UV-Vis spectroscopy coupled with firefly algorithm-enhanced artificial neural networks for the determination of propranolol, rosuvastatin, and valsartan in ternary mixtures by Ahmed Serag, Maram H. Abduljabbar, Yusuf S. Althobaiti, Farooq M. Almutairi, Shaker T. Alsharif, Rami M. Alzhrani, Marwa F. Ahmed, Atiah H. Almalki

“…An experimental design of 25 samples was employed as a calibration set, and a central composite design of 20 samples was used as a validation set. The firefly algorithm (FA) was evaluated as a variable selection procedure to optimize the developed ANN models resulting in simpler models with improved predictive performance as evident by lower relative root mean square error of prediction (RRMSEP) values compared to the full spectrum ANN models. …”
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Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) by Desi W. Sari, Suci Dwijayanti, Bhakti Y. Suprapto

“…To address these limitations, this research incorporates a linear regression line as a directional guide for ants, helping them navigate toward the optimal path more efficiently. This paper presents an improved Ant Colony Optimization (I-ACO) algorithm by integrating regression-based guidance to enhance both exploration and convergence. …”
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Toward Robust GNSS Real-Time Orbit Determination for Microsatellites Using Factor Graph Optimization by Cong Hou, Xiaojun Jin, Xiaopeng Yang, Tong Xiao

“…However, the performance of EKF-RTOD is markedly degraded when the microsatellite deviates from a stable Earth-pointing attitude and employs a low-cost receiver. Factor graph optimization (FGO), which addresses nonlinear problems through multiple iterations and re-linearization, has demonstrated superior accuracy and robustness compared to EKF in challenging environments such as urban canyons. …”
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Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems by Ebrahim Akbari, Amin Khodabakhshian, Abolfazl Rahimnejad, Stephen Andrew Gadsden

“…The proposed method ensures a balanced and effective trade-off between solution accuracy and diversity in multi-objective optimization. Comparative evaluations against well-established algorithms demonstrate the superior performance of the proposed approach in approximating the Pareto front, improving computational efficiency, and maintaining solution diversity. …”
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Rolling optimization method of virtual power plant demand response based on Bayesian Stackelberg game by Binxi Huang

“…This is iteratively solved using the whale algorithm to determine the optimal power generation distribution scheme for each unit on both the supply side and demand sides. …”
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Flexibility Resource Planning and Stability Optimization Methods for Power Systems with High Penetration of Renewable Energy by Haiteng Han, Xiangchen Jiang, Yang Cao, Xuanyao Luo, Sheng Liu, Bei Yang

“…Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. …”
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AI-driven generative and reinforcement learning for mechanical optimization of 2D patterned hollow structures by Yicheng Shan, Leitao Cao, Yu Wang, Jing Ren, Chen Huang, Wenli Gao, Shengjie Ling

“…This study demonstrates the efficacy of combining advanced AI techniques for rapid and precise material design optimization, providing a scalable and cost-effective solution for developing superior lightweight materials with tailored mechanical properties for critical engineering applications.…”
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Multi-Energy Microgrid Data-Driven Distributionally Robust Optimization Dispatch Considering Uncertainty Correlation by LI Jiawei, SUN Qinghe, WANG Qiong, YE Yujian, HU Heng, ZHANG Xi

“…[Results] Case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set，resulting in an 8.16% reduction in out-of-sample costs. The proposed sample-pruning algorithm further reduces the out-of-sample costs by 3.33%. …”
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Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes by Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao

“…Existing AutoCIL methods focus solely on single-objective optimization. However, real-world applications often involve multiple, conflicting objectives—such as predictive performance and computational cost—that must be jointly optimized. …”
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Enhanced Underwater Image Restoration and Clarity Optimization Using Advanced Digital Image Processing Techniques by Gopirajan Punniyakotti Varadharajan, Pradeep T., Charan R.

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Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids by Sungmin Lim, Jaekyu Lee, Sangyub Lee

“…Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. …”
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Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions by Carlos Alejandro Perez Garcia, Dafni Despoina Avgoustaki, Enrica Santolini, Daniele Torreggiani, Thomas Bartzanas, Patrizia Tassinari, Marco Bovo

“…The model, demonstrating satisfactory performance in predicting VPD, enables optimization of indoor growth conditions, thereby improving resources use efficiency and minimizing operational costs. …”
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Optimal Configuration of Additional Heat Source for CHP System considering Demand Response Based on Comprehensive Benefits by Yuting Zhao

“…Furthermore, an improved memetic algorithm (IMA) combined with a hierarchical sequence method is designed to solve the optimization model characterized by multiple objectives, hierarchical levels, and nonlinearity. …”
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Recursive Objective Space Exploration (ROSE): A computationally efficient deterministic approach for bi-objective optimization. by Ihab Hashem, Viviane De Buck, Seppe Seghers, Jan Van Impe

“…In this paper, we propose an alternative strategy that tackles bi-objective optimization problems by exploring the objective space recursively at a reduced computational cost. …”
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A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem by You Li, Yingxin Kou, Zhanwu Li, An Xu, Yizhe Chang

“…Simulation results show that the proposed algorithm is successfully applied in the field of WTA which improves the performance of the traditional P-ACO algorithm effectively and produces better solutions than the two well-known multiobjective optimization algorithms NSGA-II and SPEA-II.…”
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An investigation into multi-objective decision-making in fresh cold chain supply chain networks within a dual distribution framework by Junyan Sun, Xing Li, Zefei Chen

“…The algorithm results show that, compared to the traditional NSGA-II and the SA-NSGA-II algorithms with random initialization, the LHS-SA-NSGA-II algorithm demonstrates clear advantages in terms of total cost, carbon emissions, and distribution time, confirming its superiority in optimizing cold chain networks. …”
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Enhancing Power Efficiency in 4IR Solar Plants through AI-Powered Energy Optimization by S. Boobalan, TR. Kalai Lakshmi, Shubhangi N. Ghate, Mohammed Hameeduddin Haqqani, Sushma Jaiswal

“…Furthermore, the system can be customized according to your industry’s changing needs and requirements, providing the ability to reduce costs in energy usage while improving the efficiency and productivity of machines.…”
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. …”
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Artificial Intelligence and Nature-Inspired Techniques on Optimal Biodiesel Production: A Review—Recent Trends by Christos Kyriklidis, Aikaterini Koutouvou, Konstantinos Moustakas, Vayos Karayannis, Constantinos Tsanaktsidis

“…The optimal fuels are produced in laboratories and tested in common engines too. …”
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Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction by Qian Zeng, Xiaobo Li, Yixuan Chen, Minghao Yang, Xingbang Liu, Yuetian Liu, Shiwei Xiu

“…By leveraging attention parameters, ELPM acquires spatio-temporal adaptive weights, enabling accurate location predictions. We also design an improved service allocation strategy, MESDA, based on the Gray Wolf Optimization (GWO) algorithm. …”
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