Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning by Federico Rossi, Giancarlo Storti Gajani, Samuele Grillo, Giambattista Gruosso

“…This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. …”
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Optimal Temperature Calculation for Multicriteria Optimization of the Hydrogenation of Polycyclic Aromatic Hydrocarbons by NSGA-II Method by A. A. Alexandrova, S. N. Koledin

“…Introduction. Multicriteria optimization, taking into account contradicting criteria, is used to improve production efficiency, reduce costs, improve product quality and environmental safety of processes. …”
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Multi-Level Particle System Modeling Algorithm with WRF by Julong Chen, Bin Wang, Rundong Gan, Xuepeng Mou, Shiping Yang, Ling Tan

“…To improve modeling efficiency, cascade Bezier curves are designed at different line-of-sights (LoSs), utilizing the weight information of boundary particles to optimize cloud contours. …”
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An intelligent hybrid grey wolf-particle swarm optimizer for optimization in complex engineering design problem by Muhammad Suhail Shaikh, Haoyue Lin, Senlin Xie, Xiaoqing Dong, Yifan Lin, Chandan Kumar Shiva, Wulfran Fendzi Mbasso

“…The improvement in the average best optimal value varies across different cases, demonstrating the effectiveness of the proposed method. …”
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Network- and Demand-Driven Initialization Strategy for Enhanced Heuristic in Uncapacitated Facility Location Problem by Jayson Lin, Shuo Yang, Kai Huang, Kun Wang, Sunghoon Jang

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Forecast-based resource reconfiguration algorithm for network virtualization by ZHANG Shun-li, QIU Xue-song, PAN Ya-lian, MENG Luo-ming

“…In order to solve imbalance issues of used resources of substrate network,which are brought by virtual net-work joining and leaving dynamically,forecast-based resource reconfiguration algorithm（FRRA） for network virtualiza-tion environment was proposed.Algorithm FRRA took full advantage of known information to calculate interval of re-configuration and used this interval to replace the periodic time span in previous algorithms.In order to make reconfigu-ration time global optimal,it took two measures:resources were divided into critcal resources and general resources,and different algorithms were used to key resources and common resources respectively;according to the probability that re-source migration was failure,the limit number of reconfiguration requests was deduced.FRRA algorithm with VNA-II algorithm and PMPA algorithm in the experiments was compared.The results show that algorithm FRRA reduces recon-figuration cost 69% than that of VNA-II and 42% than that of PMPA.Algorithm FRRA improves VN request reception rate 29% than that of VNA-II and 52% than that of PMPA.…”
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Development of Low-Cost Single-Chip Automotive 4D Millimeter-Wave Radar by Yongjun Cai, Jie Bai, Hui-Liang Shen, Libo Huang, Bing Rao, Haiyang Wang

“…Upgrading to 4D millimeter-wave radars enables four-dimensional information perception, enhancing obstacle detection and improving the safety of autonomous driving. Given the high cost-sensitivity of in-vehicle radar systems, single-chip 4D millimeter-wave radar solutions, despite technical challenges in imaging, are of great research value. …”
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An efficient cost calculation method for disparity estimation of stereo matching considering shadow occlusion by Hongjun Sha, Wei Yuan, Xunping Wang, Xiuliu Yuan, Shunichi Koshimura

“…To verify the optimization ability and transferability of MCSP, the MCSP cost is integrated into three classical disparity estimation algorithms respectively. …”
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Dynamic appliance scheduling and energy management in smart homes using adaptive reinforcement learning techniques by Poonam Saroha, Gopal Singh, Umesh Kumar Lilhore, Sarita Simaiya, Monish Khan, Roobaea Alroobaea, Majed Alsafyani, Hamed Alsufyani

“…These dynamics are difficult for traditional systems to handle, but new developments in reinforcement learning and optimization may be able to help. The paper introduces a novel Demand Response (DR) method that integrates a Self-Adaptive Puma Optimizer Algorithm (SAPOA) with a Multi-Objective Deep Q-Network (MO-DQN), improving smart home energy consumption, cost, and user preferences management. …”
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Accounting of infiltration waters and the level of recharge of the root layer by groundwater when calculating the irrigation regime on drained lands by Mazhaysky Yuri, Pavlov Artyom

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Democratization of Crop Irrigation: A Socio-Technical Optimization Approach Using Particle Swarm Optimization by Hasna Elalaoui Elabdallaoui, Youssef Mourdi, Abdelaziz ElFazziki, Mohamed Sadgal

“…It examines how this equitable irrigation approach can contribute to the social economy by improving irrigation efficiency and reducing costs for farmers. …”
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Framework for truck–RPAS hybrid models in last-mile delivery by Armin Mahmoodi, Leila Hashemi, Jeremy Laliberte

“…High-quality Pareto-optimal solutions are produced by the non-dominated sorting genetic algorithm II, which methodically manages trade-offs between the conflicting goals. …”
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Stochastic Multi-objective Distribution Network Reconfiguration Considering Wind Turbines by Ehsan Azad Farsani, Mohsen Zare

“…The objectives of proposed DNR policy are minimization of active power losses, total electrical energy costs, and total emissions of the network. To solve the problem, an improved version of Honey Bee Mating Optimization (IHBMO) algorithm is implemented. …”
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Dynamic parking space allocation at urban scale: Problem formulation and resolution by Hanae Errousso, Jihane El Ouadi, El Arbi Abdellaoui Alaoui, Siham Benhadou

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Multiobjective optimization of suspension bridges via coupled modeling and dual population multiobjective particle swarm optimization by Peiling Yang, Jianhua Deng, Anli Wang

“…The algorithm divides the population into two parts, using the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO) for solving, with improvements to enhance the algorithm’s performance. …”
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Hybrid harbor seal and jellyfish optimization technique for selecting the optimal location of hybrid renewable energy system by Radwa S. A. Gad, Hegazy Zaher, H. Al-Wahsh, M. H. Eid, Wael M. AlMetwaly, Islam M. Abdelqawee

“…Abstract Availability of a dependable electricity source is essential for improving living standards. When designing a micro-grid system, two key factors must be considered: cost-effectiveness and service quality. …”
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THE ALGORITHM FOR THE TREATMENT OF PATIENTS WITH TREATMENT-RESISTANT HYPERTENSION AT THE OUTPATIENT STAGE by O. S. Kulbachuk, E. V. Sid, O. V. Soloviov, A. V. Piskun

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Vehicle-routing problem for low-carbon cold chain logistics based on the idea of cost–benefit by Yan Liu, Fengming Tao, Rui Zhu

“…Therefore, based on the idea of cost–benefit, a comprehensive vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer is presented. …”
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Research on SBMPC Algorithm for Path Planning of Rescue and Detection Robot by Lin-Lin Wang, Li-Xin Pan

“…Sampling-Based Model Predictive Control (SBMPC) algorithm is proposed basing on the construction of cost function and predictive kinematics model. …”
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Blind equalization algorithm based on complex support vector regression by Ling YANG, Liang CHEN, Bin ZHAO, Guolong ZHANG, Yuan LI

“…A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method.…”
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