Optimizing most structural systems used in practice requires considering design variables as discrete quantities. The paper presents a simple genetic algorithm for optimizing structural systems with discrete design variables. As genetic algorithms (GAs) are best suited for unconstrained optimization problems, it is necessary to transform the constrained problem into an unconstrained one. A penalty-based transformation method is used in the present work. The penalty parameter depends on the degree of constraint violation, which is found to be wellsuited for a parallel search using genetic algorithms. The concept of optimization using the genetic algorithm is presented in detail using a three-bar truss problem. All the computations for three successive generations are presented in the form of tables for easy understanding of the algorithm. Two standard problems from literature are solved and results compared. The application of the genetic algorithm to design optimization of a larger problem is illustrated using a 160-bar transmission tower. © ASCE.

S. Rajeev

C. S. Krishnamoorthy

Computer Programming--Algorithms

MATHEMATICAL STATISTICS--RANDOM NUMBER GENERATION

Mathematical Techniques--Algorithms

TRUSSES--STRUCTURAL DESIGN

DISCRETE DESIGN VARIABLES

Genetic algorithms

PENALTY BASED TRANSFORMATION METHOD

Structural design

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Discrete Optimization of Structures Using Genetic Algorithms

Journal of Structural Engineering (United States)

A new approach is presented for the optimization of steel lattice towers by combining genetic algorithms and an object-oriented approach. The purpose of this approach is to eliminate the difficulties in the handling of large size problems such as lattice towers. Improved search and rapid convergence are obtained by considering the lattice tower as a set of small objects and combining these objects into a system. This is possible with serial cantilever structures such as lattice towers. A tower consists of panel objects, which can be classified as separate objects, as they possess an independent property as well as inherent properties. This can considerably reduce the design space of the problem and enhance the result. An optimization approach for the steel lattice tower problem using objects and genetic algorithms is presented here. The paper also describes the algorithm with practical design considerations used for this approach. To demonstrate the approach, a typical tower configuration with practical constraints has been considered for discrete optimization with the new approach and compared with the results of a normal approach in which the full tower is considered. ©ASCE.

Journal of Computing in Civil Engineering

Object-oriented optimization approach using genetic algorithms for lattice towers

Design optimization of reinforced concrete plane frames using genetic algorithm-based methodology is presented in this paper. Most of the approaches reported in the literature consider the design variables to be continuous, and the optimal solution obtained requires further modification to make it constructible. Since the area of reinforcement after detailing is different from that obtained theoretically, use of area of reinforcement as a continuous variable cannot provide rational solutions. Aspects such as detailing and placing of reinforcement in beams and columns and other issues related to construction are brought into the optimal design model presented in this paper. Genetic algorithm-based methodologies provide ideal techniques for handling such issues and generate rational optimal solutions. Examples of reinforced concrete plane frames are solved, and results are presented. Emphasis is placed on genetic modeling aspects, which provide mechanisms for considering realistically the practical issues, resulting in a rigorous optimal design model providing rational solutions.

Computer-Aided Civil and Infrastructure Engineering

Genetic algorithm-based methodology for design optimization of reinforced concrete frames

Design optimization of trusses consists of arriving at optimum topology, configuration, and sizes of cross-sectional parameters, when weight of the truss is minimum, satisfying a set of specified constraints. Genetic algorithms-based methods provide ideal techniques for modeling many practical considerations, which make the optimal solution constructible. Two improved methods for size and configuration optimization and topology optimization are presented in this paper. The first one is a two-phase method, which in addition to arriving at better solutions compared to those obtained using earlier methods, also improves the computational efficiency. The second method, based on a variable string length genetic algorithm (VGA), addresses the topology optimization problem, taking into account a number of practical issues. Classical problems from literature are solved and the results are compared. Features of the proposed method, which help in modeling and application to optimal design of large truss structures, are demonstrated by solving a problem of a microwave antenna tower.

Journal of Structural Engineering

Genetic algorithms-based methodologies for design optimization of trusses

Discrete optimization of structures using genetic algorithms

One of the simplest approaches for optimizing statically loaded trusses is to handle stress con-straints using the fully stressed design (FSD) technique. The displacement constraints are handled using the optimality criteria (OC) approach. Optimizing structural systems require regarding design variables as discrete quantities. The easiest approach is to consider the variables continuous and later approximate them to the immediate higher available discrete values. This procedure leads to nonoptimal designs. A modified approximation rule is suggested here. This rule leaves some displacement constraints violated, but a new technique based on 1-0 integer programming (IP) is developed to satisfy the displacement constraints. The proposed technique uses very few design variables. It is very efficient for designing trusses using available sections. Problems involving up to 1027 design variables have been solved efficiently with this approach.

Optimal design of trusses using available sections

Health hazard evaluation report: HETA-94-0104-2458, University of Iowa, Iowa City, Iowa.

31st Structures, Structural Dynamics and Materials Conference

Optimal Design of a Class of Welded Structures via Genetic Algorithms

Engineering with Computers

Computer-aided pipeline operation using genetic algorithms and rule learning. PART I: Genetic algorithms in pipeline optimization

Computers & Structures

Minimum weight design of trusses using improved move limit method of sequential linear programming

Journal	Journal of Structural Engineering (United States)
ISSN	07339445
Open Access	No