Modern engineering requires finding an optimal trade-off point among conflicting requirements that must be satisfied to reach the end user's satisfaction. One of the most challenging problems in multidisciplinary optimization is to find a fitness function that can best translate what the designer really aims to obtain. In this paper, a particle swarm optimization (PSO) algorithm is coupled to a fitness function based on the definition of a technique for order preference by similarity to ideal solution (TOPSIS) in order to obtain a new algorithm called PSOTOP. The fitness of a candidate solution is found by comparing its attributes to those of an “ideal best” and “ideal worst” solution, which is dynamically updated at each iteration of the algorithm. The advantage of this solution is that the fitness allows combining attributes of different magnitude and measurement units in an effective way; this approach can be applied to whatsoever optimization problem in engineering, economics, medicine, and statistics. This paper presents a case study dealing with the optimal design of an airfoil to be adopted on the wing of an unmanned air vehicle to support civil protection operations in order to show how this strategy can impact the design of a complex product. The main limitation of this approach relates to the fact that the user must possess a good knowledge of the specific problem to be solved in order to set proper ranges for design parameters and attributes' weight in the fitness evaluation.

Engineering optimization based on dynamic technique for order preference by similarity to ideal solution fitness: Application to unmanned aerial vehicle wing airfoil geometry definition

Ceruti, Alessandro
Methodology
;
FIORINI, TOMMASO
Software
;
BOGGI, STEFANO
Data Curation
;
2018

Abstract

Modern engineering requires finding an optimal trade-off point among conflicting requirements that must be satisfied to reach the end user's satisfaction. One of the most challenging problems in multidisciplinary optimization is to find a fitness function that can best translate what the designer really aims to obtain. In this paper, a particle swarm optimization (PSO) algorithm is coupled to a fitness function based on the definition of a technique for order preference by similarity to ideal solution (TOPSIS) in order to obtain a new algorithm called PSOTOP. The fitness of a candidate solution is found by comparing its attributes to those of an “ideal best” and “ideal worst” solution, which is dynamically updated at each iteration of the algorithm. The advantage of this solution is that the fitness allows combining attributes of different magnitude and measurement units in an effective way; this approach can be applied to whatsoever optimization problem in engineering, economics, medicine, and statistics. This paper presents a case study dealing with the optimal design of an airfoil to be adopted on the wing of an unmanned air vehicle to support civil protection operations in order to show how this strategy can impact the design of a complex product. The main limitation of this approach relates to the fact that the user must possess a good knowledge of the specific problem to be solved in order to set proper ranges for design parameters and attributes' weight in the fitness evaluation.
Ceruti, Alessandro*; Fiorini, Tommaso; Boggi, Stefano; Mischi, Lorenzo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/644723
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