<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0">
<channel>
<title> International Journal of Optimization in Civil Engineering </title>
<link>http://ijoce.iust.ac.ir</link>
<description>Iran University of Science & Technology - Journal articles for year 2015, Volume 5, Number 2</description>
<generator>Yektaweb Collection - https://yektaweb.com</generator>
<language>en</language>
<pubDate>2015/3/10</pubDate>

					<item>
						<title>OPTIMAL WIND RESISTANT DESIGN OF TALL BUILDINGS UTILIZING MINE BLAST ALGORITHM</title>
						<link>http://education.iust.ac.ir/ijoce/browse.php?a_id=204&amp;sid=1&amp;slc_lang=en</link>
						<description>Practical design of tall frame-tube and diagrids are formulated as two discrete optimization problems searching for minimal weight undercodified constraints under gravitational and wind loading due to Iranian codes of practice for steel structures (Part 6 &amp; Part 10). Particular encoding of design vector is proposed to efficiently handle both problems leading to minimal search space. Two types of modeling are employed for the sizing problem one by rigid floors without rotational degrees of freedom and the other with both translational and rotational degrees of freedom. The optimal layout of diagrids using rigid model is 
searched as the second problem. Then performance of Mine Blast Optimization as a recent meta-heuristic is evaluated in these problems treating a number of three-dimensional structural models via comparative study with the common Harmony Search and Particle Swarm Optimization. Considerable benefit in material cost minimization is obtained by these algorithms using tuned parameters. Consequently, effectiveness of HS is observed less than the other two while MBO has shown considerable convergence rate and particle swarm optimiztion is found more trustable in global search of the second problem.</description>
						<author>M. Shahrouzi</author>
						<category></category>
					</item>
					
					<item>
						<title>RELIABILITY-BASED DESIGN OPTIMIZATION OF COMPLEX FUNCTIONS USING SELF-ADAPTIVE PARTICLE SWARM OPTIMIZATION METHOD </title>
						<link>http://education.iust.ac.ir/ijoce/browse.php?a_id=205&amp;sid=1&amp;slc_lang=en</link>
						<description>A Reliability-Based Design Optimization (RBDO) framework is presented that accounts for stochastic variations in structural parameters and operating conditions. The reliability index calculation is itself an iterative process, potentially employing an optimization technique to find the shortest distance from the origin to the limit-state boundary in a standard normal space. Monte Carlo simulation (MCs) is embedded into a design optimization procedure by a modular double loop approach, which the self-adaptive version of particle swarm optimization method is introduced as an optimization technique. Double loop method has the advantage of being simple in concepts and easy to implement. First, we study the efficiency of self-adaptive PSO algorithm inorder to solve the optimization problem in reliability analysis and then compare the results with the Monte Carlo simulation. While computationally significantly more expensive than deterministic design optimization, the examples illustrate the importance of accounting for uncertainties and the need for regarding reliability-based optimization methods and also, should encourage the use of PSO as the best of evolutionary optimization methods to more such reliability-based optimization problems. </description>
						<author>M. R Ghasemi</author>
						<category></category>
					</item>
					
					<item>
						<title>A MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM USING DECOMPOSITION (MOEA/D) AND ITS APPLICATION IN MULTIPURPOSE MULTI-RESERVOIR OPERATIONS</title>
						<link>http://education.iust.ac.ir/ijoce/browse.php?a_id=208&amp;sid=1&amp;slc_lang=en</link>
						<description>This paper presents a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for the optimal operation of a complex multipurpose and multi-reservoir system. Firstly, MOEA/D decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. It uses information of its several neighboring sub-problems for optimizing each sub-problem. This simple procedure makes MOEA/D have lower computational complexity compared with non-dominated sorting genetic algorithm II (NSGA-II). The algorithm (MOEA/D) is compared with the Genetic Algorithm (NSGA-II) using a set of common test problems and the real-world Zohre reservoir system in southern Iran. The objectives of the case study include water supply of minimum flow and agriculture demands over a long-term simulation period. Experimental results have demonstrated that MOEA/D can improve system performance to reduce the effect of drought compared with NSGA-II superiority. Therefore, MOEA/D is highly competitive and recommended to solve multi-objective optimization problems for water resources planning and management.</description>
						<author>I. Ahmadianfar</author>
						<category></category>
					</item>
					
