Multi-Objective Path Optimization Method for Maritime UAVs Equipped with Inertial Navigation Systems by Zhao Li, Weihao Ma, Haixiang Pang

“…Subsequently, a forward labeling-based multi-objective path planning algorithm is proposed to develop an optimization model. …”
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Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme by Mohammed Ashraf Ali, Ahmad H. Besheer, Hassan M. Emara, Ahmed Bahgat

“…Abstract The challenge of optimizing battery operating revenue while mitigating aging costs remains inadequately addressed in current literature. …”
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Optimizing frequency stability with adaptive fast frequency reserves and Wide-Area Monitoring Systems by Bwandakassy Elenga Baningobera, Irina Oleinikova, Kjetil Uhlen

“…The adaptive algorithm’s ability to adjust to varying system conditions reduces the risk of over-shedding or under-shedding loads, thereby optimizing the system’s performance. …”
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Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks by Israel Edem Agbehadji, Samuel Ofori Frimpong, Richard C Millham, Simon James Fong, Jason J Jung

“…The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. …”
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МЕСHANICAL BEHA VIOR OF GRADED HONEYCOMB AND ITS MULTI-OBJECTIVE OPTIMAL DESIGN by CHEN BiMin, HUANG XiaoDi

“…Taking the crashworthiness index of the honeycomb as the optimization goal, in the design space of the gradient honeycomb, the multi-objective optimization algorithm is directly used to determine the specific parameters of each layer of the gradient honeyeomb. …”
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Optimal Model-Free Mean-Square Consensus for Multi-Agents with Markov Switching Topology by Ruoxun Ma, Lipo Mo, Bokang Zhou

“…The goal is to design an appropriate algorithm to find the optimal control policies that minimize the performance index while achieving consensus among the agents. …”
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Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid by Peter Anuoluwapo Gbadega, Yanxia Sun, Olufunke Abolaji Balogun

“…To solve the multi-objective power management optimization problem, the Teaching-Learning-Based Optimization (TLBO) algorithm was implemented. …”
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An Enhanced Distribution System Performance with Optimization Techniques for Location of Electrical Vehicle Charging Stations by Sainadh Singh Kshatri, Venkata Anjani Kumar G, Chilakapati Lenin Babu, Palepu Suresh Babu

“…The proposed methodology leverages the Grey Wolf Optimization (GWO) metaheuristic algorithm, enthused by the grey wolves hunting, to identify the most strategic locations for EVCSs. …”
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A Bi-Level Optimization Model for Flexible Capacity Allocation with Coordinated Planning and Operation by Xinyu Yin, Xiaoyan Bian, Qinran Zhu, Jinbin Zhao, Shunfu Lin, Kwok L. Lo

“…The upper planning level introduces the economic costs, flexibility resource capacity, and flexibility index which are used as the evaluation index of system flexibility, while in the lower operation level, a morphological clustering algorithm based on the multiscale and entropy weight method is proposed for obtaining typical scenarios of flexibility demand. …”
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Eight-Figure Pattern for Enhancing the Searching Process of Grey Wolf Optimization (Eight-GWO) by Lec. Ali Hakem Alsaeedi, Suha Muhammed Hadi, Yarub Alazzawi, Emad Badry Badry

“…The proposed model is based on simulating eight pattern movements and embedded with a Grey Wolf Optimizer algorithm (GWO). It is called the Eight-Figure Grey Wolf Optimizer (Eight-GWO). …”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…In addition, samples are needed to solve such system optimization problems. Therefore, to improve the optimization accuracy of nonlinear constrained single objective systems that are without accurate mathematical models while considering the cost of obtaining samples, a new method based on a combination of support vector machine and immune particle swarm optimization algorithm (SVM-IPSO) is proposed. …”
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Optimizing Strategies in Patients Affected by Tumors Infiltrating the Skull: A Single Center Experience by Giuseppe Emmanuele Umana, Sruthi Ranganathan, Salvatore Marrone, Jessica Naimo, Matteo Giunta, Angelo Spitaleri, Marco Fricia, Gianluca Ferini, Gianluca Scalia

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Stiffness Regulation of Cable-Driven Redundant Manipulators Through Combined Optimization of Configuration and Cable Tension by Zhuo Liang, Pengkun Quan, Shichun Di

“…An improved differential evolution algorithm is employed to generate adjustable configuration solutions based on motion-level factors, while a modified gradient projection method is adopted to derive adjustable cable tension solutions based on tension-level factors. …”
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Green logistics optimization for coal supply in a power plant connecting maritime port by Mingsheng Qiu, Liping Gao, Zhouyong Lin, Minghong Zheng, Qingzhong Lin

“…This paper introduces an integrated optimization model grounded in green logistics and employs an Improved Particle Swarm Optimization (IPSO) algorithm to efficiently manage the coal supply chain from unloading at ports to loading into generators. …”
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Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective by Hang Hu, Hang Zhang, Hong Yu

“…It is proved that the maximum achievable throughput and the minimum transmission delay cannot be obtained simultaneously. An efficient algorithm is proposed to optimize the sensing bandwidth and the final decision threshold jointly such that the throughput is maximized while the delay is constrained. …”
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Coordinated Optimal Dispatch of Distribution Grids and P2P Energy Trading Markets by Jing Deng, Fawu He, Qingbin Zeng, Jie Yan, Rangxiong Liu, Dongsheng He, Song Zhou

“…To address the nonlinear, high‐dimensional optimization challenges, an improved Convex‐Soft Actor‐Critic (C‐SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy‐preserving distributed coordination. …”
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Metamodel-Based Optimization Method for Traffic Network Signal Design under Stochastic Demand by Wei Huang, Xuanyu Zhang, Haofan Cheng, Jiemin Xie

“…A gradient-based metamodel algorithm is then proposed. In the numerical example, a six-node test network is used to examine the proposed metamodel-based optimization method. …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…This paper presents a reinforcement learning-based approach to automate the tuning of a 6thorder combline bandpass filter, operating at 941 MHz, using a Q-learning algorithm. To reduce complexity, only two tuning screws are considered in the optimization. …”
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Bayesian Optimized GridDehazeNet for Adaptive Haze Removal in Real-World Applications by Sungkwan Youm

“…By employing our algorithm, we effectively identify the best architectural parameters, achieving higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to models without this optimization. …”
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Multi-objective optimization placement strategy for SDN security controller considering Byzantine attributes by Tao WANG, Hongchang CHEN

“…By giving the software defined network distributed control plane Byzantine attributes, its security can be effectively improved.In the process of realizing Byzantine attributes, the number and location of controllers, and the connection relationship between switches and controllers can directly affect the key network performance.Therefore, a controller multi-objective optimization placement strategy for SDN security controllers considering Byzantine attributes was proposed.Firstly, a Byzantine controller placement problem (MOSBCPP) model that comprehensively considered interaction delay, synchronization delay, load difference and the number of controllers was constructed.Then, a solution algorithm based on NASG-II was designed for this model, which included the initialization function, the mutation function, the fast non-dominated sorting function and the elite strategy selection function.Simulation results show that this strategy can effectively reduce interaction delay, synchronization delay, load difference and the number of controllers, while improving control plane security.…”
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