caa a22 4500 46324379X CHVBK 20180405153320.0 cr unu---uuuuu 170326e20071101xx s 000 0 eng 10.1007/s00158-006-0084-0 doi (NATIONALLICENCE)springer-10.1007/s00158-006-0084-0 Hybridization of genetic algorithm with immune system for optimization problems in structural engineering [Elektronische Daten] [S. Rajasekaran, S. Lavanya] Optimization is the task of getting the best solution among the feasible solutions. There are many methods available to obtain an optimized solution. Genetic algorithm (GA), which is a heuristic type of optimization method, is discussed in this paper. The focus of the paper is the use of GA for large dimensionality design problems, where computational efficiency is a major concern. The motivation of this paper is to hybridize GA with an immune system mechanism by avoiding the implementation of penalty constants, which are highly sensitive to the choice of algorithm parameters. The principal advantage of the immune system is in its seamless integration with GA-based search for optimal design. It is being hybridized with the immune system mechanism. The hybrid GA and immune system is applied for the design of the optimal mix of high-performance concrete (HPC), which is still based on trial mix and for which no rigorous mathematical approach is available. As such, to infer the values of strength and slump, a wavelet back propagation neural network or wavelet neural network is used for any HPC mix. It is necessary to minimize the cost of HPC/unit weight of HPC subjected to strength and slump constraints. The interwoven algorithm is also applied to obtain optimal sectional areas for minimum weight of space trusses subjected to static loading. Formian programming language is used for the generation of the space trusses, and Feast package is used for the static analysis of the trusses. In addition to the induction of immune system in the GA for constraint handling, it is being applied in this particular application for improving the search of GA in obtaining the best optimal solution. For obtaining the optimal sections of space trusses subjected to earthquake loading, SAP 90 package is used, and reliable results are obtained. Springer-Verlag Berlin Heidelberg, 2007 Immune system nationallicence Genetic algorithm nationallicence Earthquake nationallicence High-performance concrete nationallicence Response spectrum nationallicence Double-layer grid nationallicence Barrel vault nationallicence A : Area of the member nationallicence C : Constraint constant nationallicence C : Cement nationallicence D : Stress constant nationallicence D.L : Dead Load nationallicence d : Diameter of the member nationallicence E : Youngs modulus of the material nationallicence E : Error nationallicence F : Function nationallicence F y : Yield stress nationallicence g j : jth Constraint nationallicence H : Height of the column nationallicence I : Moment of inertia of the member nationallicence K : Constraint constant, penalty coefficient nationallicence L : Span of the beam, length of member nationallicence L.L : Live Load nationallicence M : Number of neurons in the output layer nationallicence mcom : Number of struts in a space structure nationallicence mten : Number of ties in a space structure nationallicence mg : Number of groups nationallicence nc : Number of constraints nationallicence ndeg : Number of degrees of freedom nationallicence q i : Force in ith member nationallicence Q : Number of neurons in the input layer nationallicence R : Number of neurons in the hidden layer nationallicence t : Thickness of the member nationallicence u : Displacements nationallicence U : Weight of synapses between input and hidden layers nationallicence u all : Allowable displacement nationallicence V : Output nationallicence W : Weight of the structure nationallicence W : Weight of synapses between hidden and output layers nationallicence W.L : Wind Load nationallicence X : Vector of variables nationallicence α : Momentum factor nationallicence Δ : Incremental quantities nationallicence η : Learning rate nationallicence μ : Fraction of individuals selected for antibodies nationallicence $\rho = \frac{d}{t}$ : Non-dimensional parameter nationallicence σ ti : Tensile stress in ith tie nationallicence σ at : Allowable tensile stress nationallicence ψ : Mother wavelet function nationallicence Z : Match function nationallicence Rajasekaran S. PSG College of Technology, 641004, Coimbatore, Tamilnadu, India aut Lavanya S. Sathyabama University, Chennai, Tamilnadu, India aut Structural and Multidisciplinary Optimization Springer-Verlag 34/5(2007-11-01), 415-429 1615-147X 34:5<415 2007 34 158 https://doi.org/10.1007/s00158-006-0084-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00158-006-0084-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rajasekaran S. PSG College of Technology, 641004, Coimbatore, Tamilnadu, India aut NATIONALLICENCE 700 1- Lavanya S. Sathyabama University, Chennai, Tamilnadu, India aut NATIONALLICENCE 773 0- Structural and Multidisciplinary Optimization Springer-Verlag 34/5(2007-11-01), 415-429 1615-147X 34:5<415 2007 34 158 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer