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Human activities and climate change have exacerbated the frequency of extreme weather events such as rainstorms and floods, which makes it difficult to accurately quantify the uncertainty characteristics in runoff prediction. Therefore, the lower and upper boundary estimation method (LUBE) has become an important means to quantify uncertainty and has been widely used. However, the traditional interval prediction evaluation system only relies on coverage and width indicators, and performs poorly in single-objective optimization methods, which limits the large-scale application of the LUBE method. Based on this, this study innovatively proposes the prediction interval fitting coefficient (PIFC), and combines the prediction interval coverage probability (PICP) and normalized average width index (PINAW) to construct the coverage width fitting-based criterion (CWFC) for the first time, which broadens and improves the interval prediction evaluation dimension system. Further, the single-objective and multi-objective LUBE interval forecasting models based on the randomized weighted particle swarm algorithm (RWPSO) and the non-dominated sorting genetic algorithms III (NSGA-III) are constructed in this study. The verification results of cascade hydropower stations in the Yalong river basin show that the calculation efficiency and prediction effect of the single target interval prediction model are both improved after the introduction of PIFC. Under the CWFC objective function, the PINAW and PIFC indexes in the prediction interval are significantly better, and the PICP gap is smaller. Under multi-objective conditions (PICP, PINAW and PIFC), the Pareto non-inferior solution set can provide more choices for decision makers. During the flood season, PICP can reach more than 93%, PINAW is controlled below 10%, and PIFC can reach more than 0.95. This fully proves that the performance of interval prediction has been significantly improved after the introduction of PIFC, and the research results can provide a new way for basin interval prediction.

Low Earth orbit (LEO) satellites located at altitudes of 500 km~1500 km can carry much stronger signals and move faster than medium Earth orbit (MEO) satellites at about a 20,000 km altitude. Taking advantage of these features, LEO satellites promise to make contributions to navigation and positioning where global navigation satellite system (GNSS) signals are blocked as well as the rapid convergence of precise point positioning (PPP). In this paper, LEO-based optimal global navigation and augmentation constellations are designed by a non-dominated sorting genetic algorithm III (NSGA-III) and genetic algorithm (GA), respectively. Additionally, a LEO augmentation constellation with GNSS satellites included is designed using the NSGA-III. For global navigation constellations, the results demonstrate that the optimal constellations with a near-polar Walker configuration need 264, 240, 210, 210, 200, 190 and 180 satellites with altitudes of 900, 1000, 1100, 1200, 1300, 1400 and 1500 km, respectively. For global augmentation constellations at an altitude of 900 km, for instance, 72, 91, and 108 satellites are required in order to achieve a global average of four, five and six visible satellites for an elevation angle above 7 degrees with one Walker constellation. To achieve a more even coverage, a hybrid constellation with two Walker constellations is also presented. On this basis, the GDOPs (geometric dilution of precision) of the GNSS with and without an LEO constellation are compared. In addition, we prove that the computation efficiency of the constellation design can be considerably improved by using master–slave parallel computing.

The present study establishes an optimization procedure in several sample networks with the objective of reducing the cost and eliminating the pressure deficit throughout the entire network. The optimization mechanism is implemented by coding the novel self-adaptive F-NSGA-III algorithm with a fuzzy structure in the MATLAB environment and integrating it with the EPANET framework. Initial definition of the cost function is based on the correlation between the cost, diameter, and length of the pipes. Subsequently, the program is ran through 10,000 and 20,000 iterations. In order to enhance the rate of convergence, the cost resulting from the breach of the permissible pressure range (minimum: 30 m and maximum: 60 m) is included into this function. Subsequently, the program is executed once more to get the optimal solution. The results indicate that the F-NSGA-III algorithm, has superior speed in identifying optimal solutions as compared to NSGA-II. In this method, the utilization of fuzzy structure and the consideration of the cost of violating the permissible pressure in each iteration result in the fastest attainment of the optimal response, as previously achieved by other researchers, for sample networks. Therefore, this structure yields more optimal solutions with fewer iterations and substantially reduces the time required to achieve convergence.

We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method on EEG data of 24 patients from the CHB-MIT public dataset. The procedure starts by decomposing the EEG data from each channel into different frequency bands using the empirical mode decomposition (EMD) or the discrete wavelet transform (DWT), and then for each sub-band four features are extracted; two energy values and two fractal dimension values. The obtained feature vectors are then iteratively tested for solving two unconstrained objectives by NSGA-II or NSGA-III; to maximize classification accuracy and to reduce the number of EEG channels required for epileptic seizure classification. Our results have shown accuracies of up to 1.00 with only one EEG channel. Interestingly, when using all the EEG channels available, lower accuracies were achieved compared to the case when EEG channels were selected by NSGA-II or NSGA-III; i.e in patient 19 we obtained an accuracy of 0.95 using all the channels and 0.975 using only two channels selected by NSGA-III. The results obtained are encouraging and it has been shown that it is possible to classify epileptic seizures using a few electrodes, which provide evidence for the future development of portable EEG seizure detection devices.

The Internet of Vehicles (IoV) has gained worldwide attentions as it provides the service of collecting real-time traffic information to improve the road safety. The IoV users can offload their computing tasks to the edge computing devices (ECDs) for low latency execution and the cloud can be engaged to process big data with sufficient computing resources. Though galactic convenience brought by the IoV cloud-edge computing system, it remains a challenge to manage the resource of ECDs by reducing the energy and time consumption while avoiding the situation of overload or underload of the ECDs to maintain the system-stability. Moreover, during the movement of the vehicles, the computing tasks and data may be uploaded to different ECDs and the data continuity may be destroyed. In this paper, a multi-objective computation offloading method (MOC) for IoV in cloud-edge computing is proposed to deal with the challenges above. A vehicle-to-vehicle communication-based route obtaining algorithm is designed first. Then, in order to ensure the trustworth of the IoV data, which ECD to upload the computing tasks to is selected. Under the case that all ECDs are overloaded, the computation offloading between ECDs and cloud is considered. In addition, non-dominated sorting genetic algorithm III is adopted to realize the multi-objective optimization of decreasing the load balancing rate and reduce the energy consumption in ECDs and shorten the time during processing the computing tasks. Furthermore, we employ the simple additive weighting and multiple criteria decision making to evaluate the solutions of our proposed method. Finally, experimental evaluations are conducted to validate the efficiency and effectiveness of our proposed method.