					<item>
						<title>OPTIMIZATION OF RC FRAMES BY AN IMPROVED ARTIFICIAL BEE COLONY ALGORITHM</title>
						<link>http://education.iust.ac.ir/ijoce/browse.php?a_id=209&amp;sid=1&amp;slc_lang=en</link>
						<description>A new meta-heuristic algorithm is proposed for optimal design of reinforced concrete (RC) frame structures subject to combinations of gravity and lateral static loads based on ACI 318-08 design code. In the present work, artificial bee colony algorithm (ABCA) is focused and an improved ABCA (IABCA) is proposed to achieve the optimization task. The total cost of the RC frames is minimized during the optimization process subject to constraints on demand capacity ratios (DCRs) of structural members. Three benchmark design examples are tested using ABCA and IABCA and the results are compared with those of presented in the literature. The numerical results indicate that the proposed IABCA is an efficient computational tool for discrete optimization of RC frames.</description>
						<author>S. Gholizadeh </author>
						<category></category>
					</item>
					
					<item>
						<title>STRUCTURAL RELIABILITY ASSESSMENT UTILIZING FOUR METAHEURISTIC ALGORITHMS</title>
						<link>http://education.iust.ac.ir/ijoce/browse.php?a_id=210&amp;sid=1&amp;slc_lang=en</link>
						<description>The failure probability of the structures is one of the challenging problems in structural engineering. To obtain the reliability index introduced by Hasofer and Lind, one needs to solve a nonlinear equality constrained optimization problem. In this study, four of the most recent metaheuristic algorithms are utilized for finding the design point and the failure probability of problems with continuous random variables. These algorithms consist of Improved Ray Optimization, Democratic Particle Swarm Optimization, Colliding Bodies Optimization, and Enhanced Colliding Bodies Optimization. The performance of these algorithms is tested on nineteen engineering optimization problems</description>
						<author>A. Kaveh </author>
						<category></category>
					</item>
					
					<item>
						<title>OPTIMUM RESISTANCE FACTOR FOR REINFORCED CONCRETE BEAMS RETROFITTED WITH U-WRAP FRP</title>
						<link>http://education.iust.ac.ir/ijoce/browse.php?a_id=212&amp;sid=1&amp;slc_lang=en</link>
						<description>The  use  of  fiber  reinforced  polymer  (FRP)  U-wrap  to  rehabilitate  concrete  beams  has increased in popularity over the past few years. As such, many design codes and guidelines have been developed to enable designers to use of FRP for retrofitting reinforced concrete beams. FIB is the only guideline for design which presents a formula for torsional capacity of  concrete  beams  strengthened  with  FRP.  The  Rackwitz-Fiessler  method  was  applied  to make a reliability assessment on the torsional capacity design of concrete beams retrofitted with U-wrap FRP laminate by this guideline. In this paper, the average of reliability index obtained is 2.92, reflecting reliability of the design procedures. This value is somehow low in  comparison  to  target  reliability  level  of  3.5  used  in  the  guideline  calibration  and  so, optimum resistance factor may be needed in future guideline revisions. From the study on the  relation  between  average  reliability  index  and  optimum  resistance  factor,  a  value  of 0.723 for the optimum resistance factor is suggested.</description>
						<author>H. Dehghani </author>
						<category></category>
					</item>
					
					<item>
						<title>IMPROVED BAT ALGORITHM FOR OPTIMUM DESIGN OF LARGE-SCALE TRUSS STRUCTURES</title>
						<link>http://education.iust.ac.ir/ijoce/browse.php?a_id=213&amp;sid=1&amp;slc_lang=en</link>
						<description>Deterring the optimum design of large-scale structures is a difficult task. Great number of design  variables,  largeness  of  the  search  space  and  controlling  great  number  of  design constraints are major preventive factors in performing optimum design of large-scale truss structures  in  a  reasonable  time.  Meta-heuristic  algorithms  are  known  as  one  of  the  useful tools  to  deal  with  these  problems.  This  paper  presents  an  improved  bat  algorithm  for optimizing large-scale structures. The capability of the algorithm is examined by comparing the  resulting  design  parameters  and  structural  weight  with  those  of  other  methods  from 
literature.</description>
						<author>S. Talatahariand </author>
						<category></category>
					</item>
					
	</channel>
</rss>
