Search Results (12)

Efficient scheduling is critical for the success of any organization. Researchers have proposed numerous strategies for addressing various scheduling problems. The hybrid flow shop (HFS) scheduling is a complex and NP-hard problem that arises in many manufacturing and service industries. This work introduces an optimization technique that utilizes atomic orbitals to address issues in HFS scheduling. Our objective is to reduce makespan (Cmax). Makespan minimization is critical for improving productivity and resource utilization. The standard atomic orbital search optimization algorithm (AOSOA) is hybridized with constructive heuristics to enhance solution quality. The scheduling problem of an electrical panel board manufacturing industry is solved using the hybrid AOSOA (HAOSOA). The results were better than those previously reported. A variety of random test situations of varying sizes and configurations were devised to assess the efficacy of the suggested algorithm. The proposed algorithm’s outcomes were compared against well-known algorithms discussed in the literature. Friedman and Wilcoxon test results indicate that the proposed methodology improves the solution quality in each test instance compared to all the metaheuristics used for comparison. The performance of the proposed algorithm is also evaluated using benchmark problems from the literature. In the first test, the algorithm has a rank value of 1, indicating it performs better than each of the comparing algorithms. In the second test, it is able to find the best makespan for 65 of the 77 problems. Full article

With a high proportion of renewable energy sources connected to the distribution network, traditional optimal power flow (OPF) methods face significant challenges including multi-objective co-optimization and dynamic scenario adaptation. This paper proposes a dynamic optimization framework based on the Dynamic Gravitational Search Algorithm (DynaG) for a multi-energy complementary distribution network incorporating wind power, photovoltaic, and energy storage systems. A multi-scenario OPF model is developed considering the time-varying characteristics of wind and solar penetration (low/medium/high), seasonal load variations, and demand response participation. The model aims to minimize both network loss and operational costs, while simultaneously optimizing power supply capability indicators such as power transfer rates and capacity-to-load ratios. Key enhancements to DynaG algorithm include the following: (1) an adaptive gravitational constant adjustment strategy to balance global exploration and local exploitation; (2) an inertial mass updating mechanism constrained to improve convergence for high-dimensional decision variables; and (3) integration of chaotic initialization and dynamic neighborhood search to enhance solution diversity under complex constraints. Validation using the IEEE 33-bus system demonstrates that under 30% penetration scenarios, the proposed DynaG algorithm reduces capacity ratio volatility by 3.37% and network losses by 1.91% compared to non-dominated sorting genetic algorithm III (NSGA-III), multi-objective particle swarm optimization (MOPSO), multi-objective atomic orbital search algorithm (MOAOS), and multi-objective gravitational search algorithm (MOGSA). These results show the algorithm’s robustness against renewable fluctuations and its potential for enhancing the resilience and operational efficiency of high-penetration renewable energy distribution networks. Full article

Second-order Linear Active Disturbance Rejection Controller (SLADRC) is a powerful control technique. Ongoing research is focused on simplifying tuning procedures, extending applicability to handle more complex systems, and ensuring efficient real-time implementation. In this proposed work, four different tuning approaches, using the Atomic Orbital Search (AOS) optimization algorithm concerning the number of tuning parameters, are presented. The performance of each tuning method for stabilizing the rotary inverted pendulum in the upright position and tracking trajectory is analyzed and validated through simulation and experimentation. The results indicate that the reduced number of SLADRC controller parameters tuned using AOS optimization provides superior performance compared to the controller with more tuning parameters for the nonlinear rotary inverted pendulum. From the analysis method, II tuning, $b_0, {\omega }_c, \mathrm{a}\mathrm{n}\mathrm{d} k$ provide the optimum results of settling time ($T_s$), 1.5 s, and maximum angle deviation of $\theta \left(3.8°\right), \alpha (3°)$. Full article

