This study presents the Big Bang and Big Crunch (bb-bc) optimization algorithm for detection of structure damage in large severity. Local damage is represented by a perturbation in the elemental stiffness parameter of...
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This study presents the Big Bang and Big Crunch (bb-bc) optimization algorithm for detection of structure damage in large severity. Local damage is represented by a perturbation in the elemental stiffness parameter of the structural finite element model. A nonlinear objective function is established by minimizing the discrepancies between the measured and calculated acceleration responses (AR) of the structure. The bb-bc algorithm is utilized to solve the objective function, which can localize the damage position and obtain the severity of the damage efficiently. Numerical simulations have been conducted to identify both single and multiple structural damages for beam, plate and European Space Agency Structures. The present approach gives accurate identification results with artificial measurement noise.
The Big Bang-Big Crunch (bb-bc) algorithm is an effective global optimization technique of swarm intelligence with drawbacks of being easily trapped in local optimal results and of converging slowly. To overcome these...
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The Big Bang-Big Crunch (bb-bc) algorithm is an effective global optimization technique of swarm intelligence with drawbacks of being easily trapped in local optimal results and of converging slowly. To overcome these shortages, an improved bb-bc algorithm (Ibb-bc) is proposed in this paper with taking some measures, such as altering the reduced form of exploding radius and generating multiple mass centers. The accuracy and efficiency of Ibb-bc is examined by different types of benchmark test functions. The Ibb-bc is utilized for damage detection of a simply supported beam and the European Space Agency structure with an objective function established by structural frequency and modal data. Two damage scenarios are considered: damage only existed in stiffness and damage existed in both stiffness and mass. Ibb-bc is also validated by an existing experimental study. Results demonstrated that Ibb-bc is not trapped into local optimal results and is able to detect structural damages precisely even under measurement noise.
In data mining, clustering is an important data analysis concept. It plays a vital role in extracting the useful hidden knowledge from large input datasets. This unsupervised technique partitions the input dataset int...
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ISBN:
(纸本)9781538633601
In data mining, clustering is an important data analysis concept. It plays a vital role in extracting the useful hidden knowledge from large input datasets. This unsupervised technique partitions the input dataset into groups called clusters. The data objects mapping is done into clusters such clusters should maintain similarity between the objects within same cluster and dissimilarity between the data objects in different clusters. In this process factors like distance measuring techniques, initial conditions and criterion functions playa key role in finding optimal clusters of data. Many optimization algorithms have come into existence to resolve these types of optimization problems. But still finding optimal clusters is a big challenging task. This work presents hybrid version of the recently devised nature-inspired algorithm i.e. Tornadogenesis Optimization algorithm (TOA) for solving data clustering problems using bb-bc. We framed this work in two phases wherein the first phase testing for optimization performance on 23 standard mathematical benchmark functions took place, in the second phase numerical ability is tested by applying hybridized Tornadogenesis Optimization algorithm (HTOA) on 10 real-world data clustering problems. In addition to that various distance measuring techniques used to test the improvement in clustering performance. We portrayed the obtained results in tabular and graphical forms. Various analysis and comparisons have been made and found that the performance of proposed HTOA is good at solving data clustering problems using Euclidean distance measuring technique.
Big Bang - Big Crunch (bb-bc) optimization algorithm relies on one of the theories of the evolution of the universe;namely, the Big Bang and Big Crunch Theory [1]. It was proposed as a novel optimization method in 200...
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ISBN:
(纸本)9781424465880
Big Bang - Big Crunch (bb-bc) optimization algorithm relies on one of the theories of the evolution of the universe;namely, the Big Bang and Big Crunch Theory [1]. It was proposed as a novel optimization method in 2006 and is shown to be capable of quick convergence. In this work, local search moves are injected in between the original "banging" and "crunching" phases of the optimization algorithm. These phases preserve their structures;but the representative point ("best" or "fittest" point) attained after crunching phase of the iteration is modified with local directional moves using the previous representative points. This hybridization scheme smoothens the path going to optima and decreases the process time for reaching the global minima. The results over benchmark test functions have proven that bb-bc algorithm enhanced with local directional moves has provided more accuracy with the same computation time or for the same number of function evaluations. As a real world case study, the newly proposed routine is applied in target motion analysis problem where the basic parameters defining the target motion is estimated through noise corrupted measurement data.
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