Search Results (908)

Designing effective drug therapies requires balancing competing objectives, such as therapeutic efficacy, safety, and cost efficiency—a task that poses significant challenges for conventional optimization methods. To address this, we propose the multi-objective spider–wasp optimizer (MOSWO), a novel approach uniquely emulating the cooperative predation dynamics between spiders and wasps observed in nature. MOSWO integrates adaptive mechanisms for exploration and exploitation to resolve complex trade-offs in multiobjective drug design. Unlike existing approaches, the algorithm employs a dynamic population-partitioning strategy inspired by predator–prey interactions, enabling efficient Pareto frontier discovery. We validate MOSWO’s performance through extensive experiments on synthetic benchmarks and real-world case studies spanning antiviral and antibiotic therapies. Results demonstrate that MOSWO surpasses state-of-the-art methods (NSGA-II, MOEA/D, MOGWO, and MOPSO), achieving 11% higher hypervolume scores, 8% lower inverted generational distance scores, 9% higher spread scores, a 30% faster convergence, and superior robustness against noisy biological datasets. The framework’s adaptability to diverse therapeutic scenarios underscores its potential as a transformative tool for computational pharmacology. Full article

Recently, forward-scatter radars (FSRs) utilizing the Global Navigation Satellite System (GNSS) as a radiation source have gained increasing attention. The radar system enables aerial target surveillance by deploying multiple receiving nodes on the ground. It offers a low-cost and easily deployable solution. Therefore, how to deploy the receiving nodes to achieve efficient utilization of node resources is an urgent problem to be addressed. In this paper, a deployment method was proposed for receiving nodes in a single-transmitter and multiple-receiver configuration. First, the problem was reformulated as an optimal equal-circle covering problem via geometric approximation. A multi-objective optimization model was subsequently established with the objective functions of minimizing node cost and maximizing spatial detection area. Second, a method based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was introduced to obtain the sub-optimal solution of node cost, thereby reducing the computational complexity of the optimization process. Finally, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to derive the deployment schemes. Then, these schemes were ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the Entropy Weight Method (EWM). The results indicate that the proposed method can obtain the optimal deployment scheme compared to the existing method and enhance the diversity of the solutions. Full article

This study aims to evaluate and optimize the influences of operational factors, including the engine’s rotational speed, methane mass, diesel mass, and the duration of injected diesel fuel on the methane/diesel reactivity-controlled compression ignition (RCCI) light-duty engine’s performance and emissions by executing the Nondominated Sorting Genetic Algorithm-II (NSGAII). The optimizations aimed to minimize peak firing pressure simultaneously, decrease indicated specific fuel consumption, and reduce tailpipe emissions. It is found that the excess air ratios of (2.22 to 2.37) are the range of feasible results of the RCCI engine, and the power should be less than 0.89 from the maximum design load of the diesel engine when it works without it after treatment. The methane/diesel RCCI engine achieves an indicative thermal efficiency of 51%. The Pareto results from the NSGA algorithm occur on multiple fronts, and there is a tradeoff between power and nitrogen oxide (NOx) in addition to unburned hydrocarbons (UHCs) and carbon monoxide (CO) with NOx emissions. Moreover, EURO IV emissions regulations can occur when using a start of injection (SOI) of −35 CA, a diesel mass of 1.82 mg, a methane mass of 9.74 mg, a diesel injection duration of 2.63 CA, and a rotational speed of 2540 rpm. This accomplished a reduction in indicative specific fuel consumption by 27.8%, higher indicative efficiency by 21.9%, and emissions reductions compared to a conventional diesel engine. Full article

Due to external environmental factors, the layout of compound channels in estuarine ports is restricted. Moreover, with the opposing distribution of anchorages and terminals within the port, vessels navigating between these areas must cross the channel, severely affecting channel navigation safety and efficiency. In order to improve the efficiency of vessel scheduling, we analyze the layout characteristics of an estuarine port and its compound channel, summarize vessel navigation modes and traffic flow conflicts, and identify five key conflict areas. On this basis, we develop a multi-objective optimization model aimed at minimizing vessel waiting times and the total channel occupancy time ratio. This model incorporates constraints such as navigation rules, traffic flow conflicts, tidal effects, and traffic control. To solve the model, we propose an adaptive non-dominated sorting genetic algorithm, ANSGA-NS-SA, which integrates neighborhood search (NS) and Simulated Annealing (SA). The entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) is used to calculate the objective weights of the Pareto frontier and identify the optimal solution. Experimental results show that the proposed model and algorithm yield optimal port entry and exit scheduling solutions. In terms of port scheduling performance, the proposed model and algorithm outperform the traditional First-Come-First-Served (FCFS) strategy and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), reducing total vessel waiting time by 33.8% and improving total channel occupancy ratio by 8.8%. This study provides a practical and effective decision support tool for estuarine port scheduling, enhancing overall port operational efficiency. Full article

