Optimum design of the laminated composite rectangular plate with specified frequencies is attempted in the presence of elliptical cutouts. Orientation of the ellipse with respect to the reference axis, aspect ratio of the cutout, orientation of plies, thickness of plies and material of the plies are used as design parameters with constraints on natural frequencies. As a first step, investigations of free vibration response of composite rectangular plate in the presence of elliptical cutout is studied and some typical results are presented. First-order shear deformation theory (FSDT) is used to account for the transverse shear stresses. Nine noded quadrilateral isoparametric element is used in the finite element formation. Genetic algorithm (GA) is employed to identify the optimum variables. Numerical results are presented for rectangular plates with simply supported edge conditions. The results show that GA is a viable tool for optimum design of laminated composite plates with cutouts.
Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems. In fact, many real-world multi-objective problems contain a number of constraints. To promote research on constrained multi-objective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multi-objective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness and diversity-hardness. We then develop a general toolkit to construct difficulty-adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. Based on this toolkit, we suggest nine difficulty-adjustable and scalable CMOPs and nine CMaOPs. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving convergence-hard DAS-CMOPs, while mechanisms of NSGA-II-CDP may be more effective in solving DAS-CMOPs with simultaneous diversity-, feasibility- and convergence-hardness. Mechanisms in C-NSGA-III may be more effective in solving feasibility-hard CMaOPs, while mechanisms of C-MOEA/DD may be more effective in solving CMaOPs with convergence-hardness. In addition, none of them can solve these problems efficiently, which stimulates us to continue to develop new CMOEAs and CMaOEAs to solve the suggested DAS-CMOPs and DAS-CMaOPs.
Article Share on Evolutionary practical optimization Author: Kalyanmoy Deb Indian Institute of Technology Kanpur, Kanpur, India Indian Institute of Technology Kanpur, Kanpur, IndiaView Profile Authors Info & Claims GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computationJuly 2007 Pages 3093–3132https://doi.org/10.1145/1274000.1274107Online:07 July 2007Publication History 0citation598DownloadsMetricsTotal Citations0Total Downloads598Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The seminar “Hybrid and Robust Approaches to Multiobjective Optimization” was a sequel to two previous Dagstuhl seminars (04461 in 2004 and 06501 in 2006). The main idea of this seminar series has been to bring together two contemporary fields related to multiobjective optimization – Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) – to discuss critical research and application issues for bringing the entire field further and for fostering future collaboration.
This particular seminar was participated by 53 researchers actively working in multiobjective optimization. The purpose of the seminar was to discuss two fundamental research topics related to multiobjective optimization: interactive methods requiring optimization and decision making aspects to be integrated for a practical implementation and robust multiobjective methodologies dealing with uncertainties in problem parameters, objectives, constraints and algorithms. The seminar was structured to have more emphasis on working group discussions, rather than individual presentations, so that the open and free environment and facilities of Schloss Dagstuhl could be fully utilized.
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