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. Full article

The efficient extraction of solar PV power is crucial to maximize utilization, even in rapidly changing environmental conditions. The increasing energy demands highlight the importance of solar photovoltaic (PV) systems for cost-effective energy production. However, traditional PV systems with bypass diodes at their output terminals often produce multiple power peaks, leading to significant power losses if the optimal combination of voltage and current is not achieved. To address this issue, algorithms capable of finding the highest value of a function are employed. Since the PV power output is a complex function with multiple local maximum power points (LMPPs), conventional algorithms struggle to handle partial shading conditions (PSC). As a result, nature-inspired algorithms, also known as metaheuristic algorithms, are used to maximize the power output of solar PV arrays. In this study, we introduced a novel metaheuristic algorithm called atomic orbital search for maximum power point tracking (MPPT) under PSC. The primary motivation behind this research is to enhance the efficiency and effectiveness of MPPT techniques in challenging scenarios. The proposed algorithm offers several advantages, including higher efficiency, shorter tracking time, reduced output variations, and improved duty ratios, resulting in faster convergence to the maximum power point (MPP). To evaluate the algorithm’s performance, we conducted extensive experiments using Typhoon HIL and compared it with other existing algorithms commonly employed for MPPT. The results clearly demonstrated that the proposed atomic orbital search algorithm outperformed the alternatives in terms of rapid convergence and efficient MPP tracking, particularly for complex shading patterns. This makes it a suitable choice for developing an MPP tracker applicable in various settings, such as industrial, commercial, and residential applications. In conclusion, our research addresses the pressing need for effective MPPT methods in solar PV systems operating under challenging conditions. The atomic orbital search algorithm showcases its potential in significantly improving the efficiency and performance of MPPT, ultimately contributing to the optimization of solar energy extraction and utilization. Full article

Mobile edge computing (MEC) powered by unmanned aerial vehicles (UAVs), with the advantages of flexible deployment and wide coverage, is a promising technology to solve computationally intensive communication problems. In this paper, an orthogonal time frequency space (OTFS)-based UAV-assisted MEC system is studied, in which OTFS technology is used to mitigate the Doppler effect in UAV high-speed mobile communication. The weighted total energy consumption of the system is minimized by jointly optimizing the time division, CPU frequency allocation, transmit power allocation and flight trajectory while considering Doppler compensation. Thus, the resultant problem is a challenging nonconvex problem. We propose a joint algorithm that combines the benefits of the atomic orbital search (AOS) algorithm and convex optimization. Firstly, an improved AOS algorithm is proposed to swiftly obtain the time slot allocation and high-quality solution of the UAV optimal path. Secondly, the optimal solution for the CPU frequency and transmit power allocation is found by using Lagrangian duality and the first-order Taylor formula. Finally, the optimal solution of the original problem is iteratively obtained. The simulation results show that the weighted total energy consumption of the OTFS-based system decreases by 13.6% compared with the orthogonal frequency division multiplexing (OFDM)-based system. The weighted total energy consumption of the proposed algorithm decreases by 11.7% and 26.7% compared with convex optimization and heuristic algorithms, respectively. Full article

Neutral (n) and zwitterionic (z) forms of cysteine monomers are combined in this work to extensively explore the potential energy surfaces for the formation of cysteine dimers in aqueous environments represented by a continuum. A simulated annealing search followed by optimization and characterization of the candidate structures afforded a total of 746 structurally different dimers held together via 80 different types of intermolecular contacts in 2894 individual non-covalent interactions as concluded from Natural Bond Orbitals (NBO), Quantum Theory of Atoms in Molecules (QTAIM) and Non-Covalent Interactions (NCI) analyses. This large pool of interaction possibilities includes the traditional primary hydrogen bonds and salt bridges which actually dictate the structures of the dimers, as well as the less common secondary hydrogen bonds, exotic X⋯Y (X = C, N, O, S) contacts, and H⋯H dihydrogen bonds. These interactions are not homogeneous but have rather complex distributions of strengths, interfragment distances and overall stabilities. Judging by their Gibbs bonding energies, most of the structures located here are suitable for experimental detection at room conditions. Full article

Connection between electric power networks is essential to cover any deficit in the generation of power from any of them. The exchange powers of the plants during load disturbance should not be violated beyond their specified values. This can be achieved by installing load frequency control (LFC); therefore, this paper proposes a new metaheuristic-based approach using a skill optimization algorithm (SOA) to design a fractional-order proportional integral derivative (FOPID)-LFC approach with multi-interconnected systems. The target is minimizing the integral time absolute error (ITAE) of frequency and exchange power violations. Two power systems are investigated. The first one has two connected plants of photovoltaic (PV) and thermal units. The second system contains four plants, namely, PV, wind turbine, and two thermal plants, with governor dead-band (GDB) and generation rate constraints (GRC). Different load disturbances are analyzed in both considered systems. Extensive comparisons to the use of chef-based optimization algorithm (CBOA), jumping spider optimization algorithm (JSOA), Bonobo optimization (BO), Tasmanian devil optimization (TDO), and Atomic orbital search (AOS) are conducted. Moreover, statistical tests of Friedman ANOVA table, Wilcoxon rank test, Friedman rank test, and Kruskal Wallis test are implemented. Regarding the two interconnected areas, the proposed SOA achieved the minimum fitness value of 1.8779 pu during 10% disturbance on thermal plant. In addition, it outperformed all other approaches in the case of 1% disturbance on the first area as it achieved ITAE of 0.0327 pu. The obtained results proved the competence and reliability of the proposed SOA in designing an efficient FOPID-LFC in multi-interconnected power systems with multiple sources. Full article