Chaotic maps are extensively utilized in the field of image encryption to generate secret keys. However, these maps suffer from hyper-parameters tuning issues. These parameters are generally selected on hit and trial basis. However, inappropriate selection of these parameters may reduce the performance of chaotic maps. Also, these hyper-parameters are not sensitive to input images. Therefore, in this paper, to handle these issues, a non-dominated sorting genetic algorithm-III (NSGA) based 4-D chaotic map is designed. Additionally, to improve the computational speed of the proposed approach, we have designed a novel master-slave model for image encryption. Initially, computationally expensive operations such as mutation and crossover of NSGA-III are identified. Thereafter, NSGA-III parameters are split among two jobs, i.e., master and slave jobs. For communication between master and slave nodes, the message passing interface is used. Extensive experimental results reveal that the proposed image encryption technique outperforms the existing techniques in terms of various performance measures.

The use of renewable energies strengthens decarbonization strategies. To integrate volatile renewable sources, energy systems require grid expansion, storage capabilities, or flexible consumption. This study focuses on industries that adapt production to real-time energy markets, offering flexible consumption to the grid. Flexible production considers not only traditional goals like minimizing production time, but also minimizing energy costs and emissions, thereby enhancing the sustainability of businesses. However, existing research focuses on single goals, neglects the combination of makespan, energy costs, and emissions, or assumes constant or periodic tariffs instead of a dynamic energy market. We present a novel memetic NSGA-III to minimize makespan, energy cost, and emissions, integrating real energy market data, and allowing manufacturers to adapt energy consumption to current grid conditions. Evaluating it with benchmark instances from literature and real energy market data, we explore the trade-offs between objectives, showcasing potential savings in energy costs and emissions on estimated Pareto fronts.

In order to alleviate the problem of water resources shortage and unequal distribution in time and space in Guyuan City, this paper firstly analyzes the basic situation of water resources in Guyuan City, and constructs a multi-objective optimal allocation model of water resources with economic benefits, social benefits and ecological benefits as the objectives. Then, NSGA-III algorithm is proposed to solve the optimal allocation scheme of water resources. Based on the original NSGA-III algorithm, multi-point crossover operator and multi-point mutation operator, namely non-dominated sorting genetic algorithm-III with Random multipoint crossover mutation(RNSGA-III), are used. In the selection of the next generation, SAW and MCDM schemes were used to evaluate the chromosomes and set the weights to achieve better and more ideal results. Finally, the proposed scheme is compared with the existing configuration scheme based on NSGA-III and NSGA-II algorithms. Simulation experiments show that the proposed scheme is better than the allocation scheme based on NSGA-III algorithm and NSGA-II algorithm in the comprehensive cost of economic benefits, water shortage and ecological benefits, which can provide a basis for the rational allocation of water resources in Guyuan City, achieve unified management and sustainable utilization of water resources and support the sustainable development of economy and society.

The increasing intricacy of modern microgrids, driven by uncertain consumption patterns, decentralized renewables, and user behavioral dynamics, calls for innovative optimization methodologies. This study introduces a hybrid quantum-classical framework for demand-side energy management, leveraging behavioral modeling to foster resilience and flexibility. By embedding principles from Social Cognitive Theory—such as behavioral imitation, confidence in personal capability, and social reinforcement—into a multi-objective optimization scheme, the model supports distributed decision-making and promotes adaptive prosumer behavior. The proposed approach employs Quantum Annealing in combination with NSGA-III to efficiently navigate the complex solution space, accounting for real-time uncertainties and the stochastic nature of both demand and renewable supply. The framework is tested within a case study of a peer-to-peer microgrid network, showcasing its effectiveness in enhancing energy efficiency, lowering peak demand, and improving operational resilience. Performance comparisons with traditional methods, including Mixed-Integer Programming and conventional metaheuristics, underline the improved scalability and robustness of the quantum-inspired model in handling trade-offs between cost, reliability, and socially-driven demand response. The research highlights the potential of integrating quantum-inspired optimization with behavioral energy modeling to advance intelligent and socially-responsive microgrid control systems.

Nowadays, manufacturing systems should be cost-effective and environmentally harmless to cope with various challenges in today’s competitive markets. This paper aims to solve an environmental-oriented multi-objective reconfigurable manufacturing system design (i.e., sustainable reconfigurable machines and tools selection) in the case of a single-unit process plan generation. A non-linear multi-objective integer program (NL-MOIP) is presented first, where four objectives are minimized respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted by machines, and the hazardous liquid wastes. Second, to solve the problem, we propose four adapted versions of evolutionary approaches, namely two versions of the well-known non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), weighted genetic algorithms (WGA), and random weighted genetic algorithms (RWGA). To show the efficiency of the four approaches, several instances of the problem are experimented, and the obtained results are analyzed using three metrics respectively hypervolume, spacing metric, and cardinality of the mixed Pareto fronts. Moreover, the influences of the probabilities of genetic operators (crossover and mutation) on the convergence of the adapted NSGA-III are analyzed. Finally, the TOPSIS method is used to help the decision-maker ranking and select the best process plans.