Search Results (52)

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article

Multimodal multiobjective optimization problems, characterized by multiple solutions mapping to identical objective vectors, are ubiquitous in real-world applications. Despite their prevalence, most existing multimodal multiobjective evolutionary algorithms (MMOEAs) predominantly focus on identifying global Pareto sets, often overlooking the equally significant local Pareto sets. While some algorithms attempt to address local Pareto sets, their performance in the objective space remains suboptimal. The inherent challenge lies in the fact that a single strategy cannot effectively tackle problems with and without local Pareto fronts. This study proposes a novel approach that first detects the presence of local Pareto fronts using a neural network, thereby enabling adaptive adjustments to the algorithm’s selection strategy and search scope. Based on this detection mechanism, we design a microscale searching multimodal multiobjective evolutionary algorithm (MMOEA_MS). Through extensive experiments on twenty-two benchmark problems, MMOEA_MS demonstrates superior performance in identifying local Pareto fronts and outperforms existing algorithms in the objective space. This study highlights the effectiveness of MMOEA_MS in solving multimodal multiobjective optimization problems with diverse Pareto front characteristics, thereby addressing key limitations of current methodologies. Full article

This paper presents an improved NSGA-II algorithm for the multi-objective optimization of irregular polygon patch antennas (IPPAs), improving convergence efficiency and Pareto front quality. The algorithm integrates adaptive mechanisms that dynamically adjust crossover and mutation rates based on generational progression, accelerating convergence while preserving solution diversity. Furthermore, a simulated annealing-inspired acceptance criterion is embedded during offspring generation to mitigate local optima trapping and enhance evolutionary robustness. A dual-objective formulation simultaneously minimizes antenna volume and maximizes operational bandwidth within the X-band. Optimization is executed via HFSS co-simulation, with detailed electromagnetic models ensuring physical realizability and design fidelity. The optimized antenna achieves a compact volume of 2807.6 mm³ and an operational bandwidth of 2.7 GHz. Experimental validation of fabricated prototypes demonstrates agreement with simulations, confirming the accuracy and reliability of the proposed method. These results demonstrate the effectiveness of the improved NSGA-II algorithm in addressing complex multi-objective design challenges and underscore its potential in advanced broadband antenna applications. Full article

Local Climate Zone (LCZ) classification is essential for urban microclimate modeling and heat mitigation planning. Traditional methods relying on manual sampling face limitations in scalability, objectivity, and handling spatial heterogeneity. This study presents an automated framework for LCZ sample generation, facilitating efficient large-scale LCZ mapping and LCZ-based urban climate analysis and geospatial applications. To this aim, it proposes a dual-path automated framework integrating GIS-driven sample generation to enhance LCZ classification accuracy: a multi-parameter Synergistic Optimization approach for urban LCZs and a Distance-driven Maximum Coverage method for natural LCZs. Specifically, urban samples are selected via multi-objective optimization and Pareto front screening for quality and representativeness, while the selection of natural samples prioritizes spatial coverage and diversity. Combining urban morphological parameters with Sentinel-2 imagery and a Random Forest classifier yielded a final accuracy of 0.95 in our test site, confirming the framework’s effectiveness. Full article

This research describes a multi-objective trajectory planning method for robotic arms based on time, energy, and impact. The quintic Non-Uniform Rational B-Spline (NURBS) curve was employed to interpolate the trajectory in joint space. The quintic NURBS interpolation curve can make the trajectory become constrained within the kinematic limits of velocity, acceleration, and jerk while also satisfying the continuity of jerk. Then, based on the Parrot Optimization (PO) algorithm, through improvements to reduce algorithmic randomness and the introduction of appropriate multi-objective strategies, the algorithm was extended to the Multi-Objective Parrot Optimization (MOPO) algorithm, which better balances global search and local convergence, thereby more effectively solving multi-objective optimization problems and reducing the impact on optimization results. Subsequently, by integrating interpolation curves, the multi-objective optimization of joint trajectories could be performed under robotic kinematic constraints based on time–energy-jerk criteria. The obtained Pareto optimal front can provide decision-makers in industrial robotic arm applications with flexible options among non-dominated solutions. Full article

