一种新的蚁群算法及其在飞行器设计中的应用
New ant colony algorithm and its application on optimization design of flight vehicle
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摘要: 尝试将蚁群算法引入飞行器优化设计领域,为此建立了适用于高维、多目标、多约束优化问题的连续空间蚁群算法,并以高超声速飞行器气动布局的多目标优化设计为例进行了验证.优化设计结果与采用遗传算法得到的优化结果进行了对比,指出了蚁群算法的优点.该研究可为蚁群算法应用于复杂、高维的大规模飞行器设计问题提供参考.Abstract: Ant colony algorithm(ACA) is a new bionic optimization algorithm developed in recent years.With global and efficient characteristics,it has been applied in discontinuous space successfully.To introduce it to aircraft design field,a high dimensional,multi-objective and multi-restrained ACA for continuous space was built.In an example,it was applied to the multi-objective optimization design of aerodynamic configuration for hypersonic cruise vehicle(HCV).Through comparison with Pareto genetic algorithms(GA),ACA shows its advantage.Finally,from our research work,ACA has great reference values for complex,multidimensional and large-scale optimization problems in aircraft design field.
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