This paper presents a novel irradiance sensorless Maximum Power Point Tracking (MPPT) controller for photovoltaic (PV) systems using a Particle Swarm Optimization (PSO)-based Integral Backstepping (IBSC) and Immersion...
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This paper presents a novel irradiance sensorless Maximum Power Point Tracking (MPPT) controller for photovoltaic (PV) systems using a Particle Swarm Optimization (PSO)-based Integral Backstepping (IBSC) and Immersion & Invariance (I&I) algorithm. The proposed controller addresses the limitations of traditional and contemporary MPPT methods, such as the need for costly irradiance sensors and suboptimal performance under dynamic environmental conditions. The integration of a higher-order sliding mode differentiator (HOSMD) with the IBSC enhances transient response by completely eliminating overshoots, achieving a 0 % overshoot compared to 4.8 % with the conventional IBSC under standard test conditions. The system exhibits rapid tracking convergence with a significantly reduced tracking time of 0.4 ms, approximately seven times faster than the traditional Perturb and Observe (P&O) algorithm's 3 ms. Under real-world conditions, the proposed system's irradiance estimator maintains a mean absolute error below 15 W/m(2), with a maximum error of 69 W/m(2) at high irradiance levels. The system achieves an operating efficiency of 99.99 % with peak-to-peak power ripples of just 0.17 % under standard conditions, outperforming eight state-of-the-art MPPT techniques. This robust and efficient MPPT solution is validated through extensive simulations and real-climatic conditions. Additionally, real-climatic experimental implementations are carried out using Microcontroller-in-the-loop (MIL) integration. The acquired experimental results do not only corroborate the simulation outcomes but also endorses the reliability and practical robustness of the proposed MPPT controller.
The variability in the availability of network-shared manufacturing resources and the release times of orders pose challenges to the operational decision-making of industrial internet platforms. This paper addresses t...
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The variability in the availability of network-shared manufacturing resources and the release times of orders pose challenges to the operational decision-making of industrial internet platforms. This paper addresses these characteristics by studying the identical parallel machine scheduling problem, aiming to minimize total weighted tardiness under constraints of arbitrary release times and multiple machine unavailability periods. To address this research problem, a decoding mechanism based on machine idle periods is first proposed, effectively solving the impact of machine unavailability periods on the scheduling scheme. Secondly, a multi-population cooperative evolutionary algorithm is designed in which the mechanisms of selection, crossover, mutation, and information exchange between populations are improved. The optimal scheduling properties of two jobs on the same machine and different machines are analyzed, resulting in the design of two local search mechanisms. Additionally, Q-learning is introduced to enhance the adaptability of algorithm parameters by dynamically adjusting them within the multi-population cooperative evolutionary algorithm, resulting in a Q-learning-driven multi-population cooperative evolutionary algorithm with local search (Q-MPCEA-LS). Finally, comparative experiments between the Q-MPCEA-LS algorithm and various metaheuristic algorithms are conducted. The experimental results show that, across all instances, the average relative error in the average value metric of the Q-MPCEA-LS algorithm is 40.0%, 0.1%, 44.2%, and 75.9% lower than that of Q-MPCEALS without local search, Q-MPCEA-LS without Q-learning-based dynamic parameter adjustment, the iterative hybrid metaheuristic algorithm, and the hybrid genetic immune algorithm, respectively. These results validate the effectiveness of the individual components and the overall effectiveness of the Q-MPCEA-LS algorithm.
The time-fractional optimal transport (OT) models are developed to describe the anomalous transport of the agents such that their densities are transported from the initial density distribution to the terminal one wit...
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The time-fractional optimal transport (OT) models are developed to describe the anomalous transport of the agents such that their densities are transported from the initial density distribution to the terminal one with the minimal cost. The general-proximal primal-dual hybrid gradient (G-prox PDHG) algorithm is applied to solve the OT formulations, in which a preconditioner induced by the numerical approximation to the time-fractional PDE is derived to accelerate the convergence of the algorithm. Numerical experiments for OT problems between Gaussian distributions are carried out to investigate the performance of the OT formulations. Those numerical experiments also demonstrate the effectiveness and flexibility of our proposed algorithm.
We study how to infer new choices from prior choices using the framework of choice functions, a unifying mathematical framework for decision-making based on sets of preference orders. In particular, we define the natu...
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We study how to infer new choices from prior choices using the framework of choice functions, a unifying mathematical framework for decision-making based on sets of preference orders. In particular, we define the natural (most conservative) extension of a given choice assessment to a coherent choice function-whenever possible-and use this natural extension to make new choices. We provide a practical algorithm for computing this natural extension and various ways to improve scalability. Finally, we test these algorithms for different types of choice assessments.
There are many different nature-inspired algorithms in the literature, and almost all such algorithms have algorithm-dependent parameters that need to be tuned. The proper setting and parameter tuning should be carrie...
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There are many different nature-inspired algorithms in the literature, and almost all such algorithms have algorithm-dependent parameters that need to be tuned. The proper setting and parameter tuning should be carried out to maximize the performance of the algorithm under consideration. This work is the extension of the recent work on parameter tuning by Joy et al. (2024) presented at the International Conference on Computational Science (ICCS 2024), and the Firefly algorithm (FA) is tuned using three different methods: the Monte Carlo method, the Quasi-Monte Carlo method and the Latin Hypercube Sampling. The FA with the tuned parameters is then used to solve a set of six different optimization problems, and the possible effect of parameter setting on the quality of the optimal solutions is analyzed. Rigorous statistical hypothesis tests have been carried out, including Student's t-tests, F-tests, non-parametric Friedman tests and ANOVA. Results show that the performance of the FA is not influenced by the tuning methods used. In addition, the tuned parameter values are largely independent of the tuning methods used. This indicates that the FA can be flexible and equally effective in solving optimization problems, and any of the three tuning methods can be used tune its parameters effectively.
