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Showing 19 results for Harmony Search

K.s. Lee, S.w. Han, Z.w. Geem,
Volume 1, Issue 1 (3-2011)
Abstract

Many methods have been developed for structural size and configuration optimization in which cross-sectional areas are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes two efficient structural optimization methods based on the harmony search (HS) heuristic algorithm that treat both discrete sizing variables and integrated discrete sizing and continuous geometric variables. The HS algorithm uses a stochastic random search instead of a gradient search so the former has a new-paradigmed derivative. Several truss examples from the literature are also presented to demonstrate the effectiveness and robustness of the new method, as compared to current optimization methods.
S. Gholizadeh, A. Barzegar , Ch. Gheyratmand,
Volume 1, Issue 3 (9-2011)
Abstract

The main aim of the present study is to propose a modified harmony search (MHS) algorithm for size and shape optimization of structures. The standard harmony search (HS) algorithm is conceptualized using the musical process of searching for a perfect state of the harmony. It uses a stochastic random search instead of a gradient search. The proposed MHS algorithm is designed based on elitism. In fact the MHS is a multi-staged version of the HS and in each stage a new harmony memory is created using the information of the previous stages. Numerical results reveal that the proposed algorithm is a powerful optimization technique with improved exploitation characteristics compared with the standard HS.
S. Gholizadeh, H. Barati,
Volume 2, Issue 3 (7-2012)
Abstract

In the present study, the computational performance of the particle swarm optimization (PSO) harmony search (HS) and firefly algorithm (FA), as popular metaheuristics, is investigated for size and shape optimization of truss structures. The PSO was inspired by the social behavior of organisms such as bird flocking. The HS imitates the musical performance process which takes place when a musician searches for a better state of harmony, while the FA was based on the idealized behavior of the flashing characteristics of natural fireflies. These algorithms were inspired from different natural sources and their convergence behavior is focused in this paper. Several benchmark size and shape optimization problems of truss structures are solved using PSO, HS and FA and the results are compared. The numerical results demonstrate the superiority of FA to HS and PSO.
A. Kaveh, N. Shamsapour, R. Sheikholeslami, M. Mashhadian,
Volume 2, Issue 4 (10-2012)
Abstract

This paper presents application of an improved Harmony Search (HS) technique and Charged System Search algorithm (CSS) to estimate transport energy demand in Iran, based on socio-economic indicators. The models are developed in two forms (exponential and linear) and applied to forecast transport energy demand in Iran. These models are developed to estimate the future energy demands based on population, gross domestic product (GDP), and the data of numbers of vehicles (VEH). Transport energy consumption in Iran is considered from 1968 to 2009 as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1968-2003) and partly for testing the models (2004-2009). Finally transport energy demand in Iran is forecasted up to the year 2020.
A. Csébfalvi , E. Szendrői ,
Volume 2, Issue 4 (10-2012)
Abstract

This paper presents an experimental investigation of the Sounds of Silence (SoS) harmony search metaheuristic for the multi-mode resource-constrained project scheduling problem (MRCPSP) using a pre-optimized starting repertoire. The presented algorithm is based on the time oriented version of the SoS harmony search metaheuristic developed by Csébfalvi et al. [1] for the single-mode resource-constrained project scheduling problem (RCPSP). The multi-mode SoS version exploits the fact that using a state-of-the art solver a small mixed integer linear programming problem (MILP) or a large linear programming problem (LP) can be solved within reasonable time. In order to illustrate the viability of the pre-optimized starting repertoire we present computational results for the hardest and largest MMLIB+ benchmark set developed by Van Peteghem and Vanhoucke [2]. The computational result reveals the fact, that the pre-optimized repertoire drastically increases the efficiency of the problem solvong process.
S. Carbas, M.p. Saka,
Volume 3, Issue 1 (3-2013)
Abstract

Many optimization techniques have been proposed since the inception of engineering optimization in 1960s. Traditional mathematical modeling-based approaches are incompetent to solve the engineering optimization problems, as these problems have complex system that involves large number of design variables as well as equality or inequality constraints. In order to overcome the various difficulties encountered in obtaining the solution of these problems, new techniques called metaheuristic algorithms are suggested. These techniques are numerical optimization algorithms that are based on a natural phenomenon. In this study, a state-of-art improved harmony search method with a new adaptive error strategy is proposed to handle the design constraints. Number of numerical examples is presented to demonstrate the efficiency of the proposed algorithm in solving engineering optimization problems.
S. Gholizadeh, R. Kamyab , H. Dadashi,
Volume 3, Issue 2 (6-2013)
Abstract

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative process needed to meet code requirements. In the PBDO procedure, the metaheuristics minimize the structural weight subjected to performance constraints on inter-story drift ratios at various performance levels. Two numerical examples are presented demonstrating the superiority of the PSO to the GA, ACO and HS metaheuristic algorithms.
H. Fattahi, M. A. Ebrahimi Farsangi, S. Shojaee, K. Nekooei , H. Mansouri,
Volume 3, Issue 2 (6-2013)
Abstract