An accurate and stable reservoir prediction model is essential for oil location and production. We propose an predictive hybrid model ILSTM-BRVFL based on an improved long short-term memory network (IAOS-LSTM) and a bidirectional random vector functional link (Bidirectional-RVFL) for this problem. Firstly, the Atomic Orbit Search algorithm (AOS) is used to perform collective optimization of the parameters to improve the stability and accuracy of the LSTM model for high-dimensional feature extraction. At the same time, there is still room to improve the optimization capability of the AOS. Therefore, an improvement scheme to further enhance the optimization capability is proposed. Then, the LSTM-extracted high-dimensional features are fed into the random vector functional link (RVFL) to improve the prediction of high-dimensional features by the RVFL, which is modified as the bidirectional RVFL. The proposed ILSTM-BRVFL (IAOS) model achieves an average prediction accuracy of 95.28%, compared to the experimental results. The model’s accuracy, recall values, and F1 values also showed good performance, and the prediction ability achieved the expected results. The comparative analysis and the degree of improvement in the model results show that the high-dimensional extraction of the input data by LSTM is the most significant improvement in prediction accuracy. Secondly, it introduces a double-ended mechanism for IAOS to LSTM and RVFL for parameter search. Full article

Density functional theory, DFT, calculations were carried out on complexes containing cyclopentadienyl anions and lithium or sodium cations; half-sandwich, sandwich and sandwich-like complexes (among them triple-decker ones) are analyzed. Searches performed through the Cambridge Structural Database revealed that crystal structures containing these motifs exist, mostly structures with sodium cations. The DFT calculations performed here include geometry optimization and frequency calculations of the complexes at the ωB97XD/aug-cc-pVTZ level, followed by the partitioning of the energy of interaction via the Energy Decomposition Analysis scheme, EDA, at the BP86-D3/TZ2P level. Additional calculations and analyses were performed using both the Quantum Theory of Atoms in Molecules, QTAIM, and the Natural Bond Orbital analyses, NBO. The results of this work show that the electrostatic interaction energy is the most important attractive contribution to the total interaction energy of each of the complex systems analyzed here, and that complexation itself leads to minor electron charge shifts. Full article

The popularization and use of green buildings are of great significance for reducing the carbon emissions of buildings and achieving sustainable development. Scientific evaluation of the green building design scheme is the key factor in ensuring the popularization and use of green buildings. To overcome the shortage of a systematic evaluation index system and comprehensive evaluation method, an evaluation index system of green building design schemes and an evaluation method based on the projection pursuit model were developed. First, according to the needs of green building development, an evaluation index system of green building design schemes was systematically constructed from the five aspects of the economy, the resource utilization index, environmental impacts, technical management, and social impacts. The calculation methods of all secondary indexes are provided in detail. Then, a novel evaluation method based on the projection pursuit model optimized by the atomic orbital search was constructed. This method searches for key influencing factors and determines the evaluation grade from the evaluation data structure, and realizes the scientific and objective evaluations of green building design schemes. Finally, the Nanchang Hengda Project was selected to conduct a detailed empirical study. The research results show that the incremental net present value of the investment, the energy consumption of the air conditioning system, and the ratio of the window area to the indoor area are the most important secondary indexes. Moreover, the environmental impact index was found to be the most important primary index. Via comparisons with different optimization algorithms and evaluation methods, the superiority of the proposed model is proven. Full article

In this manuscript, a hybrid system depending on the optimal location of electric vehicle parking lots (PL) and capacitors under voltage profile care and power loss is proposed. The proposed hybrid scheme is the joint execution of both the atomic orbital search (AOS) and arithmetic optimization algorithm (AOA). Commonly it is called the AOSAOA technique. In the paper, the allocation of the parking lot and capacitor is introduced to congestion management with reactive power compensation. To optimally regulate that parking lot size, the AOSAOA technique is adopted. Furthermore, parking lot and capacitor allocation are introduced to congestion management and reactive power compensation. With this proper control, the perfect sitting of capacitor and EV parking lots under the grid, including the deterioration of real and reactive power loss and voltage profiles are optimally chosen. Furthermore, the implementation of the proposed AOSAOA model is developed by the MATLAB/Simulink platform, and the efficiency of the proposed model is likened to other techniques. Full article