Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm

Mohammad Khajehzadeh, Mohd. Raihan Taha, Mahdiyeh Eslami

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retaining walls. The proposed algorithm combines an adaptive gravitational search algorithm (AGSA) with pattern search (PS) called AGSA-PS. In the resulting hybrid approach, the PS algorithm is employed as a local search algorithm around the global solution found by AGSA. The proposed algorithm was tested on a set of five well-known benchmark functions and simulation results demonstrate the superiority of the new method compared with the standard algorithm. Thereafter, the proposed AGSA-PS is applied for multi-objective optimisation of RC retaining walls. Two objective functions include total cost and embedded CO2 emissions of retaining wall are considered. The reliability and efficiency of the AGSA-PS for multi-objective optimisation of retaining structures are investigated by considering two design examples of retaining walls. Experimental results demonstrate that the resulting algorithm has high viability, accuracy and significantly outperforms the original algorithm and some other methods in the literature.

Original languageEnglish
Pages (from-to)229-242
Number of pages14
JournalCivil Engineering and Environmental Systems
Volume31
Issue number3
DOIs
Publication statusPublished - 2014

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Retaining walls
Multiobjective optimization
Reinforced concrete

Keywords

  • gravitational search algorithm
  • multi-objective optimisation
  • pattern search
  • retaining wall

ASJC Scopus subject areas

  • Civil and Structural Engineering

Cite this

Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm. / Khajehzadeh, Mohammad; Taha, Mohd. Raihan; Eslami, Mahdiyeh.

In: Civil Engineering and Environmental Systems, Vol. 31, No. 3, 2014, p. 229-242.

Research output: Contribution to journalArticle

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