9,504 research outputs found
Particle swarm optimization with sequential niche technique for dynamic finite element model updating
Peer reviewedPostprin
A comparison of two global optimization algorithms with sequential niche technique for structural model updating
Peer reviewedPostprin
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models
A new adaptive hybrid optimization strategy, entitled squads, is proposed for
complex inverse analysis of computationally intensive physical models. The new
strategy is designed to be computationally efficient and robust in
identification of the global optimum (e.g. maximum or minimum value of an
objective function). It integrates a global Adaptive Particle Swarm
Optimization (APSO) strategy with a local Levenberg-Marquardt (LM) optimization
strategy using adaptive rules based on runtime performance. The global strategy
optimizes the location of a set of solutions (particles) in the parameter
space. The LM strategy is applied only to a subset of the particles at
different stages of the optimization based on the adaptive rules. After the LM
adjustment of the subset of particle positions, the updated particles are
returned to the APSO strategy. The advantages of coupling APSO and LM in the
manner implemented in squads is demonstrated by comparisons of squads
performance against Levenberg-Marquardt (LM), Particle Swarm Optimization
(PSO), Adaptive Particle Swarm Optimization (APSO; the TRIBES strategy), and an
existing hybrid optimization strategy (hPSO). All the strategies are tested on
2D, 5D and 10D Rosenbrock and Griewank polynomial test functions and a
synthetic hydrogeologic application to identify the source of a contaminant
plume in an aquifer. Tests are performed using a series of runs with random
initial guesses for the estimated (function/model) parameters. Squads is
observed to have the best performance when both robustness and efficiency are
taken into consideration than the other strategies for all test functions and
the hydrogeologic application
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