Search Results (237)

Combined sewer overflow (CSO) pollution threatens urban water environments, yet optimizing integrated green–grey infrastructure solutions remains computationally intensive, often making robust, large-scale multi-algorithm comparisons impractical. This study’s primary contribution is the development of an innovative physics-guided optimization framework that overcomes this computational barrier. By coupling a deep learning surrogate (trained on 60,000 scenarios generated in 7.7 h) with evolutionary algorithms, this framework provides a 6.2- to 7.7-fold acceleration in total project time (approximately 13 h vs. 80–100 h) compared to direct SWMM optimization. This significant speedup enabled a comprehensive comparative analysis of four multi-objective evolutionary algorithms (MOEAs), which established NSGA-II’s superiority in discovering a larger and more diverse set of optimal trade-off solutions. The physics-guided surrogate achieved an R² of 0.9965 and a Mean Absolute Error (MAE) corresponding to 0.5% of the baseline overflow volume. The validated framework successfully identified Permeable Pavement as the dominant control variable and a critical knee-point scenario. This solution, requiring a 426 million CNY investment, achieved a 67.0% overflow volume reduction and a 74.4% COD load reduction under the 5-year design storm. Furthermore, the optimized system demonstrated high resilience to extreme events, contrasting sharply with the failure of a cost-minimized approach, which underscores the importance of designing for resilience. This framework provides urban planners with a validated, efficient, and reliable methodology for designing resilient, cost-effective CSO control systems. Full article

Laser engraving may be used in a variety of industries, from medicine to defense, and it has many uses that require high-quality precision production. However, in practice, operators have to adjust the laser settings manually, which can result in wasted material and poor color quality and even decrease productivity. Current optimization approaches mostly concentrate on single objectives, making it impossible to co-optimize engraving quality and production efficiency simultaneously. In this paper, an approach based on a multi-objective genetic algorithm, a combination of NSGA-II, SPEA2, and MOEA/D, is proposed to automatically establish the relationship between CMYK color attributes, which are extracted from images of engravings, and laser parameters (power, speed, and frequency). Anodized aluminum 6061 was laser-processed using an SPI 30W fiber laser. While the proposed framework is general, the experimental validation in this study was specifically constrained to this material. The results also indicate that MOEA/D converges in a short time and becomes relatively stable after 20 generations. NSGA-II results in solutions that are more diverse, and SPEA2 offers a good trade-off between the speed of convergence and solution size. This approach resulted in optimization in terms of both a decrease in material used and color matching between manual operations, with the average CMYK improvement being up to 28%. Our results indicate that multi-objective evolutionary optimization is feasible for the optimization of efficiency and quality in laser cutting. Full article

To address the uneven distribution of the Pareto front and insufficient convergence in multi-UAV task allocation, this paper proposes GrEAPSO, an improved algorithm that hybridizes Particle Swarm Optimization (PSO) with Genetic Algorithm (GA). GrEAPSO balances exploitation and exploration through grid partitioning, adopts a dual-encoding scheme coupled with crossover and mutation to enhance population diversity, and employs a grid-based environmental selection mechanism to improve the uniformity of the Pareto set. After initialization, the algorithm iteratively performs a PSO-based local search, genetic crossover and mutation, and grid-based environmental selection. The offspring and parent populations are then merged, and the archive set is updated accordingly. Across three military UAV task-allocation scenarios (small, medium, and large), GrEAPSO is benchmarked against MOPSO, NSGA-II/III, MOEA/D-DE, RVEA, IBEA, MOMVO, and MaOGOA. All experiments use a population size of 100. Its reference point is undominated and dominates some competitors, with median gains of 55.78% in hypervolume and 8.11% in spacing. Finally, the sensitive analysis further indicates that dividing the objective space into 15–20 grids offers the best trade-off between search breadth and solution distribution. Full article

