S. Kazemzadeh Azad

Discrete size optimization of steel trusses using a refined big bang–big crunch algorithm

This article presents a methodology that provides a method for design optimization of steel truss structures based on a refined big bang–big crunch (BB-BC) algorithm. It is shown that a standard formulation of the BB-BC algorithm occasionally falls short of producing acceptable solutions to problems from discrete size optimum design of steel trusses. A reformulation of the algorithm is proposed and implemented for design optimization of various discrete truss structures according to American Institute of Steel Construction Allowable Stress Design (AISC-ASD) specifications. Furthermore, the performance of the proposed BB-BC algorithm is compared to its standard version as well as other well-known metaheuristic techniques. The numerical results confirm the efficiency of the proposed algorithm in practical design optimization of truss structures.

Upper bound strategy for metaheuristic based design optimization of steel frames

Optimum design of structural systems based on metaheuristic algorithms suffers from enormously time-consuming structural analyses to locate a reasonable design. In this paper an upper bound strategy (UBS) is proposed for reducing the total number of structural analyses in metaheuristic based design optimization of steel frame structures. The well-known big bang-big crunch algorithm as well as its two enhanced variants are adopted as typical metaheuristic algorithms to evaluate the effect of the UBS on computational efficiency of these techniques. The numerical results reveal that the UBS can significantly lessen the total computational cost in metaheuristic based design optimization of steel frames.

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

Abstract 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.

Discrete optimization of truss structure using Probability Collectives

Traditionally, complex systems were treated using centralized approaches; however, recent trends highlighted that the growing complexity can be best dealt by decomposing the entire system into subsystems and further treat them in a distributed way. The approach of Probability Collectives (PC) in Collective Intelligence (COIN) framework decomposes the entire system into a Multi-Agent System (MAS) or a collection of rational and self interested agents and further optimizes them in a distributed and decentralized way to reach the desired system objective. The complexity of the system increases when constraints are involved. The approach of PC is incorporated with a feasibility-based rule to handle the solution based on number of constraints violated, and further drives the convergence towards feasibility. Importantly for the first time, constrained PC approach has been tested solving a discrete problem such as 45-bar truss structure. The results are validated by comparing with the contemporary algorithms as well.

Upper Bound Strategy in Optimum Design of Truss Structures: A Big Bang-Big Crunch Algorithm Based Application

One main shortcoming of metaheuristic search techniques in structural optimization is the large number of time-consuming structural analyses required for convergence to a reasonable solution. This study is an attempt to apply the so-called upper bound strategy (UBS) as a simple, yet an efficient strategy to reduce the total number of structural analyses through avoiding unnecessary analyses during the course of optimization. Although, the usefulness of the UBS is demonstrated in conjunction with a big bang-big crunch algorithm developed for optimum design of truss structures, it can be integrated with any other metaheuristic technique which works on the basis of (μ+Λ) evolutionary model. The numerical investigations over three benchmark truss optimization instances reveal that the UBS can reduce the total number of required structural analyses of the standard BB-BC algorithm to a great extent.