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Structural and Multidisciplinary Optimization
Published in: Structural and Multidisciplinary Optimization 6/2017

02-12-2016 | RESEARCH PAPER

Modified firefly algorithm for multidimensional optimization in structural design problems

Authors: Jui-Sheng Chou, Ngoc-Tri Ngo

Published in: Structural and Multidisciplinary Optimization | Issue 6/2017

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Abstract

An enhanced nature-inspired metaheuristic optimization algorithm, called the modified firefly algorithm (MFA) is proposed for multidimensional structural design optimization. The MFA incorporates metaheuristic components, namely logistic and Gauss/mouse chaotic maps, adaptive inertia weight, and Lévy flight with a conventional firefly algorithm (FA) to improve its optimization capability. The proposed MFA has several advantages over its traditional FA counterpart. Logistic chaotic maps provide a diverse initial population. Gauss/mouse maps allow the tuning of the FA attractiveness parameter. The adaptive inertia weight controls the local exploitation and the global exploration of the search process. Lévy flight is used in the exploitation of the MFA. The proposed MFA was evaluated by comparing its performance in solving a series of benchmark functions with those of the FA and other well-known optimization algorithms. The efficacy of the MFA was then proven by its solutions to three multidimensional structural design optimization problems; MFA yielded the best solutions among the observed algorithms. Experimental results revealed that the proposed MFA is more efficient and effective than the compared algorithms. Therefore, the MFA serves as an alternative algorithm for solving multidimensional structural design optimization problems.
previous article Reliability analysis of structures using stochastic response surface method and saddlepoint approximation
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Metadata
Title
Modified firefly algorithm for multidimensional optimization in structural design problems
Authors
Jui-Sheng Chou
Ngoc-Tri Ngo
Publication date
02-12-2016
Publisher
Springer Berlin Heidelberg
Published in
Structural and Multidisciplinary Optimization / Issue 6/2017
Print ISSN: 1615-147X
Electronic ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-016-1624-x

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