The rapid use of renewables like wind power (WP) and photovoltaic (PV) power is essential for a sustainable energy future, yet their volatility poses a threat to grid stability. Electric vehicles (EVs) contribute to the solution by providing storage, while biomass energy (BE) ensures a reliable and sustainable power supply, solidifying its critical role in the stable operation and sustainable development of the power system. Therefore, a dynamic economic emission dispatch (DEED) model based on WP–PV–BE–EVs (DEED_WPBEV) is proposed. The DEED_WPBEV model is designed to simultaneously minimize operating costs and environmental emissions. The model formulation incorporates several practical constraints, such as those related to power balance, the travel needs of EV owners, and spinning reserve. To obtain a satisfactory dispatch solution, an adaptive improved multi-objective evolutionary algorithm based on decomposition with differential evolution (IMOEA/D-DE) is further proposed. In IMOEA/D-DE, the initialization of the population is achieved through an iterative chaotic map with infinite collapses, and the differential evolution mutation operator is adaptively adjusted. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified on the ten-units system. The experimental results show that the proposed model and algorithm can effectively mitigate renewable energy uncertainty, reduce system costs, and lessen environmental impact. Full article

Aiming at the challenges of high dimensionality in both design variables and optimization objectives, along with high computational resource consumption in the multi-disciplinary optimization of aerodynamic and stealth performance for an unmanned aerial vehicle (UAV) S-shaped inlet, this paper proposes a multi-objective optimization method that integrates design variable dimensionality reduction and a Co-Kriging multi-fidelity surrogate model. First, the S-shape inlet was defined by utilizing parametric modeling with a total of 11 design variables. Simulations were performed to obtain a subset of samples, and Sobol’ sensitivity analysis was applied to eliminate parameters with minor influence on performance, thereby achieving design variable dimensionality reduction. Subsequently, a Co-Kriging surrogate model was constructed. Based on the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm, multi-objective optimization was carried out with the total pressure recovery coefficient, total pressure distortion coefficient, and the average forward radar cross-section (RCS) as the optimization objectives, yielding a Pareto front solution set. Finally, three optimized inlets were selected from the Pareto front and compared with the original inlet to evaluate their aerodynamic and stealth performance. The results demonstrate that the proposed optimization method balances efficiency and accuracy effectively, significantly increasing the total pressure recovery coefficient while markedly reducing the total pressure distortion coefficient and RCS of the optimized inlet. Full article

In the era of big data and rapid information growth, investors encounter a complex financial environment characterized by extensive data, conflicting investment objectives, and markets that are unpredictable due to economic and policy fluctuations. Hence, asset selection is vital for both investors and researchers. Multi-objective optimization algorithms balance multiple objectives to find optimal solutions and are widely used in engineering, economics, etc. This paper proposes a multi-objective decomposition optimization algorithm integrated with differential grouping (DG-MOEA/D). Initially, the algorithm employs the recursive spectral clustering differential grouping (RDGSC) technique to identify dependencies among variables, grouping them to reduce interactions between the variables. It then uses MOEA/D-UTEA to optimize each group, with an external archive for storing and updating solutions. Experimental results on the DTLZ and LSMOP test functions show that the DG-MOEA/D algorithm greatly outperforms the other seven comparison algorithms. When used in real-world scenarios like stock and bond asset allocation, the algorithm continues to outperform other methods, demonstrating its significant advantages in practical applications. Full article

Rail vehicles, frequently utilized as a heavy-duty, high-speed means of transportation, have been observed to result in substantial casualties and economic losses in the event of accidents. Energy-absorbing structures are critical to achieving passive safety, effectively absorbing and dissipating energy. The present study utilizes numerical simulation to assess the performance of origami-inspired tree-shaped structures (OTSs) under diverse surface configurations. OTSs offer significant advantages in reducing IPCF without substantially compromising other performance metrics. This experimental approach is employed to validate the efficacy of a finite element model. A multi-criteria decision-making method integrates MOEA/D-DAE and TOPSIS. This integrated approach is employed to identify optimal structures. The validity of the method was established through a comparison of the predicted results with the outcomes of finite element analysis. The findings demonstrated a 31.2% reduction in IPCF, a 3.6% increase in SEA, and a 10.4% rise in ULC. The optimized IPCF is 4.9919 kN, SEA is 12.316 kJ/kg. The collective results indicate the efficacy of the method as a tool for analyzing and optimizing energy-absorbing structures. Full article

