Abstract
A major financial expense for any military is the acquisition, operation, and maintenance of vehicles such as ships [1] and aircraft [2]. For example, the U.S. Air Force estimates that the acquisition of the F-35 fighter aircraft will cost $156 million each [3], hence even slight improvements in fleet efficiency and/or effectiveness can save governments large amounts of money or, using the same budget, can buy better equipment. Such high costs have driven the development and application of optimization and simulation methodologies to problems of military fleet mix computation and analysis. The complexity of military fleet mix problems, due in large part to the uncertainty, multi-objectivity, and temporal criticality of military missions, has resulted in the increased use of computational intelligence (CI) methods for solving them.
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