Search

This study investigates the effect of superstructure configuration on the optimum design of slab on Precast I (PCI) girder bridges. For this purpose, more than 20,000 bridge cases of varying superstructure configurations are considered to investigate the effects of various superstructure parameters such as girder spacing, span length, slab thickness and girder types on the optimum design of slab on PCI girder bridges. PCI girders are designed conforming to the AASHTO LRFD for flexure using stress limits at the service limit state, then checked at ultimate for flexure and shear using factored loads at the strength limit state. A modified harmony search optimization algorithm is used to obtain optimum bridge design parameters using standard AASHTO PCI girders according to these AASHTO LRFD requirements. Those girders are designed taking into consideration geometrical constraints, stress constraints and constraints related to the conformity of the design with the AASHTO LRFD code. Various sensitivity analysis are performed to investigate the effect of different geometrical factors on the design of the girders, and easy-to-use design aids were developed. The outcomes of this study may facilitate the bridge engineers to choose optimum design parameters such as girder types and spacing as well as number strands for a certain span length before the design of slab on PCI girder bridges. [ABSTRACT FROM AUTHOR]

Accurate spatial estimation of precipitation is central to hydroclimatic studies. In this study, deterministic and probabilistic inverse distance weighting (IDW) methods were utilized to provide spatial estimations of precipitation. To improve their accuracy, a modified version of IDW was developed involving a separation of IDW weights in the X-Y plane and Z direction. Moreover, four different weight functions were used to investigate how changing the pattern of the weights relative to the distance and elevation affects the accuracy of the interpolation. Finally, four IDW variants were implemented for spatial modelling of precipitation in the Central Plateau watershed located in Iran, using monthly data from 582 raingauge stations during 2005 to 2015. Based on the statistics, the modified IDW methods were about 8% more accurate than the standard IDW methods. Additionally, correlated triple collocation (CTC) analysis showed that the modified models resulted in 12% smaller root mean square errors in comparison with the standard models. Performance of deterministic/probabilistic inverse distance weighting (IDW) models was enhanced by modifying the weight function. Precipitation datasets constructed by IDW variants were assessed point-wisely by statistical and uncertainty metrics through leave-one-out cross-validation (LOOCV) approach. To assess the datasets continuously, correlated triple collocation (CTC) method was utilized and error of the datasets was computed relative to the unknown truth in presence of TRMM and PERSIANN precipitation datasets. [ABSTRACT FROM AUTHOR]

Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset. [ABSTRACT FROM AUTHOR]

The challenges we face in today’s world are increasingly complex, and effectively managing uncertainty when modeling control problems can yield significant benefits. However, the complexity of these models often leads to higher computational costs. Therefore, the main contribution of this article is the use of the theory of shadowed type-2 fuzzy sets to address these challenges and to control the search space exploration in the harmony search algorithm by employing two alpha planes, and with this, it was possible to reduce the computational cost and obtain effective results. Furthermore, the application of this approach aims to find optimal parameters for the membership functions of a type-2 fuzzy controller and analyze its behavior. By adopting the proposed methodology, it becomes possible to minimize computational costs while still achieving feasible solutions for interval type-2 control problems. A key aspect is that symmetry is considered in the design of the controller to also obtain good results. To validate the effectiveness of the approach, extensive simulations were conducted with varying levels of noise introduced to the type-2 controller. This comprehensive analysis allowed for a thorough examination of the results obtained. The findings of the simulations are presented, showcasing the advantages of the proposed methodology. By incorporating noise into the system, it was observed that the objective function, in this case, the root mean square error (RMSE), was reduced. Moreover, the signal obtained with the presence of noise demonstrated a superior performance compared to the noise-free reference. In conclusion, the proposed approach of utilizing shadowed type-2 fuzzy systems, combined with the harmony search algorithm, offers a promising solution for managing complex control problems. By carefully analyzing the behavior of the system through simulations, it is evident that the inclusion of noise helps improve the system’s performance.