The calculation of Total Transfer Capability (TTC) for transmission corridors serves as the foundation for security region determination and electricity market transactions. However, existing TTC methods often neglect corridor correlations, leading to overly optimistic results. TTC computation involves complex stability verification and requires enumerating numerous renewable energy operation scenarios to establish security boundaries, exhibiting high non-convexity and nonlinearity that challenge gradient-based iterative algorithms in approaching global optima. Furthermore, practical power systems feature coupled corridor effects, transforming multi-corridor TTC into a complex Pareto frontier search problem. This paper proposes a MOEA/D-FRRMAB (Fitness–Rate–Reward Multi-Armed Bandit)-based method featuring: (1) a TTC model incorporating transient angle stability constraints, steady-state operational limits, and inter-corridor power interactions and (2) a decomposition strategy converting the multi-objective problem into subproblems, enhanced by MOEA/D-FRRMAB for improved Pareto front convergence and diversity. IEEE 39-bus tests demonstrate superior solution accuracy and diversity, providing dispatch centers with more reliable multi-corridor TTC strategies. Full article

The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most existing algorithms tend to emphasize one objective over the other, resulting in imbalanced solutions, limited Pareto fronts, or poor scalability on larger instances. To overcome these shortcomings, this study introduces a Hybrid MOCPO–AGE-MOEA algorithm that strategically combines the exploratory strength of Multi-Objective Crested Porcupines Optimization (MOCPO) with the exploitative refinement of the Adaptive Geometry-based Evolutionary Algorithm (AGE-MOEA), while a Kruskal-based repair operator is integrated to strictly enforce feasibility and preserve solution diversity. Moreover, through extensive experiments conducted on Euclidean graphs with 11–100 nodes, the hybrid consistently demonstrates superior performance compared with five state-of-the-art baselines, as it generates Pareto fronts up to four times larger, achieves nearly 20% reductions in hop counts, and delivers order-of-magnitude runtime improvements with near-linear scalability. Importantly, results reveal that allocating 85% of offspring to MOCPO exploration and 15% to AGE-MOEA exploitation yields the best balance between diversity, efficiency, and feasibility. Therefore, the Hybrid MOCPO–AGE-MOEA not only addresses critical gaps in constrained MST optimization but also establishes itself as a practical and scalable solution with strong applicability to domains such as software-defined networking, wireless mesh systems, and adaptive routing, where both computational efficiency and solution diversity are paramount Full article

Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment. Full article

To obtain propellant formulations with superior comprehensive and robustness performance, the study establishes a multi-objective optimization model that accounts for uncertainties. The model adopts a bi-layer structure. The inner layer computes performance bounds to construct uncertainty intervals, which are subsequently transformed into deterministic performance via interval order relations. The outer layer optimizes component mass fractions using MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) to maximize the deterministic performance. The study leverages Large Language Models (LLMs) as pre-trained optimizers to automate the operator design of MOEA/D. Designers can identify formulations that satisfy the performance requirements and robustness criteria by adjusting uncertainty levels and MOEA/D weight coefficients. The results on ZDTs and UFs demonstrate that MOEA/D-LLM achieves approximately a 4.0% improvement in hypervolume values compared to MOEA/D. Additionally, the NEPE propellant optimization case shows that MOEA/D-LLM improves the computational speed by about 13.05% and enhances hypervolume values by around 2.7% compared to MOEA/D. The specific impulse increases by 1.11%, the generation of aluminum oxide and hydrogen chloride decreases by approximately 18.43% and 16.40%, respectively, and the impact sensitivity is reduced by about 1.67%. Full article