In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local optimality. To address these challenges, a bilevel optimization framework based on GA-NSGA-II and task segmentation is proposed to balance the total inspection distance and the distance standard deviation of UAVs, where the outer optimization employs the NSGA-II to assign task units to each UAV evenly, while the inner optimization deploys an adaptive genetic algorithm with an elite retention strategy to optimize the inspection direction and order in each task domain to obtain a Pareto-optimal solution set under constraints. To avoid the dimensionality disaster, the massive inspection points are combined into task units based on the UAV’s endurance. In scenarios with 284 tower task points, the proposed algorithm has reduced the standard deviation of UAV flight distances by 41.91% to 84.63% and the longest flight distance by 29.41% to 43.98% compared to the GA-GA bilevel optimization. Against task-adaptive clustering optimization, it decreased the standard deviation by 18.25% to 94.93% and the longest flight distance by 15.97% to 37.33%. Applying it to 406 tower task points also confirmed the GA-NSGA-II bilevel optimization’s effectiveness in minimizing the total inspection distance and balancing UAV workloads. Full article

Mobile drip irrigation (MDI) systems integrate the technological advantages of center-pivot irrigation (CPI) systems and drip irrigation systems, boasting a high water-saving potential. To further enhance water use efficiency in alfalfa production in northern China, this preliminary study verified the accuracy of the HYDRUS-2D soil water movement numerical model through field experiments. Using the numerical model, four drip-line installation distances (60, 75, 90, and 105 cm), three deficit irrigation thresholds (45–50% FC, 55–60% FC, and 65–70% FC), and four irrigation depths (70% W, 85% W, 100% W, and 115% W) were set to simulate root water uptake, soil surface evaporation, total irrigation amount, and deep percolation during the entire growth cycle of alfalfa, respectively. Objective functions were constructed according to the simulation results, and the NSGA-II algorithm was used for multi-objective optimization of the deficit irrigation schedule. The preliminary results indicated that HYDRUS-2D can accurately simulate soil water movement under MDI systems, as the RMSE values of soil water content at all measured depths were less than 0.021 cm³/cm³, with the NRMSE values being below 23.3%, and the MAE values below 0.014 cm³/cm³. Increasing the deficit irrigation threshold from F1 to F3 enhanced root water uptake by 12.24–15.34% but simultaneously increased the total irrigation amount, soil surface evaporation (by up to 29.58%), and the risk of deep percolation; similar trends were observed with increasing irrigation depth. The drip-line installation distance had no significant impact on irrigation performance. The NSGA-II multi-objective optimization algorithm was used to obtain Pareto-optimal solutions that balance conflicting objectives. For this case study, a drip-line installation distance of 105 cm, a deficit irrigation threshold of 50–55% FC, and an irrigation depth of 112% W were recommended to achieve balance among the various optimization objectives. This study provides a preliminary framework for optimizing MDI systems and irrigation strategies. However, since a deeper root distribution (>80 cm) was not investigated in this study, future research incorporating deeper root zones is required for developing more comprehensive irrigation scheduling suitable for typical alfalfa cultivation scenarios. Full article

As an efficient management pattern to improve logistics efficiency through intensive management of construction materials, construction logistics centers (CLCs) have received active attention from academics and practitioners. However, the CLC location–allocation problem, which considers periodic demand and transportation risk, has not been adequately solved. This work provides an approach to integrating transportation path risk into multiperiod CLC location–allocation optimization for large-scale projects in complex environmental regions. For this purpose, this paper formulates a hybrid non-linear integer planning model to define this location–allocation problem and minimize the total cost of construction logistics and transportation risk. The model also incorporates critical features from realistic scenarios, including CLC’s service coverage, capacity constraints, and minimum utilization limits. We have designed an NSGA-II based on endocrine hormone regulation (EHR-NSGA-II) to solve the model. Finally, a large-scale railroad construction project in complex environmental regions is used as an example to prove the effectiveness of the model and algorithm. Compared with the single-period model, the multiperiod model designed in this paper provides a total cost reduction of 8.11% for the CLC location–allocation scheme. In addition, analyzing several key parameters provides valuable insights for managers to design more reliable construction logistics networks. Full article