Smart factories in harsh large-scale environments are achieved by installation of group-based industrial wireless sensor networks (GIWSNs), in which a group of sensors are deployed on each machine target, for security and saving deployment time. In GIWSNs, enormous data for production (e.g., 3D digital twin and automated optical inspection images) and surveillance is transmitted frequently among multiple machines, and consumes huge energy. Furthermore, a real-world factory whose radio environment is interfered by mobile devices is dynamic and uncertain. Therefore, this paper investigates reliable energy-efficient routing for surveillance in dynamic uncertain GIWSNs, and further proposes a fuzzy improved global-best harmony search approach, where improved operators are integrated to efficiently explore the search space locally and globally; and a fuzzy evaluation scheme is employed to address uncertain factors. Through simulation under various parameter settings, this approach can find reliable routing in dynamic environments, and shows high performance as compared with other approaches. In addition, this approach can always find the reliable surveillance routing under various data amounts, while activating fewer crucial sensors to effectively reduce energy consumption. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) provides an infrastructure to constructing smart cities. Through the installed IoT sensors in the city, a large amount of information of the city is detected and collected in a cloud center, which provides residents to have immediate and convenient services. To achieve the goal of smart cities, the fundamental challenge has been to minimize the energy consumption of the IoT sensor network, which is involved with the deployment and sleep scheduling of IoT sensors. Most of the previous related works proposed two-stage approaches to address deployment and sleep scheduling problems of IoT sensors separately. However, the deployment at the first stage influences the optimality of the sleep scheduling at the second stage significantly. Consequently, this work jointly considers deployment and sleep scheduling of IoT sensors in a smart city. To improve the performance of sleep scheduling of IoT sensors, the deployment at the first stage concurrently considers a part of the optimality of later sleep scheduling, and it is optimized by the proposed improved geometric selective harmony search algorithm that incorporates crossover and dynamic schemes. The crossover scheme can effectively solve the problem of different decision variables in the solution representation; and the dynamic scheme can increase stability and diversity of searching for solutions. The performance of the proposed algorithm is evaluated by the simulation under various combinations of covering requirements, the number of sensors, and practical parameter settings. [ABSTRACT FROM AUTHOR]

In the context of this study, it is focused on the design of cantilever soldier piles under the concept of Pareto optimality with multiobjective analyses of cost and CO2 emission considering the change in the excavation depth, the shear strength parameters of the foundation soil strata, and the unit costs and unit emission amounts of structural materials. Considering this aim, the harmony search algorithm was used as a tool to achieve the integrated effects of the solution variants. The lateral response of the soil mass was determined based on the active Rankine earth pressure theory and the design process was shaped according to the beams on the elastic foundation soil assumption. Moreover, the specification envisaged by the American Concrete Institute (ACI 318-11) was used to control the structural requirements of the design. Pareto front graphs and also design charts were created to achieve the eco- and cost optimization, simultaneously, for the design with arbitrarily selected cases to compare the results of the multiobjective analysis to minimize both the cost and the CO2 emission. [ABSTRACT FROM AUTHOR]

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system ( DEED IWEv ) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEED IWEv model. [ABSTRACT FROM AUTHOR]

In the injection moulding process, a set-up containing positioning moulds, a cleaning raw material hopper and machine settings is required. The set-up is uncertain owing to random factors, such as the crew's skill levels and unexpected breakdowns. The general version of this problem is known as the single-machine scheduling problem with stochastic sequence-dependent set-up times. In this specialized form of the problem, the objective function is defined so as to minimize the total expected cost, which includes set-up, tardiness and earliness costs. Moreover, the effect of the client-dependent tardiness cost on the optimal machine schedule under uncertainty is explored. As a solution approach, a two-stage stochastic programming method is used. For the solution of large-size problems, the harmony search algorithm is proposed, and its performance is compared with the exact solution approach. Finally, the value of the stochastic solution is assessed by comparing the results with the deterministic approach. [ABSTRACT FROM AUTHOR]

