This paper proposes a combined optimization method for multi-period empty container repositioning and inventory control based on adaptiveparticleswarm optimization (APSO) algorithm, which addresses the limitations o...
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This paper proposes a combined optimization method for multi-period empty container repositioning and inventory control based on adaptiveparticleswarm optimization (APSO) algorithm, which addresses the limitations of existing research, such as decoupling empty container repositioning and inventory control optimization, and lacking multi-period dynamic collaboration mechanisms. Firstly, a joint optimization model integrating (s, S) inventory control strategy is constructed. By adopting the strategy, the selection of repositioning paths and inventory resource allocation are synergistically optimized to balance unit empty container rental costs, inventory costs, and repositioning costs. Secondly, we design an adaptiveparticleswarm optimization algorithm, introduce dynamic inertia weight and acceleration coefficient adjustment mechanisms, and design heuristic rules for empty container repositioning. In this way, we reduce unreasonable empty container mobilization through the setting of surplus, shortage, and balance ports of empty containers, which can narrow the search space and improve the algorithm’s global search ability and convergence efficiency in high-dimensional decision spaces. Numerical experiments show that the joint optimization model designed can reduce the total cost of empty container management for shipping companies and maintain the rental cost in a stable state. Sensitivity analysis reveals that the unit container rental cost and the maximum inventory capacity of the port have a significant impact on the total system cost, providing a new approach for shipping companies to reduce empty container management costs.
Aiming to address the challenges posed by multiple decision elements and a vast decision space in multi-spacecraft mission planning, a multi-spacecraft mission allocation planning algorithm based on potential games is...
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Aiming to address the challenges posed by multiple decision elements and a vast decision space in multi-spacecraft mission planning, a multi-spacecraft mission allocation planning algorithm based on potential games is proposed to enhance the efficiency of multi-spacecraft distributed mission planning. Additionally, an adaptive particle swarm algorithm is introduced for further improvement and optimization. First, a comprehensive mission planning model is developed based on mission matching degree, mission time constraints, and energy consumption constraints. In mapping the mission planning model to the Potential Game model, a distributed mission allocation algorithm is proposed. In addition, the adaptive particle swarm algorithm is introduced into the mission allocation algorithm to solve the problem of finding the global optimal solution. Finally, experimental examples show the feasibility of the model as well as algorithm selection is correct.
Energy Consumption (EC) in the process of mechanical manufacturing directly leads to environmental pollution and resource waste. However, the EC characteristics of machine tool processing are complex, and most energy-...
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Energy Consumption (EC) in the process of mechanical manufacturing directly leads to environmental pollution and resource waste. However, the EC characteristics of machine tool processing are complex, and most energy-saving optimization models require accurate material performance data and cutting force models. In response to the above issues, the study first analyzes the structural composition of the machining system, clarifies the main variable parameters for optimization, and then establishes a mathematical model with the determined optimization variables to describe the EC characteristics. Finally, the established optimization model is solved using the adaptive particle swarm algorithm to find the optimal combination of process parameters and achieve energy-saving optimization. The improved adaptiveparticleswarm intelligence algorithm tends to converge after more than 50 iterations. When taking low cost and low EC as the optimization goal, the cutting EC of the optimization solution is 3.49 x 10(5) J, the processing time is 42.68 s, and the processing cost is 46.71 points, and the processing cost and EC are between the single optimization goal of low cost and low EC. It is indicated that the proposed method provides a reasonable energy-saving optimization strategy for machining process parameters, and provides support for the implementation of energy-saving optimization of machining center process parameters.
When using robots to carry out grinding and polishing processing of industrial blades, due to factors such as non-zero approach speed and discontinuous dynamic characteristics, the robot grinding and polishing process...
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When using robots to carry out grinding and polishing processing of industrial blades, due to factors such as non-zero approach speed and discontinuous dynamic characteristics, the robot grinding and polishing processing has contact force impact and oscillation problems during the contact transition process, which seriously affects the quality of the blade surface processing, contour accuracy, and control system stability. In order to solve the problem of large impact of contact force in robot grinding and polishing, this paper proposes an optimization method for impact suppression in the transition state of robot grinding and polishing. Taking the robot abrasive belt grinding and polishing as the research object, the adaptive weighted particleswarmalgorithm is used to optimize the input shaper and realize the parameter self-tuning of the input shaping technology. The experimental results show that the stabilization time is shortened by about 86.5%, and the maximum overshoot of contact force is reduced by about 88.5% during contact transition. The method proposed in this paper can realize the smooth transition of the blade grinding and polishing process, does not need system modeling, effectively shorten the system stabilization time, reduce the maximum overshoot, and accelerate the system response speed, and it has strong stability and flexibility.
