Optimization of weight and collapse energy of space structures using the multi-objective modified crow search algorithm

Due to the use of space structures in monumental and significant buildings, providing adequate safety and proper structural performance must always be taken into account. In this study, the geometric and material nonlinear behaviors are directly considered in the design of space structures so that the resulting designs are economical while having appropriate collapse behavior. Weight and collapse energy are considered the objective functions, and the problem is considered as a multi-objective optimization problem. This is the first attempt to combine the weight and collapse energy simultaneously for the optimal design of structures. To solve such problems, two new multi-objective algorithms based on the recently introduced crow search algorithm (CSA) have been proposed. These algorithms are called multi-objective crow search algorithm (MOCSA) and multi-objective modified crow search algorithm (MOMCSA). MOCSA and MOMCSA have similar structures and details, except that the MOCSA generates the new solution as the CSA approach does while generating the new solution in the MOMCSA is modified. The modification of the search vector and the search range is employed as two simple and effective changes in MOMCSA to enhance the exploration and exploitation. To evaluate the proposed algorithms, three space structures were optimized using the proposed algorithms and two well-known algorithms, MOPSO and NSGA-II. The results indicate superiority of MOMCSA to the other algorithms.

[1]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[2]  Aboul Ella Hassanien,et al.  Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization , 2020, Neural Computing and Applications.

[3]  Huu-Tai Thai,et al.  Nonlinear inelastic time-history analysis of truss structures , 2011 .

[4]  Huu-Tai Thai,et al.  Large deflection inelastic analysis of space trusses using generalized displacement control method , 2009 .

[5]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Vijander Singh,et al.  An improved Crow Search Algorithm for high-dimensional problems , 2017, J. Intell. Fuzzy Syst..

[8]  Honghai Liu,et al.  Research on gesture recognition of smart data fusion features in the IoT , 2019, Neural Computing and Applications.

[9]  A. Kaveh,et al.  A new multi-swarm multi-objective optimization method for structural design , 2013, Adv. Eng. Softw..

[10]  C. S. Krishnamoorthy,et al.  Inelastic post‐buckling analysis of truss structures by dynamic relaxation method , 1994 .

[12]  Li-Juan Li,et al.  A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures , 2018 .

[13]  George E. Blandford,et al.  Post‐Buckling Analysis of Steel Space Trusses , 1989 .

[14]  Seung-Eock Kim,et al.  Practical advanced analysis software for nonlinear inelastic dynamic analysis of steel structures , 2011 .

[15]  S. Fong,et al.  Metaheuristic Algorithms: Optimal Balance of Intensification and Diversification , 2014 .

[16]  Saeed Gholizadeh,et al.  Performance based discrete topology optimization of steel braced frames by a new metaheuristic , 2018, Adv. Eng. Softw..

[17]  Gonzalo Pajares,et al.  Improving multi-criterion optimization with chaos: a novel Multi-Objective Chaotic Crow Search Algorithm , 2017, Neural Computing and Applications.

[18]  Saeed Gholizadeh,et al.  Multi-objective seismic design optimization of steel frames by a chaotic meta-heuristic algorithm , 2017, Engineering with Computers.

[19]  A. Kaveh,et al.  Multi-objective colliding bodies optimization algorithm for design of trusses , 2019, J. Comput. Des. Eng..

[20]  Saeed Gholizadeh,et al.  Multi-objective design optimization of steel moment frames considering seismic collapse safety , 2019, Engineering with Computers.

[21]  S. Talatahari,et al.  Seismic energy-based design of BRB frames using multi-objective vibrating particles system optimization , 2020 .

[22]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[23]  Eysa Salajegheh,et al.  Size optimization of nonlinear scallop domes by an enhanced particle swarm algorithm , 2013 .

[24]  Eysa Salajegheh,et al.  Enhanced crow search algorithm for optimum design of structures , 2019, Appl. Soft Comput..

[25]  Saeed Gholizadeh,et al.  Collapse-performance-aided design optimization of steel concentrically braced frames , 2019, Engineering Structures.

[26]  Gerard Parke,et al.  Dynamic Snap-Through Buckling of Truss-Type Structures , 2001 .

[27]  Cedric Marsh,et al.  Closure of "Modification of Behavior of Double-Layer Grids: Overview" , 1989 .

[28]  Sungwon Kim,et al.  A New Optimization Approach for the Least-Cost Design of Water Distribution Networks: Improved Crow Search Algorithm , 2019, Water Resources Management.

[29]  Antonio J. Nebro,et al.  A survey of multi-objective metaheuristics applied to structural optimization , 2014 .

[30]  Xu Wang,et al.  Multi-objective topology and sizing optimization of truss structures based on adaptive multi-island search strategy , 2011 .

[31]  Saeed Gholizadeh,et al.  Performance-Based Optimum Design of Steel Frames by an Improved Quantum Particle Swarm Optimization , 2014 .

[32]  Victor Yepes,et al.  Multiobjective Optimization of Concrete Frames by Simulated Annealing , 2008, Comput. Aided Civ. Infrastructure Eng..

[33]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[34]  Alireza Askarzadeh,et al.  Multi-objective optimization framework of a photovoltaic-diesel generator hybrid energy system considering operating reserve , 2018, Sustainable Cities and Society.

[35]  Mohamed El Yafrani,et al.  A hybrid crow search algorithm for solving the DNA fragment assembly problem , 2018, Expert Syst. Appl..

[36]  C. A. Coello Coello,et al.  Multiobjective structural optimization using a microgenetic algorithm , 2005 .

[37]  Ariel Hanaor,et al.  Ultimate Load Testing of Space Trusses , 1982 .

[38]  M. Papadrakakis,et al.  Multi-objective optimization of skeletal structures under static and seismic loading conditions , 2002 .

[39]  Seung-Eock Kim,et al.  Reliability-based design optimization of nonlinear inelastic trusses using improved differential evolution algorithm , 2018, Adv. Eng. Softw..

[40]  Nantiwat Pholdee,et al.  Multi-objective modified heat transfer search for truss optimization , 2020, Engineering with Computers.

[41]  Alireza Askarzadeh,et al.  A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm , 2016 .

[42]  Guan-Chun Luh,et al.  Multi-objective optimal design of truss structure with immune algorithm , 2004 .

[43]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[44]  Alireza Askarzadeh,et al.  Optimizing operation of a photovoltaic/diesel generator hybrid energy system with pumped hydro storage by a modified crow search algorithm , 2020 .

[45]  Nantiwat Pholdee,et al.  Simultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithms , 2018, Engineering with Computers.

[46]  Manolis Papadrakakis,et al.  Multiobjective Optimization of Space Structures under Static and Seismic Loading Conditions , 2005, Evolutionary Multiobjective Optimization.

[47]  Nantiwat Pholdee,et al.  Structural optimization using multi-objective modified adaptive symbiotic organisms search , 2019, Expert Syst. Appl..