Gold is a precious metal often used for investment, due to its cash-in ease and yearly value increase. This indicates that price forecasting is used to determine the prospect of future gold prices. Strong gold price forecasting is highly desired by investors to make decisions. That is why technical indicators are very important used for forecasting. By using technical indicators the information obtained can be more informative than using pure gold prices. One of the commonly used methods is Backpropagation (BP). BP has been shown to have good performance in dealing with nonlinear problems. However, due to the random determination of the parameters of neurons in the hidden layer BP requires a number of neurons in the hidden layer to get optimal results. Therefore, this study aims to analyze the optimization of Backpropagation (BP) through the Harmony Search (HS) algorithm by evaluating the use of relevant technical indicators for forecasting gold prices. In the HS-BP model, this method is used to determine input variables and neurons in the hidden layer. HS with the principle of musicians with the aim of finding the best harmony. This technique is used based on the results of the fitness function. In this research, the fitness function used is Mean Square Error (MSE). HS aims to optimize BP in such a way that the forecasting system provides the lowest MSE and improves the forecasting performance of gold prices. Based on this research, the input variables used are Moving Average, Relative Strength Index, and Bollinger Bands. Next, the selected variables and neurons are applied to the BP algorithm. Where the implementation uses gold closing price data for January 2020-2021. The results showed that the proposed method has better results in forecasting accuracy and convergence error. HS-BP provides a better level of gold price forecasting than the regular BP model. [ABSTRACT FROM AUTHOR]

The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. In this paper, the trajectory optimization of a three-stage launch vehicle in the powering phase subject to the sun-synchronous orbit is considered. To solve the optimal control problem, the Gauss pseudo-spectral method (GPM) is used to transform the optimization model to a nonlinear programming (NLP) problem and sequential quadratic programming is applied to find the optimal solution. However, the sensitivity of the initial guess may cost the solver significant time to do the Newton iteration or even lead to the local minimum. Aiming at this issue, a good initial guess generated by combination of harmony search and GPM is introduced to help the optimizer to faster and better solve the sun-synchronous orbit (SSO) trajectory optimization. Comparative simulation tests are conducted with the proposed algorithm and popular approaches, the results indicate that with the optimized initial guess, the proposed method could achieve better performance in terms of convergence ability and convergence rate. [ABSTRACT FROM AUTHOR]

Shear wave velocity (VS) is one of the most important parameters in deep and surface studies and the estimation of geotechnical design parameters. This parameter is widely utilized to determine permeability and porosity, lithology, rock mechanical parameters, and fracture assessment. However, measuring this important parameter is either impossible or difficult due to the challenges related to horizontal and deviation wells or the difficulty in reaching cores. Artificial Intelligence (AI) techniques, especially Machine Learning (ML), have emerged as efficient approaches for dealing with such challenges. Therefore, considering the advantage of the ML, the current research proposes a novel Fully-Self-Adaptive Harmony Search—Group Method of Data Handling (GMDH)-type neural network, named FSHS-GMDH, to estimate the VS parameter. In this way, the Harmony Memory Consideration Rate (HMCR) and Pitch Adjustment Rate (PAR) parameters are calculated automatically. A novel method is also introduced to adjust the value of the Bandwidth (BW) parameter based on the cosine wave and each decision variable values. In addition, a variable-size harmony memory is proposed to enhance both the diversification and intensification. Our proposed FSHS-GMDH algorithm quickly explores the problem space and exploits the best regions at the late iterations. This algorithm allows for the training of the prediction model based on the P-wave velocity (VP) and the bulk density of rock (RHOB). Applying the proposed algorithm to a carbonate petroleum reservoir in the Persian Gulf demonstrates that it is capable of accurately estimating the VS parameter better than state-of-the-art machine learning methods in terms of the coefficient of determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE). [ABSTRACT FROM AUTHOR]

In this study, a seismic isolator placed on the base of a structure was optimized under various earthquake records using an adaptive harmony search algorithm (AHS). As known, the base-isolation systems with very low stiffness provide a rigid response of superstructure, so it was assumed that the structure is rigid and the base-isolated structure can be considered as a single-degree of freedom structure. By using this assumption, an optimization method that is independent of structural properties but specific to the chosen earthquake excitation set is proposed. By taking three different damping ratio limits and isolator displacement limits, the isolator period and damping ratio were optimized so that the acceleration of the structure was minimized for nine cases. In the critical seismic analysis performed with optimum isolator parameters, the results obtained for different damping ratios and isolator periods were compared. From the results, it is determined that isolators with low damping ratios require more ductility, and as the damping ratio increases, further restriction of the movement of the isolator increases the control efficiency. Thus, it is revealed that increasing the ductility of the isolator is effective in reducing the total acceleration in the structure. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) energy has met great attention in the electrical power generation field for its many advantages. The connection of the PV systems to the electrical network is performed through DC-DC converter and DC-AC inverter. This paper aims at enhancing the integration of the PV systems into the electrical network through controlling both the converter and the inverter. An adaptive reference PI (ARPI) controller for the inverter is introduced to enhance the system performance through supporting low voltage ride through (LVRT) capability, and smoothing the PV generated power fluctuations during variable environmental conditions. While the converter is used to achieve the maximum power point tracking (MPPT) of the PV system-based perturb and observe (P&O) algorithm at different environmental conditions. A comparison between the system performance using the proposed ARPI and an optimal PI controller optimized by harmony search (HS) is introduced to test the effectiveness of the proposed strategy.

