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• This paper proposed a novel wind speed forecasting algorithm. • A new dataset division mechanism based on the moving window is used in this paper. • In order to improve the accuracy and stability simultaneously, the multi-objective particle swarm optimization is applied. • We compared the proposed MOPSO-Combined method with the combined model that published before in the paper. Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to describe refinements to the recently developed GA-ACO method and to show its application to a real world engineering design problem. The GA-ACO method is a genetic algorithm with a new operator, called an ACO operator. ACO stands for Ant Colony Optimization. The ACO operator uses pheromone trails, a method from Ant Colony Optimization to influence the genetic algorithm. The GA-ACO method is used to optimize an engineering design. Engineers produce a preliminary design for a system using a CAD tool. The output of the CAD tool is then translated into a design graph. Many additional characteristics of the design can be represented by labels on the design graph. The GA-ACO method is then used to optimize these labels. This technique can be applied widely to many design optimization problems. The application considered in this paper concerns optimization of designs for efficient assembly. It uses problems in engineering design encountered at Newport News Shipbuilding, the largest shipyard in the United States. We present a comparison of variations of the GA-ACO method with a standard genetic algorithm for this type of problem. [Copyright &y& Elsevier]

Abstract: This paper presents an optimal control strategy suitable for investigating the mission performance of a spacecraft equipped with a NASAʼs Evolutionary Xenon Thruster. Using an accurate mathematical model of the propulsion system, based on both experimental data and a semi-empirical schematization of the power generation system, the minimum-time rendezvous problem with various celestial bodies is investigated through an indirect approach. Unlike a conventional approach, in which the thruster power is varied with continuity within a given range, here the solar electric propulsion system is described using a finite number of operation points, characterized by different pairs of thruster input power and propellant mass flow rate. The model includes the possibility of obtaining coasting phases along the spacecraft trajectory. The optimal control strategy is found for a three-dimensional problem in which the actual shape and relative orientation of the initial and final orbits are taken into account. The paper presents the simulation results for a number of minimum-time trajectories towards inner planets (Mercury, Venus, and Mars) and three Near Earth Asteroids (NEAs) of particular interest for the scientific community: 1998 KY-26, 99942 Apophis, and 2007 VK-184. [Copyright &y& Elsevier]

Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a solution to address one of the requirements identified in the US Northwest 6th Power Plan, specifically, meeting an increase in the forecasted Pacific Northwest demand with renewable generation. A model is presented which evaluates one source of renewable generation, off-shore wind energy. The model analyzes different wind turbine design characteristics, assigns significance to potential synergistic effects of one design feature on another, and provides an optimized design relative to the cost, environment, robustness, and overall efficiency of an off-shore wind turbine. [Copyright &y& Elsevier]

Abstract: Infrastructure systems in the US are in urgent need of maintenance and rehabilitation. According to the most recent factsheet published by the American Society of Civil Engineers, one of the top five infrastructure concerns of today in the US is the transportation system. The major challenge facing maintenance managers in state Departments of Transportation (DOTs) today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives. Absence of simpler decision-making tools exacerbates the matter. This paper presents the development and implementation of a simpler, yet useful, network-level pavement maintenance optimization model, which is a Linear Program (LP) subject to budget constraints and the agencies’ pavement performance goals in terms of total lane-miles in each pavement condition state. A decision-making tool is developed using Frontline Systems’ Risk Solver Platform add-in for Microsoft Office Excel. This decision-making tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Pavement condition data pertaining to one of the Districts within a state DOT is used to test the model presented herein. Within this context, nine treatment types along with their corresponding unit prices ($/Lane-Mile), five pavement condition states, pavement deterioration rates, network-level pavement performance targets, and available annual maintenance budget for a 15-year planning horizon are defined. The results presented show how an annual highway maintenance budget needs to be allocated or determined to achieve the District’s value proposition for various scenarios. Comparing the results of these varying scenarios provides insight on long-term strategies and the impact of target constraints on budget expenditures. [Copyright &y& Elsevier]

Abstract: In response to the scientific interest in Jupiter''s Galilean moons, NASA and ESA have plans to send orbiting missions to Europa and Ganymede, respectively. The inter-moon transfers of the Jovian system offer obvious advantages in terms of scientific return, but are also challenging to design and optimize due in part to the large, often chaotic, sensitivities associated with repeated close encounters of the planetary moons. The approach outlined in this paper confronts this shortcoming by exploiting the multi-body dynamics with a patched three-body model to enable multiple “resonant-hopping” gravity assists. Initial conditions of unstable resonant orbits are pre-computed and provide starting points for the elusive initial guess associated with the highly nonlinear optimization problem. The core of the optimization algorithm relies on a fast and robust multiple-shooting technique to provide better controllability and reduce the sensitivities associated with the close approach trajectories. The complexity of the optimization problem is also reduced with the help of the Tisserand–Poincaré (T–P) graph that provides a simple way to target trajectories in the patched three-body problem. Preliminary numerical results of inter-moon transfers in the Jovian system are presented. For example, using only 59m/s and 158 days, a spacecraft can transfer between a close resonant orbit of Ganymede and a close resonant orbit of Europa. [Copyright &y& Elsevier]

Coordinating the distribution of ammunition and scheduling strategic transportation resources during military contingency operations is a complex process. This paper presents a large-scale optimization-based planning method that uses column generation to schedule the movement of ammunition and transportation resources through a time-space network representation of the distribution system. The optimization-based planner is initialized using a feasible solution generated by a heuristic planning method. Both the optimization-based planner and the heuristic planner generate plans with improved ship utilization and delivery tardiness values as compared to plans generated using current planning techniques. In addition, the heuristic planner is implemented within a closed-loop planning and control framework, and is used to generate plans on a rolling horizon basis. [Copyright &y& Elsevier]