperative Evolutionary Marine Genetic Algorithm (MACEMGA). MACEMGA combines cooperative coevolution, adaptive dynamic weighting, dynamic penalty functions, and advanced genetic operators to navigate the solution space efficiently and identify Pareto optimal schedules. …”
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