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In this paper, a multiobjective optimization approach for obtaining the optimal distribution of surface-bonded piezoelectric sensors and actuators for noise attenuation in sandwich panels is presented. The noise attenuation is achieved by using negative velocity feedback control with co-located sensors and actuators. The control gains are also optimized in order to obtain the most efficient noise attenuation in a given frequency band. An in-house implementation of a viscoelastic soft core sandwich plate finite element, including surface-bonded piezoelectric sensors and actuators with active control capabilities, is used for obtaining the frequency response of the panels. The sound transmission capability of the panels is evaluated using the radiated sound power, along with the Rayleigh integral approach, which is suitable for lightly coupled structural/acoustic problems. The Direct MultiSearch (DMS) optimization algorithm is used to minimize the added weight due to the piezoelectric material, minimizing also the number of required controllers and maximizing the noise attenuation. The total length of the radiated sound power curve is shown to be an effective measure of noise attenuation in a given frequency band. Trade-off Pareto fronts and the obtained optimal configurations are presented and discussed. [ABSTRACT FROM AUTHOR]

The optimal design of laminated composite panels with constrained layer damping (CLD) treatments is addressed in this article. The objectives are simultaneously to minimize weight and maximize modal damping. The design variables are the number and position of CLD patch treatments on the surface of the laminated plate. The problem is solved using Direct MultiSearch solver, which does not use any derivatives of the objective functions. A finite element model for sandwich plates with viscoelastic core and laminated face layers is used. Trade-off Pareto optimal fronts and the respective treatment configurations are obtained and the results are analyzed and discussed. [ABSTRACT FROM AUTHOR]

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible front the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front. even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts. [ABSTRACT FROM AUTHOR]