The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.
A basic overview of optimization techniques is provided. The standard form of the general non-linear, constrained optimization problem is presented, and various techniques for solving the resulting optimization problem are discussed. The techniques are classified as either local (typically gradient-based) or global (typically nongradient based or evolutionary) algorithms. A great many optimization techniques exist and it is not possible to provide a complete review in the limited space available here. Instead, an effort is made to concentrate on techniques that are commonly used in engineering optimization applications. The review is kept general in nature, without considering special cases like linear programming, convex problems, multi-objective optimization, multidisciplinary optimization, etc. The advantages and disadvantages of the different techniques are highlighted, and suggestions are made to aid the designer in selecting an appropriate technique for a specific problem at hand. Where possible, a short overview of a representative method is presented to aid the discussion of that particular class of algorithms.
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