6,480 research outputs found

    Optimizing Engagement Simulations Through the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) Software

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    The ability to effectively model and simulate military missions holds the potential to save lives, money, and resources for the United States. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) software is a tool used to rapidly simulate and model new technologies and mission level scenarios. In this thesis, our objective is to integrate a closed loop optimization routine with AFSIM to identify an effective objective function to assess optimal inputs for engagement scenarios. Given the many factors which impact a mission level engagement, we developed a tool which interfaces with AFSIM to observe the effects from multiple inputs in an engagement scenario. Our tool operates under the assumption that simulation results have met an acceptable convergence threshold. The objective function evaluates the effectiveness and associated cost with a scenario using a genetic algorithm and a particle swarm optimization algorithm. From this, a statistical analysis was performed to assess risk from the distribution of effectiveness and cost at each point. The method allows an optimal set of inputs to be selected for a desired result from the selected engagement scenario.No embargoAcademic Major: Mechanical Engineerin

    Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

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    A particle swarm optimization based memetic algorithm for dynamic optimization problems

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    Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60

    State-of-the-art in aerodynamic shape optimisation methods

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    Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
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