Multiobjective wing design using genetic algorithms and fuzzy logic

Abstract

The designer frequently faces problems in which the project depends on many parameters and the final solution must be evaluated according to several optimization objectives. This may be the case for the aeroelastic and aeromechanical design of lifting surfaces. The aim of the present paper is to show how the combined techniques of genetic algorithms (GAs) and fuzzy logic can be useful in these situations. The leading idea is the development of a tool to assist the designer in the preliminary phase of activity by assigning a genetic code to every possible solution and assessing its performance by means of a fuzzy controller in order to handle simultaneously different design criteria. Examples are given for some typical problems in aeronautical design.

Evolutionary optimization in ANFIS for intelligent navigation system

“…The random search process in evolutionary algorithms has proved its robustness in optimization even when the mathematical model of the system is very complicated or not well defined [9]. The goal of MA optimization technique is to determine an ANFIS in the form of equation (2), and minimize the objective function Where P is the number of outputs and d j (k) is the jth desired output at time k. Initialization of population has been discussed in the previous section.…”

“…Before discussing crossover and mutation the major difference between Genetic and Memetic optimization cycle has to be discussed. In GA after performing crossover and mutation the whole population is replaced by the new generation, but in a MA the new generation does not replace the entire population, only the best of existing and the new generation has got a chance to survive to the new population [9]. Thus the population is not replaced for every generation as in GA but it is updated for every generation [13,14].…”

Genetically optimized ANFIS based Intelligent Navigation System

“…The searching process is GA has proved its robustness in optimization even when the mathematical model of the system very complicated or not well defined [9].…”

Automatic Estimation of Inertial Navigation System Errors for Global Positioning System Outage Recovery

“…The searching process is comparable with the natural evolution of biological creatures in which successive generations of organisms are given birth and raised until they themselves are able to breed. GAs have revealed their robustness in the field of optimization, especially when the mathematical model of the optimization problem is quite complicated or not well defined [24], because it is very complicated to set a certain stochastic model for each IMU sensor. In addition, Fig.…”