Elite Particles Method in Discrete Metaheuristic Optimization of Structures

Authors

1 Department of Civil Engineering, Mahabad Branch, Islamic Azad university, Mahabad, Iran

2 Faculty of Civil Engineering, University of Urmia, Urmia, Iran

Abstract

One of the main concerns in optimization methods is reduction the number of function evaluation which is mentioned in many studies like (Gholizadeh et al, 2018).The present study focuses on a method and series of actions designed to achieve the answer with the aim of minimization the total number of analysis; and so the time; needed for global and local search. Although the suggested elite particles method (EPM) can be used for any population based optimization method, but here it is applied to one of the fundamental and widely developed metaheuristic algorithms; namely particle swarm optimization (Eberhart et al, 1995) to handle the truss structures optimization with discrete design variables. As the original version of the assumed method suffers from the slow convergence rate; specially when dealing with the discrete optimization problems; the elite particles modification algorithm; which can be used in almost all population based metaheuristic optimization methods; will be implemented for that method. Here the elite particles method is attached with the particle swarm optimization method and the gained metaheuristic algorithm is named modified particle swarm optimization (MPSO). MPSO utilizes two computational strategies named ‘Regeneration’ and ‘Mutation’.

Keywords


Cheng MY, Prayogo D, Wu YW, Lukito MM, “A hybrid harmony search algorithm for discrete sizing optimization of truss structure”, Automation in Construction, 2016, 69, 21-33.
Dorigo M, Birattari M, “Ant colony optimization”, Encyclopedia of Machine Learning, Springer, 2010, 36-39.
Eberhart RC, Kennedy J, “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4-6 Oct. 1995.
Gholizadeh S, “Layout optimization of truss structures by hybridizing cellular automata and particle swarm optimization”, Computers and Structures, 2013, 125, 86-99.
Gholizadeh S, Ebadijalal M, “Performance based discrete topology optimization of steel braced frames by a new metaheuristic”, Advances in Engineering Software, 2018, 123, 77-92.
Gholizadeh S, Poorhoseini H, “Seismic layout optimization of steel braced frames by an improved dolphin echolocation algorithm”, Structural Multidisciplinary Optimization, 2016, 54, 1011-29.
Gholizadeh S, Milany A, “An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures”, Engineering Optimization, 2018, 50, 1-21.
Ho-Huu V, Nguyen-Thoi T, Vo-Duy T, Nguyen-Trang T, “An adaptive elitist differential evolution for optimization of truss structures with discrete design variables”, Computers and Structures, 2016, 165, 59-75
Kaveh A, Ilchi Ghazaan M, “A comparative study of CBO and ECBO for optimal design of skeletal structures”, Computers and Structures, 2015, 153, 137-147.
Kaveh A, Mahdavi V, “Colliding Bodies Optimization method for optimum discrete design of truss structures”, Computers and Structures, 2014, 139, 43-53.
Kaveh A, Talatahari S, “Size optimization of space trusses using Big Bang-Big Crunch algorithm”, Computers and Structures, 2009, 87, 1129-40.
Kaveh A, Talatahari S, “A charged system search with a fly to boundary method for discrete optimum design of truss structures”, Asian Journal of Civil Engineering, 2010, 11 (3), 277-293.
Kaveh A, Talatahari S, “A particle swarm ant colony optimization for truss structures with discrete variables”, Journal of Constructional Steel Research, 2009, 65, 1558-68.
Kazemzadeh Azad S, Hasançebi O, “An elitist self-adaptive step-size search for structural design optimization”, Applied Soft Computing, 2014, 19, 226-235.
Li LJ, Huang ZB, Liu F, “A heuristic particle swarm optimization method for truss structures with discrete variables”, Computers and Structures, 2009, 87, 435-43.
Mirjalili S, “SCA: A Sine Cosine Algorithm for solving optimization problems”, Knowledge-Based Systems, 2016, 96, 120-133.
Rashedi E, Nezamabadi-Pour H, Saryazdi S, “GSA: a gravitational search algorithm”, Information Sciences, 2009, 179, 2232-2248.
Sadollah A, Eskandar H, Bahreininejad A, Kim JH, “Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures”, Computers and Structures, 2015, 149, 1-16.
Storn R, Price K, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, 1997, 11, 341-359.
Togan V, Daloglu AT, “An improved genetic algorithm with initial population strategy and self-adaptive member grouping”, Computers and Structures, 2008, 86, 1204-1218.
Zhu JH, He F, Liu T, FH, Zhang WH, Liu Q, Yang C, “Structural topology optimization under harmonic base acceleration excitations”, Structural and Multidisciplinary Optimization, 2018, 57, 1061-1078.