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multi-objective particle swarm optimization

در نشریات گروه مواد و متالورژی
تکرار جستجوی کلیدواژه multi-objective particle swarm optimization در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه multi-objective particle swarm optimization در مقالات مجلات علمی
  • A. Foorginejad *, K. Khalili, V. Abdollahi, H. Afshari
    Scaffold geometry plays a crucial role in determining its mechanical strength, as changes in shape can significantly impact its properties. Additionally, porosity, which varies with geometry, weakens the scaffold's mechanical performance. This study investigates the influence of scaffold geometry on mechanical properties and porosity in biomedical applications. Seven distinct geometries were designed using identical materials and fabricated through 3D printing. The scaffolds underwent compressive strength testing and finite element simulations to evaluate their load-bearing capacity and porosity. Among the designs, hexagonal and circular geometries demonstrated superior mechanical performance and controlled porosity. A total of 81 hexagonal and 27 circular scaffold samples were analyzed using Abaqus software. Initially, Response Surface Methodology (RSM) was employed to model the relationship between pressure and porosity, identifying the optimal design space with high predictive accuracy (R² > 96%). Then, a multi-objective optimization process using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was implemented. The results revealed a Pareto front for each geometry, enabling the selection of scaffolds with specific load-bearing capacities and maximum porosity levels. Validation tests showed a mean error of 3.4% for circular geometries and 3.53% for hexagonal geometries, demonstrating the reliability of the simulation and optimization methods. This comprehensive approach integrates experimental, simulation, and optimization techniques, offering a robust framework for designing high-performance scaffolds tailored to biomedical needs.
    Keywords: Scaffold, Multi Objective Particle Swarm Optimization, Additive Manufacturing, Finite Element, Bio Printing
  • R. Ebrahimi Gouraji, H. Soleimani *, B. Afshar Najafi
    In today's competitive market, manufacturers and service providers are continuously seeking ways to reduce costs and save time to gain a competitive edge. One of the most significant challenges they face is the vehicle routing problem (VRP), which is crucial due to its direct impact on the delivery time of services or products. Efficient vehicle routing not only enhances delivery performance but also optimizes the overall network, resulting in reduced operational costs. This study focuses on evaluating the VRP specifically for trucks while incorporating sustainability indicators into the analysis. The key sustainability indicators considered include social, economic, and environmental aspects. By integrating these indicators, the study aims to address multiple objectives simultaneously: reducing delivery time, minimizing costs, and mitigating the environmental impact of vehicle operations.The primary objective of this research is to minimize overall costs, fuel consumption, and route complexity associated with truck deliveries. Given the growing concern over environmental issues, there is a strong emphasis on improving methods to reduce greenhouse gas (GHG) emissions and streamline logistics processes. The research addresses these concerns by proposing a model that not only aims to enhance operational efficiency but also contributes to environmental protection and social responsibility.To achieve these objectives, the study employs advanced optimization techniques, specifically the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). These methods are utilized to solve the VRP while balancing the trade-offs between various objectives, such as cost reduction, fuel efficiency, and route optimization.The results of the study indicate that the proposed model successfully improves aspects of environmental protection and social responsibility while simultaneously addressing economic concerns. The integration of sustainability indicators into the vehicle routing problem provides a comprehensive approach to optimizing logistics operations, highlighting the importance of considering environmental and social factors alongside economic performance.Overall, this research contributes to the field by offering a refined model for tackling the VRP, with a focus on sustainability. The findings underscore the potential for optimization algorithms to drive improvements in both operational efficiency and environmental stewardship, ultimately supporting more sustainable and socially responsible practices in the transportation and logistics industry.
    Keywords: Exchange Locations, Vehicle Routing Problem, Algorithms, Non-Dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Metaheuristic, Time Constraint
  • M. Shafiee, M. Amirahmadi *, M. Farzinfar, A. Lapthorn
    Installation of Shunt Capacitor Banks (SCBs) and Voltage Regulators (VRs) within distribution system is one of the most effective solutions in reactive power control for improving the voltage profile and reducing power losses along the feeder. However, the presence of the VRs can deteriorate the Voltage Stability Margin (VSM) in distribution feeders. To address this issue, this paper proposes a multi-objective programming model for the simultaneous optimal allocation of VRs and SCBs in the distribution network to improve the voltage profile and to minimize power losses and installation costs. In the proposed model, a Voltage Stability Index (VSI) is considered to prevent voltage instability during SCBs/VRs allocation. A new Modified Multi-Objective Particle Swarm Optimization (MMOPSO) algorithm which includes a dynamic inertia weight and mutation operator is proposed to obtain the optimal solutions as a Pareto set. Thereinafter, a Fuzzy Satisfaction Method (FSM) determines the optimal solution. A practical long radial distribution feeder has been employed to demonstrate the efficiency and efficacy of the proposed model along with a comparison between the proposed MMOPSO and the original MOPSO.
    Keywords: Allocation, Capacitor bank, Multi-objective Particle swarm optimization, Voltage Regulator, Voltage Stability Enhancement
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