As the demand for natural gas from downstream users continues to grow, periodic expansions of gas storage facilities have become crucial for ensuring the safety of natural gas supply. The primary challenge in optimizi...
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As the demand for natural gas from downstream users continues to grow, periodic expansions of gas storage facilities have become crucial for ensuring the safety of natural gas supply. The primary challenge in optimizing parameters for multi-period expansion networks is that, with the addition of new platforms and gas wells, the field network may become inadequate for future transportation requirements. Currently, there is a notable research gap concerning the multi-period expansion of gas storage surface pipeline networks (GSSPN). This paper introduces an innovative multi-period expansion network parameter model (MPENP model) based on pipeline structure, alongside an expansion network parameter model (ENP model). To tackle the issue of pipeline parameter discreteness during the solution process, we propose two genetic algorithms: a genetic algorithm based on the modified feasible direction method (GA-MFDM) and a genetic algorithm based on successive linear programming (GA-SLP). These approaches are evaluated through two cases and three scenarios. In Scenario 1, GA-MFDM demonstrates significantly better performance in terms of iteration count and convergence results compared to GA-SLP. Scenario 2 reveals that the network cost optimized by GA-MFDM for the MPENP model is 357.291 x 104 CNY higher than that of the ENP model. However, only the MPENP model's optimization results meet the flow and pressure constraints under both injection and production conditions. Scenario 3 compares the optimized network with the field network, further validating the effectiveness of the MPENP model and GAMFDM. This paper holds profound significance for advancing network design parameter optimization to accommodate periodic expansion needs.
Emerging applications such as telemedicine, the tactile Internet or live streaming place high demands on low latency to ensure a satisfactory Quality of Experience (QoE). In these scenarios the use of trees can be par...
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Emerging applications such as telemedicine, the tactile Internet or live streaming place high demands on low latency to ensure a satisfactory Quality of Experience (QoE). In these scenarios the use of trees can be particularly interesting to efficiently deliver traffic to groups of users because they further enhance network performance by providing redundancy and fault tolerance, ensuring service continuity when network failure or congestion scenarios occur. Furthermore, if trees are isolated from each other (they do not share common communication elements as links and/or nodes), their benefits are further enhanced since events such as failures or congestion in one tree do not affect others. However, the challenge of computing fully disjoint trees (both link- and node-disjoint) introduces significant mathematical complexity, resulting in longer computation times, which negatively impacts latency-sensitive applications. In this article, we propose a novel algorithm designed to rapidly compute multiple fully (either link- or node-) disjoint trees while maintaining efficiency and scalability, specifically focused on targeting the lowlatency requirements of emerging services and applications. The proposed algorithm addresses the complexity of ensuring disjointedness between trees without sacrificing performance. Our solution has been tested in a variety of network environments, including both wired and wireless scenarios. The results showcase that our proposed method is approximately 100 times faster than existing techniques, while achieving a comparable success rate in terms of number of obtained disjoint trees. This significant improvement in computational speed makes our approach highly suitable for the low-latency requirements of next-generation networks.
A comprehensive Grobner system for a parametric ideal I in K(A)[X] represents the collection of all Grobner bases of the ideals I ' in K[X] obtained as the values of the parameters A vary in K. The recent algorith...
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A comprehensive Grobner system for a parametric ideal I in K(A)[X] represents the collection of all Grobner bases of the ideals I ' in K[X] obtained as the values of the parameters A vary in K. The recent algorithms for computing them consider the corresponding ideal J in K[A, X], and are based on stability of Grobner bases of ideals under specializations of the parameters A. Starting from a Grobner basis of J, the computation splits recursively depending on the vanishing of the evaluation of some "coefficients" in K[A]. In this paper, taking inspiration from the algorithm described by Nabeshima, we create a new iterative algorithm to compute comprehensive Grobner systems. We show how we keep track of the sub-cases to be considered, and how we avoid some redundant computation branches using "comparatively-cheap" ideal-membership tests, instead of radical-membership tests.
cost is introduced to change the corresponding control strategy. A hybrid energy storage is used in this model to smooth out the solar power and wind power fluctuations. Hence, a multi-objective artificial hummingbird...
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cost is introduced to change the corresponding control strategy. A hybrid energy storage is used in this model to smooth out the solar power and wind power fluctuations. Hence, a multi-objective artificial hummingbird optimization algorithm is proposed and uses to solve the optimal operation strategy of the microgrid. The final optimal operation strategy is obtained from the Pareto solution set using TOPSIS. The results show that the proposed microgrid system has 20.2 % lower total operating costs, 4.5 % lower carbon emissions, and 32.6 % longer battery life than the conventional microgrid system, which is critical for improving the operation stability, economy, low carbon of the system, and extending the service life of the battery.
Accurate prediction of drug susceptibility is one of themost important steps in personalized *** of machine learning to pharmacogenomic data for sensitivity prediction can help study the mechanism of drug response and...
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Accurate prediction of drug susceptibility is one of themost important steps in personalized *** of machine learning to pharmacogenomic data for sensitivity prediction can help study the mechanism of drug response and find more effective anti-tumor drugs.
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