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In this work we consider shape optimization of systems, which are governed by external Bernoulli free boundary problems. A pseudo-solid approach for solving discrete free boundary problems is introduced. The solution strategy readily allows us to obtain geometrical sensitivities of the system, which can then be used to solve e.g. inverse design problems. Numerical examples show that the location of the free boundary can, to some extent, be controlled by changing the shape of the other component of the boundary.

This paper proposes a novel Memetic Algorithm consisting of an Adaptive Evolutionary Algorithm (AEA) with three Intelligent Mutation Local Searchers (IMLSs) for designing optimal multidrug Structured Treatment Interruption (STI) therapies for Human Immunodeficiency Virus (HIV) infection. The AEA is an evolutionary algorithm with a dynamic parameter setting. The adaptive use of the local searchers helps the evolutionary process in the search of a global optimum. The adaptive rule is based on a phenotypical diversity measure of the population. The proposed algorithm has been tested for determining optimal 750-day pharmacological protocols for HIV patients. The pathogenesis of HIV is described by a system of differential equations including a model for an immune response. The multidrug therapies use reverse transcriptase inhibitor and protease inhibitor anti-HIV drugs. The medical protocol designed by the proposed algorithm leads to a strong immune response; the patient reaches a "healthy" state in one and half years and after this the STI medications can be discontinued. A comparison with a specific heuristic method and a standard Genetic Algorithm (GA) has been performed. Unlike the heuristic, the AEA with IMLSs does not impose any restrictions on the therapies in order to reduce the dimension of the problem. Unlike the GA, the AEA with IMLSs can overcome the problem of premature convergence to a suboptimal medical treatment. The results show that the therapies designed by the AEA lead to a "healthy" state faster than with the other methods. The statistical analysis confirms the computational effectiveness of the algorithm.

A multiobjective multidisciplinary design optimization (MDO) of two-dimensional airfoil designs is presented. In this paper an approximation for the Pareto set of optimal solutions is obtained by using a genetic algorithm (GA). The first objective function is the drag coefficient. As a constraint, it is required that the lift coefficient is above a given value. The CFD analysis solver is based on finite volume discretisation of inviscid Euler equations. The second objective function is equivalent to the integral of the transverse magnetic radar cross section (RCS) over a given sector. The computational electromagnetics (CEM) wave field analysis requires the solution of a two-dimensional Helmholtz equation using a fictitious domain method. Numerical experiments illustrate the above evolutionary methodology on a IBM SP2 parallel computer.

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