An excavation damage zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. This paper presents an approach to build a model for the identification and classification of the EDZ. The Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linearity and high dimension. However, the practicability of the SVM is influenced by the difficulty of selecting appropriate SVM parameters. In this study, the proposed hybrid Harmony search (HS) with the SVM was applied for identification and classification of damaged zone, in which HS was used to determine the optimized free parameters of the SVM. For identification and classification of the EDZ, based upon the modulus of the deformation modulus and using the hybrid of HS with the SVM a model for the identification and classification of the EDZ was built. To illustrate the capability of the HS-SVM model defined, field data from a test gallery of the Gotvand dam, Iran were used. The results obtained indicate that the HS-SVM model can be used successfully for identification and classification of damaged zone around underground spaces.
A. Kaveh, B. Mirzaei, A. Jafarvand,
Volume 3, Issue 4 (10-2013)
Abstract

The objective of this paper is to present an optimal design for single-layer barrel vault frames via improved magnetic charged system search (IMCSS) and open application programming interface (OAPI). The IMCSS algorithm is utilized as the optimization algorithm and the OAPI is used as an interface tool between analysis software and the programming language. In the proposed algorithm, magnetic charged system search (MCSS) and improved harmony search (IHS) are utilized to achieve a good convergence and good solutions especially in final iterations. The results confirm the efficiency of OAPI as a powerful interface tool in the analysis process of barrel vault structures and also the ability of IMCSS algorithm in fast convergence and achieving optimal results.
S. Danka,
Volume 3, Issue 4 (10-2013)
Abstract

This paper, we presents a new primary-secondary-criteria scheduling model for resource-constrained project scheduling problem (RCPSP) with uncertain activity durations (UD) and cash flows (UC). The RCPSP-UD-UC approach producing a “robust” resource-feasible schedule immunized against uncertainties in the activity durations and which is on the sampling-based scenarios may be evaluated from a cost-oriented point of view. In the presented approach, it is assumed that each activity-duration and each cash flow value is an uncertain-but-bounded parameter, which is characterized by its optimistic and pessimistic estimations. The evaluation of a given robust schedule is based on the investigation of variability of the makespan as a primary and the net present value (NPV) as secondary criterion on the set of randomly generated scenarios given by a sampling-on-sampling-like process. Theoretically, the robust schedule-searching algorithm is formulated as a mixed integer linear programming problem, which is combined with a cost-oriented sampling-based approximation phase. In order to illustrate the essence of the proposed approach we present detailed computational results for a larger and very challenging project instance. A problem specific fast and efficient harmony search algorithm for large uncertain problems will be presented in a forthcoming paper.
S. Danka,
Volume 3, Issue 4 (10-2013)
Abstract

In this paper, we present a new idea for robust project scheduling combined with a cost-oriented uncertainty investigation. The result of the new approach is a makespan minimal robust proactive schedule, which is immune against the uncertainties in the activity durations and which can be evaluated from a cost-oriented point of view on the set of the uncertain-but-bounded duration and cost parameters using a sampling-based approximation. In this paper, we assume that the sources of uncertainty are the variability of the activity durations and the cash flow values, and present an appropriate hybrid method, which is a combination of mathematical programming, metaheuristic and sampling-based elements, to cope with this "uncertainty in uncertainty" like real problem.
R. Kamyab , E. Salajegheh,
Volume 4, Issue 2 (6-2014)
Abstract

This paper presents an efficient meta-heuristic algorithm for optimization of double-layer scallop domes subjected to earthquake loading. The optimization is performed by a combination of harmony search (HS) and firefly algorithm (FA). This new algorithm is called harmony search firefly algorithm (HSFA). The optimization task is achieved by taking into account geometrical and material nonlinearities. Operation of HSFA includes three phases. In the first phase, a preliminary optimization is accomplished using HS. In the second phase, an optimal initial population is produced using the first phase results. In the last phase, FA is employed to find optimum design using the produced optimal initial population. The optimum design obtained by HSFA is compared with those of HS and FA. It is demonstrated that the HSFA converges to better solution compared to the other algorithms.
H. Fattahi,
Volume 5, Issue 1 (1-2015)
Abstract

The slope stability analysis is routinely performed by engineers to estimate the stability of river training works, road embankments, embankment dams, excavations and retaining walls. This paper presents a new approach to build a model for the prediction of slope stability state. The support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linearity and high dimension. However, the practicability of the SVM is influenced by the difficulty of selecting appropriate SVM parameters. In this study, the proposed hybrid harmony search (HS) with SVM was applied for the prediction of slope stability state, in which HS was used to determine the optimized free parameters of the SVM. A dataset that includes 55 data points was applied in current study, while 45 data points (80%) were used for constructing the model and the remainder data points (10 data points) were used for assessment of degree of accuracy and robustness. The results obtained indicate that the SVM-HS model can be used successfully for the prediction of slope stability state for circular failure.
M. Shahrouzi, A. Meshkat-Dini , A. Azizi,
Volume 5, Issue 2 (3-2015)
Abstract

