An elitist self-adaptive step-size search for structural design optimization

Azad, S. Kazemzadeh
Hasançebi, Oğuzhan
This paper presents a method for optimal sizing of truss structures based on a refined self-adaptive step-size search (SASS) algorithm. An elitist self-adaptive step-size search (ESASS) algorithm is proposed wherein two approaches are considered for improving (i) convergence accuracy, and (ii) computational efficiency. In the first approach an additional randomness is incorporated into the sampling step of the technique to preserve exploration capability of the algorithm during the optimization. Furthermore, an adaptive sampling scheme is introduced to enhance quality of the final solutions. In the second approach computational efficiency of the technique is accelerated through avoiding unnecessary analyses throughout the optimization process using the so-called upper bound strategy (UBS). The numerical results indicate the efficiency of the proposed ESASS algorithm.


A reformulation of the ant colony optimization algorithm for large scale structural optimization
Hasançebi, Oğuzhan; Saka, M.p. (2011-01-01)
This study intends to improve performance of ant colony optimization (ACO) method for structural optimization problems particularly with many design variables or when design variables are chosen from large discrete sets. The algorithm developed with ACO method employs the so-called pheromone scaling approach to overcome entrapment of the search in a poor local optimum and thus to recover efficiency of the method for large-scale optimization problems. Besides, a new formulation is proposed for the local upda...
A two-step method for the optimum design of trusses with commercially available sections
Oral, Süha (1997-01-01)
A two-step method is presented for the optimum design of trusses with available sections under stress and Euler buckling constraints. The shape design of the truss is used as a means to convert the discrete solution into a continuous one. In the first step of the method, a continuous solution is obtained by sizing and shape design using an approximate polynomial expression for the buckling coefficients. In the second step, the member sizes obtained are changed to the nearest available sections and the truss...
The static stochastic knapsack problem with normally distributed item sizes
Merzifonluoglu, Yasemin; Geunes, Joseph; Romeijn, H. Edwin (Springer Science and Business Media LLC, 2012-09-01)
This paper develops exact and heuristic algorithms for a stochastic knapsack problem where items with random sizes may be assigned to a knapsack. An item's value is given by the realization of the product of a random unit revenue and the random item size. When the realization of the sum of selected item sizes exceeds the knapsack capacity, a penalty cost is incurred for each unit of overflow, while our model allows for a salvage value for each unit of capacity that remains unused. We seek to maximize the ex...
Adaptive dimensional search: A new metaheuristic algorithm for discrete truss sizing optimization
Hasançebi, Oğuzhan (2015-07-01)
In the present study a new metaheuristic algorithm called adaptive dimensional search (ADS) is proposed for discrete truss sizing optimization problems. The robustness of the ADS lies in the idea of updating search dimensionality ratio (SDR) parameter online during the search for a rapid and reliable convergence towards the optimum. In addition, several alternative stagnation-control strategies are integrated with the algorithm to escape from local optima, in which a limited uphill (non-improving) move is p...
A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
Citation Formats
S. K. Azad and O. Hasançebi, “An elitist self-adaptive step-size search for structural design optimization,” APPLIED SOFT COMPUTING, pp. 226–235, 2014, Accessed: 00, 2020. [Online]. Available: