Dissertation/Thesis Abstract

Parametric Study of the Multi-Objective Particle Swarm Optimization and the Multi-Objective Bee Algorithm Applied to a Simply Supported Flat-Truss Bridge Structure
by Suarez, Sergio, M.S., California State University, Long Beach, 2018, 88; 10978095
Abstract (Summary)

Most engineering fields often encounter challenges in material, performance, and time efficiency. Truss design is a subject many structural engineers confront in their careers. Optimization is an effective approach in solving preliminary designs of truss structures. This paper studies two different multi-objective optimization algorithms, the particle swarm optimization (MOPSO) and the bee algorithm (MOBA), to optimize a simply supported flat-truss bridge designed by California State University, Long Beach’s Steel Bridge team for the American Institute of Steel Construction (AISC) Spring 2018 competition. The variables, randomly selected from a continuous domain, are the top chord area, bottom chord area, web member area, and the center-to-center distance between the top and bottom chords. The optimized objectives are the weight and deflections of the bridge for the six load combinations stipulated in AISC’s rules. Both algorithms are calibrated using recommended parameter values derived from the parametric studies conducted. To compare their effectiveness, the recommended parameters were selected so that run-times for both optimization codes were similar. Both algorithms generated optimized solutions to the multi-objective truss problem, but MOPSO exhibited more, and better, solutions in a slightly longer run-time than MOBA.

Indexing (document details)
Advisor: Terzic, Vesna
Commitee: Calabrese, Andrea, Rahmani, Mehran
School: California State University, Long Beach
Department: Civil Engineering
School Location: United States -- California
Source: MAI 58/04M(E), Masters Abstracts International
Subjects: Engineering, Civil engineering
Keywords: Bridge (flat-truss), MOBA, MOPSO, Optimization, Structural, Truss
Publication Number: 10978095
ISBN: 978-0-438-89347-4
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