To address the limitations of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) in optimizing active glass curtain wall shading systems—particularly its suboptimal convergence efficiency and high computational demands—this study proposes an improved NSGA-II algorithm incorporating parameter categorization. Shading system parameters (e.g., slat width, angle, separation, and blind-to-glass distance) are classified into distinct categories based on their character and optimized sequentially. This phased approach reduces the search space dimensionality, lowering computational complexity while maintaining optimization accuracy. The framework integrates user preferences and climatic adaptability to balance energy efficiency and glare mitigation. The louver parameters were optimized under the same experimental conditions, and the enhanced algorithm exhibits 49% lower energy consumption values and 5% smaller visual discomfort time duration compared to the baseline algorithm in the optimization outcomes. Full article

High-speed railways (HSRs), with their advantages of safety, energy conservation, and convenience, are increasingly becoming the preferred mode of transportation. Railway operators schedule full-schedule timetables to operate as many trains and serve as many passengers as possible. However, due to the fluctuation in passenger demands, it is not necessary to operate all trains in full-schedule timetable, which results in high operation costs and too much energy consumption. Based on this, we propose an integrated approach to schedule passenger train plans and train timetables by selecting trains to operate from the full-schedule timetable, adjusting their stopping scheme and operation sequence to reduce operation costs and energy consumption and contribute to sustainable development. In the scheduling process, both operation costs and passenger service quality are considered, and a two-objective model is established. An algorithm is designed based on Non-dominated Sorting Genetic Algorithms-II (NSGA-II) to solve the model, containing techniques for acceleration that utilize overtaking patterns, in which overtaking chromosomes are used to illustrate the train operation sequence, and parallel computing, in which the decoding process is computed in parallel. A set of Pareto fronts are obtained to offer a diverse set of results with different operation costs and passenger service quality. The model and algorithm are verified by cases based on the Beijing–Shanghai HSR line. The results indicate that compared to the full-schedule timetable, the operation costs under three sets of passenger demands decreased by 35.4%, 27.7%, and 15.7% on average. Compared to the genetic algorithm with weighting multiple objectives and NSGA-II without acceleration techniques, the algorithm proposed in this paper with the two acceleration techniques of utilizing overtaking patterns and parallel computing can significantly accelerate the solution process, with an average reduction of 42.9% and 38.3% in calculation time, indicating that the approach can handle the integrated scheduling problem economically and efficiently. Full article

A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted for various products, and this study assumes the transport of a fresh/green product with gradually decreasing quality due to its perishable nature. In a two-stage cold supply chain, multiple objective functions can be defined, including cost minimization, product quality optimization, and transportation/storage condition optimization. We developed a mathematical model to optimize these objectives, incorporating two specific functions, cost minimization and product age reduction, to ensure efficient supply chain performance. Traditional solution methods often struggle with multi-objective mathematical models due to their complexity. Therefore, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a Genetic Algorithm-based approach, was applied to solve the model efficiently. NSGA-II optimized planning for a 7-day period under specific demand conditions, ensuring better resource allocation. The results showed that NSGA-II was better than traditional methods at making decisions and routing efficiently in the two-stage cold supply chain. This led to much better outcomes, with lower costs, less waste, and better product quality throughout the process. Full article

In recent years, the urgent need to mitigate stormwater runoff and address urban waterlogging has garnered significant attention. Low Impact Development (LID) has emerged as a promising strategy for managing urban runoff sustainably. However, the vast array of potential LID layout combinations presents challenges in quantifying their effectiveness and often results in high construction costs. To address these issues, this study proposes a simulation-optimization framework that integrates the Storm Water Management Model (SWMM) with advanced optimization techniques to minimize both runoff volume and costs. The framework incorporates random variations in rainfall intensity within the basin, ensuring robustness under diverse climatic conditions. By leveraging a multi-objective scatter search algorithm, this research optimizes LID layouts to achieve effective stormwater management. The algorithm is further enhanced by two local search techniques—namely, the ‘cost–benefit’ local search and path-relinking local search—which significantly improve computational efficiency. Comparative analysis reveals that the proposed algorithm outperforms the widely used NSGA-II (Non-dominated Sorting Genetic Algorithm II), reducing computation time by an average of 8.89%, 16.98%, 1.72%, 3.85%, and 1.23% across various scenarios. The results demonstrate the method’s effectiveness in achieving optimal LID configurations under variable rainfall intensities, highlighting its practical applicability for urban flood management. This research contributes to advancing urban sponge city initiatives by providing a scalable, efficient, and scientifically grounded solution for sustainable urban water management. The proposed framework is expected to support decision-makers in designing cost-effective and resilient stormwater management systems, paving the way for more sustainable urban development. Full article

Due to the anisotropic structure and mechanical properties of composite laminates, internal damage cracks can easily occur. In this study, orthotropic anisotropic glass-fiber-reinforced polymer composites were used as the repair object. Firstly, the anisotropic material was analyzed using the finite element method, the self-healing structural compliance, water head loss, and volume percentage of the microvascular network were taken as the objective functions, and the topology optimization of the microvascular network structure was carried out using non-dominated soring genetic algorithm II. Secondly, the self-healing material with a wall-less microvascular network was prepared via the vacuum-assisted resin transfer molding process and the embedded wire removal method. Finally, the light repair performance was tested using the three-point bending test. The results show that in the case of no intervention for light repair, the average maximum failure load of the self-healing structure after embedding the microvascular network can reach 94.06% of that before embedding; with the introduction of real-time light repair, the average maximum failure load of the self-healing structure with light repair was increased by 4.1% compared with the self-healing structure without light repair. Meanwhile, the second peak load of the light-repaired structure can reach 51.36% of the average maximum failure load, which is 28.56% higher than that of the non-light-repaired structure. Full article

A fractional-order Lorenz system is optimized to maximize its maximum Lyapunov exponent and Kaplan-York dimension using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. The fractional-order Lorenz system is integrated with a recent process called the “modified two-stage Runge-Kutta” (M2sFRK) method, which is very fast and efficient. A Pseudo-Random Number Generator (PRNG) was built using one of the optimized systems that was obtained. The M2sFRK method allows for obtaining a very fast optimization time and also designing a very efficient PRNG with linear complexity, $O\left(n\right)$. The designed PRNG generates 24 random bits at each iteration step, and the random sequences pass all the National Institute of Standards and Technology (NIST) and TestU01 statistical tests, making the PRNG suitable for cryptographic applications. The presented methodology could be extended to any other chaotic system. Full article

This paper investigates an improved permanent magnet synchronous planar motor (PMSPM) featuring a moving magnet configuration to enhance thrust density and positioning accuracy. A novel split Halbach permanent magnet (PM) array is introduced, and the optimization begins with adjusting the pole size ratio α, analyzing the flux density distribution, and calculating thrust using an electromagnetic force model. Results demonstrate that the optimized Halbach array reduces thrust fluctuations and improves the uniformity of the air gap magnetic field. Multi-objective optimization using the non-dominated sorting genetic algorithm-II (NSGA-II) fine-tunes auxiliary magnet width and magnetization angles, resulting in a segmented auxiliary permanent magnet structure that achieves a 9.1% improvement in thrust density over conventional designs. Additionally, the optimized Halbach array effectively reduces thrust fluctuations and improves the uniformity of the air gap magnetic field, addressing key challenges in planar motor design. Extensive simulations and experimental validation demonstrate the superior performance of the proposed structure in terms of thrust density and positioning precision. These enhancements make the PMSPM suitable for high-precision, high-dynamic industrial applications. A detailed comparison of motor parameters and thrust performance validates the effectiveness of the proposed structure. Full article

Electric vehicle technologies present promising solutions for achieving energy conservation and emission reduction goals. However, efficiently distributing power across hybrid energy storage systems (HESSs) remains a major challenge in enhancing overall system performance. To address this, this paper proposes an energy management strategy (EMS) based on stepwise rules optimized by Particle Swarm Optimization (PSO). The approach begins by applying a multi-objective optimization method, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to fine-tune the parameters of lithium-ion batteries and ultracapacitors for an optimal balance in system performance. Additionally, an innovative stepwise-based EMS has been designed using adaptive PSO. This strategy builds a real-time control mechanism by dynamically adjusting the power distribution gradient threshold, taking into account the compensation for the state of charge (SOC). Comparative analysis across three typical operating conditions—urban, suburban, and highway—demonstrates that the stepwise-rule optimized strategy reduces the energy consumption of the HESS by 3.19%, 7.9%, and 5.37%. Full article