Maintaining operational efficiency in distributed solar energy systems requires intelligent coordination of inspection tasks and workforce resources to handle diverse fault conditions. This study presents a bi-level multi-objective optimization framework that addresses two tightly coupled problems: dynamic job scheduling and skill-based team formation. The job scheduling component assigns geographically dispersed inspection tasks to mobile teams while minimizing multiple conflicting objectives, including travel distance, tardiness, and workload imbalance. Concurrently, the team formation component ensures that each team satisfies fault-specific skill requirements by optimizing team cohesion and compactness. To solve the bi-objective team formation problem, we propose HMOO-AOS, a hybrid algorithm integrating six metaheuristic operators under an NSGA-II framework with an Upper Confidence Bound-based Adaptive Operator Selection. Experiments on datasets of up to seven instances demonstrate statistically significant improvements ($p<0.05$) in solution quality, skill coverage, and computational efficiency compared to NSGA-II, NSGA-III, and MOEA/D variants, with computational complexity $\mathcal{O}\left(G·N·(M+logN)\right)$ (time complexity), $\mathcal{O}(N·L)$ (space complexity). A cloud-integrated system architecture is also proposed to contextualize the framework within real-world solar inspection operations, supporting real-time data integration, dynamic rescheduling, and mobile workforce coordination. These contributions provide scalable, practical tools for solar operators, maintenance planners, and energy system managers, establishing a robust and adaptive approach to intelligent inspection planning in renewable energy operations. Full article

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article

In the integrated hydro–wind–solar–storage system, the strong output fluctuations of wind and solar power, along with prominent system nonlinearity and time-varying characteristics, make it difficult for traditional PID controllers to achieve high-precision and robust dynamic control. This paper proposes a fuzzy fractional-order PID control strategy based on a multi-objective optimization algorithm, aiming to enhance the system’s frequency regulation, power balance, and disturbance rejection capabilities. The strategy combines the adaptive decision-making ability of fuzzy control with the high-degree-of-freedom tuning features of fractional-order PID. The multi-objective optimization algorithm AGE-MOEA-II is employed to jointly optimize five core parameters of the fuzzy fractional-order PID controller (K_p, K_i, K_d, λ, and μ), balancing multiple objectives such as system dynamic response speed, steady-state accuracy, suppression of wind–solar fluctuations, and hydropower regulation cost. Simulation results show that compared to traditional PID, single fractional-order PID, or fuzzy PID controllers, the proposed method significantly reduces system frequency deviation by 35.6%, decreases power overshoot by 42.1%, and improves renewable energy utilization by 17.3%. This provides an effective and adaptive solution for the stable operation of hydro–wind–solar–storage systems under uncertain and variable conditions. Full article

In this paper, the multi-objective optimization of the 12-pole radial active magnetic bearing (RAMB) is investigated. In the optimization of the RAMB, the decision-maker is more interested in the Pareto-optimal solutions in a certain region. This paper proposes a decomposition-based and preference-based multi-objective evolutionary algorithm (MOEA/D-Pref). The proposed MOEA/D-Pref not only allows the number of Pareto-optimal solutions to be more concentrated in the region of interest but also preserves solutions in other regions. These preserved solutions enable decision-makers to observe a more complete Pareto front, thus gaining more comprehensive insights. In this paper, a mathematical model of the 12-pole RAMB is established, and, with the help of this model and the proposed algorithm, the optimal design of the 12-pole RAMB is completed. The difference between the current stiffness coefficients of the optimized RAMB, calculated by the proposed algorithm and by the finite element method, is 2.3%. The difference between the displacement stiffness coefficient of the optimized RAMB as calculated by the proposed algorithm and by the finite element method is 3.9%. These differences, being less than 4%, are relatively low and verify the reliability of the mathematical model established. Full article