It is important to improve the performance indexes including the net power and system efficiency for polymer electrolyte membrane fuel cell (PEMFC), especially considering the parameter uncertainty. To this end, this ...
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It is important to improve the performance indexes including the net power and system efficiency for polymer electrolyte membrane fuel cell (PEMFC), especially considering the parameter uncertainty. To this end, this paper proposes a hierarchical multi-objective optimization framework including the off -line and online optimization. Firstly, a steadystate non -linear PEMFC system model is established as the base model. Secondly, an adaptive multi-objective particleswarm optimization (AMPSO) algorithm scheme is proposed in the off -line optimization procedure for the exact optimization of operation parameters. Meanwhile, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Finally, to decrease the influence caused by parameter uncertainty, the interval optimization method is proposed in the on -line optimization layer based on the results of AMPSO. The convergence condition of the proposed optimization scheme is verified by theory analysis. The proposed AMPSO is compared with different algorithms in the numerical simulation and hardware -inloop (HIL) experiments. Meanwhile, the performance of the proposed method is tested on four representative benchmark problems. These results demonstrate that the PEMFC system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the net power and system efficiency indexes, revealing the success of this hierarchical optimization approach in solving the accurate optimization and parameter uncertainty problems. By using statistical test methods, the proposed algorithm performs better hypervolume (HV) metric from the Pareto solution distribution.
The hub motor significantly increases the unsprung mass of electric in-wheel vehicles, which deteriorates the ride comfort and safety of vehicles and which can be effectively improved by optimizing the main suspension...
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The hub motor significantly increases the unsprung mass of electric in-wheel vehicles, which deteriorates the ride comfort and safety of vehicles and which can be effectively improved by optimizing the main suspension parameters of vehicles reasonably, so a multi-objective optimization method of main suspension parameters based on adaptive particle swarm algorithm is proposed and the dynamic model of a half in-wheel electric vehicle is established. Taking the stiffness coefficient of the suspension damping spring and damping coefficient of the damper as independent variables, the vertical acceleration of the body, the pitch acceleration and the vertical impact force of the hub motor as optimization variables, and the dynamic deflection of the suspension and the dynamic load of the wheel as constraint variables, the multi-objective optimization function is constructed, and the parameters are simulated and optimized under the compound pavement. The simulation results show that the vertical acceleration and pitch acceleration are reduced by 20.2% and 18.4% respectively, the vertical impact force of the front hub motor is reduced by 3.7%, and the ride comfort and safety are significantly improved.
To research the problem on the multi-objective reactive power optimization, to utilize the theory of multi-objective fuzzy optimization to change the multi-objective optimization into the single-objective optimization...
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ISBN:
(纸本)9781627485654
To research the problem on the multi-objective reactive power optimization, to utilize the theory of multi-objective fuzzy optimization to change the multi-objective optimization into the single-objective optimization and to adopt the fuzzy adaptive particle swarm algorithm to carry out solutions. Comprehensively considering the security and economical efficiency of the system, as well as the condition of the operation constraints, to propose a comprehensive and practical multi-objective reactive power optimization model. To consider the multi-objective reactive power optimization model of the voltage stability index can optimize the economic benefit and safety benefit of the system. Applying the theory of multi-objective fuzzy optimization combined with the adaptiveparticleswarm optimization algorithm to the problem of the multi-objective reactive power optimization could solve the problem of the different dimensional multi-objective optimization in a better way. After adopting the fuzzy adaptive particle swarm algorithm, the superiorities, such as achieving the global optimal solution, reducing the computational complexity, and improving the computational efficiency, are displayed. (c) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Society for Automobile, Power and Energy Engineering
To research the problem on the multi-objective reactive power optimization, to utilize the theory of multiobjective fuzzy optimization to change the multi-objective optimization into the single-objective optimization,...
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To research the problem on the multi-objective reactive power optimization, to utilize the theory of multiobjective fuzzy optimization to change the multi-objective optimization into the single-objective optimization, and to adopt the fuzzy adaptive particle swarm algorithm to carry out solutions. Comprehensively considering the security and economical efficiency of the system, as well as the condition of the operation constraints, to propose a comprehensive and practical multi-objective reactive power optimization model. To consider the multi-objective reactive power optimization model of the voltage stability index can optimize the economic benefit and safety benefit of the system. Applying the theory of multi-objective fuzzy optimization combined with the adaptiveparticleswarm optimization algorithm to the problem of the multi-objective reactive power optimization could solve the problem of the different dimensional multi-objective optimization in a better way. After adopting the fuzzy adaptive particle swarm algorithm, the superiorities, such as achieving the global optimal solution, reducing the computational complexity, and improving the computational efficiency,are displayed.
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