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 & 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.
H. Hatefi, A. Karamodin, S. Baygi,
Volume 8, Issue 1 (1-2018)
Abstract

The purpose of this research to present for the first time a practical plan to cost optimize the composite metal floor deck, so that the designer, by having the dimensions of the main beam framing, will be able to come to the most design including: the section of composite steel beam, the beam span, the thickness of metal deck sheets and the thickness of the concrete slab. The main method of optimization by using metaheuristic algorithm is harmony search which its objective function is equal with these costs: steel beam, metal deck and concrete slab in one square meter of the floor. The standards of this design is according to SDI code and the Iranian National Building Code, Part 10: Design and Construction of steel buildings -allowable stress method. In this design all the metal beam profiles are the common and practical sections in building industry. Also the chosen metal deck, by the brand name Sundeck 75, is the most economical metal deck produced in the country Iran which is invented by the writer 1 and has been confirmed by Road, housing and Development Research center. In the end to ensure all the results of the harmony search optimization, several questions has been optimized, so the comparison of its results with the harmony algorithm results indicates that the high convergence in this algorithm.


A. Kaveh, A. Dadras,
Volume 8, Issue 2 (8-2018)
Abstract

In this paper the performance of four well-known metaheuristics consisting of Artificial Bee Colony (ABC), Biogeographic Based Optimization (BBO), Harmony Search (HS) and Teaching Learning Based Optimization (TLBO) are investigated on optimal domain decomposition for parallel computing. A clique graph is used for transforming the connectivity of a finite element model (FEM) into that of the corresponding graph, and k-median approach is employed. The performance of these methods is investigated through four FE models with different topology and number of meshes. A comparison of the numerical results using different algorithms indicates, in most cases the BBO is capable of performing better or identical using less time with equal computational effort.
S. Dehghani Fordoei, S.a. Razavian Amrei, M. Eghbali, M. Sh. Nasrollah Beigi,
Volume 8, Issue 4 (10-2018)
Abstract

Vulnerability assessment of structures encounter many uncertainties like seismic excitations intensity and response of structures. The most common approach adopted to deal with these uncertainties is vulnerability assessment through fragility functions. Fragility functions exhibit the probability of exceeding a state namely performance-level as a function of seismic intensity. A common approach is finding some response points of the fragility function and then fitting a typical probability distribution like lognormal through curve fitting estimation techniques. Maximum-likelihood approach is a fitting method to find the probability distribution parameters. Performing this approach for distributions like lognormal which is defined by just two parameters are straight forward while for more complicated distribution which are based on additional characterizing parameters is not feasible, since this approach is based on minimizing an error function through classic mathematical approaches like calculating partial derivations. An applicable modification is to add an efficient optimization approach to determine maximum-likelihood function. In this article, an optimization algorithm is proposed with maximum-likelihood-estimation and the results indicate the efficiency and feasibility of future developments in finding the most appropriate fragility function.
R. Soofifard1, M. Khakzar Bafruei, M. Gharib,
Volume 8, Issue 4 (10-2018)
Abstract

Risks are natural and inherent characteristics of major projects. Risks are usually considered independently in analysis of risk responses. However, most risks are dependent on each other and independent risks are rare in the real world. This paper proposes a model for proper risk response selection from the responses portfolio with the purpose of optimization of defined criteria for projects. This research has taken into account the relationships between risk responses; especially the relationships between risks, which have been rarely considered in previous works. It must be pointed out that not considering or superficial evaluation of the interactions between risks and risk responses reduces the expected desirability and increases project execution costs. This model is capable of optimization of different criteria in the objective function based on the proposed projects. Multi-objective Harmony Search (MOHS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve this model and the numerical results obtained are analyzed. Finally, it was observed that ranges of objective functions in MOHS are better than those in NSGA-II.
D. Sedaghat Shayegan, A Lork, S.a.h. Hashemi,
Volume 9, Issue 3 (6-2019)
Abstract

In this paper, the optimum design of a reinforced concrete one-way ribbed slab, is presented via recently developed metaheuristic algorithm, namely, the Mouth Brooding Fish (MBF). Meta-heuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. The MBF algorithm simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. This algorithm uses the movement, dispersion and protection behavior of Mouth Brooding Fish as a pattern to find the best possible answer. The cost of the system is considered to be the objective function, and the design is based on the American Concrete Institute’s ACI 318-08 standard. The performance of this algorithm is compared with harmony search (HS), colliding bodies optimization (CBO), particle swarm optimization (PSO), democratic particle swarm optimization (DPSO), charged system search (CSS) and enhanced charged system search (ECSS). The numerical results demonstrate that the MBF algorithm is able to construct very promising results and has merits in solving challenging optimization problems.

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