Multi-objective optimization problems often face challenges in balancing solution accuracy, computational efficiency, and convergence speed. Many existing methods struggle with achieving an optimal trade-off between exploration and exploitation, leading to premature convergence or excessive computational costs. To address these issues, this paper proposes a chaotic decomposition-based approach that leverages the ergodic properties of chaotic maps to enhance optimization performance. The proposed method consists of three key stages: (1) chaotic sequence initialization, which generates a diverse population to enhance the global search while reducing computational costs; (2) chaos-based correction, which integrates a three-point operator (TPO) and a local improvement operator (LIO) to refine the Pareto front and balance the exploration–exploitation trade-offs; and (3) Tchebycheff decomposition-based updating, ensuring efficient convergence toward optimal solutions. To validate the effectiveness of the proposed method, we conducted extensive experiments on a suite of benchmark problems and compared its performance with several state-of-the-art methods. The evaluation metrics, including inverted generational distance (IGD), generational distance (GD), and spacing (SP), demonstrated that the proposed method achieves competitive optimization accuracy and efficiency. While maintaining computational feasibility, our approach provides a well-balanced trade-off between exploration and exploitation, leading to improved solution diversity and convergence stability. The results establish the proposed algorithm as a promising alternative for solving multi-objective optimization problems. Full article

With the advent of Industry 4.0, the seamless integration of industrial systems and unmanned technologies has significantly accelerated the development of smart industries. However, the research on task allocation for railway maintenance robots remains limited, particularly with respect to optimizing costs and efficiency within smart railway systems. To address this gap, the present study explores multi-robot task allocation for automated orbital bolt maintenance, aiming to enhance operational efficiency by minimizing both makespan and total travel distance for all robots. To achieve this, an improved hybrid algorithm combining NSGA-II and MOPSO is proposed. Initially, a dynamic task planning method, tailored to the specific conditions of railway operations, is developed. This method uses the coordinates of track bolts to extract environmental features, enabling the dynamic partitioning of task areas. Subsequently, a multi-elite archive strategy is introduced, along with an adaptive mechanism for adjusting crossover and mutation probabilities. This ensures the preservation and maintenance of multiple solutions across various Pareto fronts, effectively mitigating the premature convergence commonly observed in traditional NSGA-II algorithms. Moreover, the integration of the MOPSO algorithm strikes a balance between local and global search capabilities, thereby enhancing both optimization efficiency and solution quality. Finally, a series of experiments, conducted with varying task sizes and robot quantities during the railway maintenance window, validate the effectiveness and improved performance of the proposed algorithm in addressing the multi-robot task allocation problem. Full article

A multi-objective grasshopper optimization algorithm (MOGOA) with an adaptive curve c(t) and the enhanced Levy fight strategy (CLMOGOA) was proposed to optimize the process parameters of rotary screen coating, setting the thickness and uniformity of the adhesive layer on the structural plates in spacecraft as its optimization objectives. The adaptive curve strikes a balance between global exploration and local development and accelerates the convergence speed. The enhanced Levy strategy helps the algorithm to escape local optimizations, increases the population diversity, and possesses dual searching capabilities. After multiple runs, the average values of the CLMOGOA’s reverse generation distance were 0.0288, 0.0233, and 0.1810 on the test sets, which were less than those of the MOGOA. The best Pareto-optimal front obtained by the CLMOGOA had a higher accuracy and better coverage compared to that of the MOGOA. Thus, it is indicated that the CLMOGOA managed to outperform the MOGOA on the test functions. In order to solve the optimization problem, 108 sets of process experiments were designed, and then the experimental data were used to train a Back Propagation Neural Network (BPNN), a Least Squares Support Vector Machine (LSSVM), and Random Forest (RF) to obtain the best prediction model for the process parameters. Considering the thickness and uniformity of the adhesive layer as the objectives, the improved algorithm was used to optimize the prediction model to obtain the optimal process parameters. The actual coating effect showed that the optimization algorithm improved the efficiency and qualification rate of the product. Full article

Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO₂ emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO₂ emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 “all sectors and EC incentive” for the year 2024, a large reduction of 55% of CO₂ emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front. Full article

The paper proposes an innovative adaptation of the estimation of distribution algorithm (EDA), intended for multi-objective optimisation of a ship’s route in a non-stationary environment (tidal waters). The key elements of the proposed approach—the adaptive Markov chain-based path generator and the dynamic programming-based local search algorithm—are presented in detail. The experimental results presented indicate the high effectiveness of the proposed algorithm in finding very good quality approximations of optimal solutions in the Pareto sense. Critical for this was the proposed local search algorithm, whose application improved the final result significantly (the Pareto set size increased from five up to nine times, and the Pareto front quality just about doubled). The proposed algorithm can also be applied to other domains (e.g., mobile robot path planning). It can be considered a framework for (simulation-based) multi-objective optimal path planning in non-stationary environments. Full article

Stochastic optimization approaches benefit from random variance to produce a solution in a reasonable time frame that is good enough for solving the problem. Compared with them, deterministic optimization methods feature faster convergence rates and better reproducibility but may get stuck at a local optimum that is insufficient to solve the problem. In this paper, we propose a group-based deterministic optimization method, which can efficiently achieve comparable performance to heuristic optimization algorithms, such as particle swarm optimization. Moreover, the weighted sum method (WSM) is employed to further improve our deterministic optimization method to be multi-objective optimization, making it able to seek a balance among multiple conflicting circuit performance metrics. With a case study of three common analog circuits tested for our optimization methodology, the experimental results demonstrate that our proposed method can more efficiently reach a better estimation of the Pareto front compared to NSGA-II, a well-known multi-objective optimization approach. Full article

Multi-objective evolutionary algorithms (MOEA) have attracted much attention because of their good global exploration ability; however, their local search ability near the optimal value is weak, and for large-scale decision-variable optimization problems the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient optimization algorithms can overcome these difficulties well, but gradient search methods are difficult to apply to multi-objective optimization problems (MOPs). To this end, this paper introduces a stochastic weighting function based on the weighted average gradient and proposes two multi-objective stochastic gradient operators. Further, two efficient evolutionary algorithms, MOGBA and HMOEA, are developed. Their local search capability has been greatly enhanced while retaining the good global exploration capability by using different offspring update strategies for different subpopulations. Numerical experiments show that HMOEA has excellent capture ability for various Pareto formations, and it can easily solve multi-objective optimization problems with many objectives, which improves the efficiency by a factor of 5–10 compared with typical multi-objective evolutionary algorithms. HMOEA is further applied to the multi-objective aerodynamic optimization design of the RAE2822 airfoil and the ideal Pareto front is obtained, which indicates that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design. Full article

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies. Full article

Home health care companies provide health care services to patients in their homes. Due to increasing demand, the provision of home health care services requires effective management of operational costs while satisfying both patients and caregivers. In practice, uncertain service times might lead to considerable delays that adversely affect service quality. To this end, this paper proposes a new bi-objective optimization problem to model the routing and scheduling problems under uncertainty in home health care, considering the qualification and workload of caregivers. A mixed-integer linear programming formulation is developed. Motivated by the challenge of computational time, we propose the Adaptive Large Neighborhood Search embedded in an Enhanced Multi-Directional Local Search framework (ALNS-EMDLS). A stochastic ALNS-EMDLS is introduced to handle uncertain service times for patients. Three kinds of metrics for evaluating the Pareto fronts highlight the efficiency of our proposed method. The sensitivity analysis validates the robustness of the proposed model and method. Finally, we apply the method to a real-life case and provide managerial recommendations. Full article