It is well known that thermal conductivity measurement is a challenging task, due to the weaknesses of the traditional methods, such as the high cost, complex data analysis, and limitations of sample size. Nowadays, the requirement of quality of life and tightening energy efficiency regulations of buildings promote the demand for new construction materials. However, limited by the size and inhomogeneous structure, the thermal conductivity measurement of wall samples becomes a demanding topic. Additionally, we find the thermal parameter values of the samples measured in the laboratory are different from those obtained by theoretical computation. In this paper, a novel signal-transmissive wall is designed to provide the problem solving of signal connectivity in 5G. We further propose a new thermal conductivity predictor based on the Harmony Search (HS) algorithm to estimate the thermal properties of laboratory-made wall samples. The advantages of our approach over the conventional methods are simplicity and robustness, which can be generalized to a wide range of solid samples in the laboratory measurement.

The operation of dams can have a significant downstream impact, causing major flood damage. For efficient dam operation, it is essential to accurately predict the inflow using deep learning such as a long short-term memory (LSTM). The modified LSTM (MLSTM) with new optimizers combined with a metaheuristic optimization algorithm was suggested. The MLSTM were trained using rainfall, water level, and discharge of the Yongdam Dam from 2010 to 2019 as input data and the inflow of the Daecheong Dam as output data. Normalization and time lagged cross correlation (TLCC) were applied as data preprocessing. The inflow of the Daecheong Dam in 2020 was predicted using rainfall, water level, and discharge of the Yongdam Dam. Compared to LSTM, MLSTM showed an accuracy improvement of about 63.85–83.34% based on the mean square error (MSE), which is a loss function. The results of proactive dam operation were calculated based on the predicted inflow considering TLCC. Compared to the existing operation, the peak water level of the reservoir was reduced from 77.54 m to 77.17 m, and the resilience was improved from 0.9225 to 0.9623. New dam operation can maintain low water levels of reservoir and improve resilience to prevent flood damage. • MLSTM using new combined optimizers with harmony search was developed. • Inflow of Daecheong Dam was predicted based on various data using MLSTM. • The predicted inflow using MLSTM was compared with the observed inflow. • MLSTM was compared with LSTM by evaluating the ranks of five indicators. • A new dam operation method using predicted inflow maintained low water level of reservoir and improved resilience. [ABSTRACT FROM AUTHOR]

Recently, due to extreme climate change, damage from flooding has been increasing; however, water shortages are being announced simultaneously. Moreover, the water distribution system’s ability to supply consumers is being overwhelmed because of urbanization, population concentration, and increases in water consumption. For this reason, to solve the water shortage problem, water reuse technologies are developing and improving that perform simple chemical treatment processes to reuse water for flushing toilets, washing, gardening, etc. but not as drinking water. However, most water reuse systems are designed and operated as independent systems, such as reusing water used in individual buildings or using rainwater. Therefore, this study develops an optimal design for the combined water systems, which is modeling and designing water distribution systems, urban drainage systems, and water reuse systems simultaneously to solve the water shortage and reduce flooding damage. To consider the combined water systems design, the existing water distribution system (WDS) demand is divided into drinking water and other uses, and the resource of other water is assumed by the rainwater storage tank for covering the amount of exceeding precipitation. To derive optimal design solutions for the combined three water systems, single- and multi-objective optimization techniques are applied considering various design criteria (i.e., construction cost, system resilience, and flooding volume on the exceeding design rainfall intensity). The developed combining water system design techniques could be used to create designs that solve the problems of medium and long-term water shortages and sustainable water systems development.

CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy.

Nowadays, most mobile robot applications use two-dimensional LiDAR for indoor mapping, navigation, and low-level scene segmentation. However, single data type maps are not enough in a six degree of freedom world. Multi-LiDAR sensor fusion increments the capability of robots to map on different levels the surrounding environment. It exploits the benefits of several data types, counteracting the cons of each of the sensors. This research introduces several techniques to achieve mapping and navigation through indoor environments. First, a scan matching algorithm based on ICP with distance threshold association counter is used as a multi-objective-like fitness function. Then, with Harmony Search, results are optimized without any previous initial guess or odometry. A global map is then built during SLAM, reducing the accumulated error and demonstrating better results than solo odometry LiDAR matching. As a novelty, both algorithms are implemented in 2D and 3D mapping, overlapping the resulting maps to fuse geometrical information at different heights. Finally, a room segmentation procedure is proposed by analyzing this information, avoiding occlusions that appear in 2D maps, and proving the benefits by implementing a door recognition system. Experiments are conducted in both simulated and real scenarios, proving the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing allocates virtual resources dynamically on user's demand. The sudden rise of data storage and computation in the cloud computing environment may cause an imbalanced workload distribution. As a result, job completion time will be higher in overloaded servers than the underloaded servers in the same environment. Distributing load fairly in the cloud is a crucial challenge. Traditionally, load balancing is used to distribute the workload among multiple servers to overcome the overloading and underloading of servers. This article presents a novel load balancing approach for cloud computing using improved gray wolf optimization algorithm. We compare our approach with harmony search algorithm, artificial bee colony algorithm, particle swarm optimization, and gray wolf optimization algorithms. Results of simulation are encouraging with improved system performance and fair utilization of resources. [ABSTRACT FROM AUTHOR]

Cost and CO2 are two factors in the optimum design of structures. This study proposes a modified harmony search methodology for optimization of reinforced concrete beams with minimum CO2 emissions. The optimum design was developed in detail by considering all possible combinations of variable loads, including dynamic force resulting from earthquake motion. Moreover, time-history analyses were performed, and requirements of the ACI-318 building code were considered in the reinforced concrete design. The results show that the optimum design based on CO2 emission minimization is greatly different from the optimum cost design results. According to these results, using recycled members provides a sustainable design. [ABSTRACT FROM AUTHOR]

Focusing on a cellular manufacturing system with inter-cell operations, a new scheduling framework is proposed in which a novel mathematical model is provided considering the cell scheduling sequence and an extended disjunctive graph model is established with multi-cell. In addition, an improved harmony search algorithm is proposed, comparison experiments show that the proposed solution can obtain the best results in most cases. To enhance further the generalizability of our strategy, an adaptive neuro-fuzzy inference system (ANFIS) structure is generated. ANFIS is based on the improved harmony search algorithm and takes the cell scheduling properties as input and the results of our proposed solution as output. Then, the optimal ANFIS structure is obtained by comparing different ANFIS structures. Finally, the ANFIS-predicted values are compared with those of the proposed solution and get a high fitting degree, which verifies the validity of the ANFIS structure. [ABSTRACT FROM AUTHOR]

Harmony Search Algorithm (HS) is a new meta-heuristic algorithm based on music creation process. The algorithm has been widely used in many fields because of its simple and flexible structure and fewer controlled parameters. However, HS is easy to be affected by the initial harmony memory and falls into local optimization. In addition, there are also problems, such as low searching accuracy and slow convergence speed of the optimal solution. Therefore, this paper proposes a hybrid harmony search algorithm based on firefly algorithm and boltzmann machine (HSFA-BM). First, in order to avoid the possibility of harmony search algorithm falling into local optimum, boltzmann machine (BM) mechanism is used to update the harmony memory in this paper. Second, in order to enhance the ability of the algorithm to exploit optimal solutions, this paper uses the movement mode of fireflies to generate new solutions. The improved algorithm enhances the ability of global optimization and search precision, and speeds up the convergence speed. In this paper, six classical test functions are used to test the effectiveness of the algorithm, and a very obvious optimization result is obtained. [ABSTRACT FROM AUTHOR]

In this study, the tuned liquid damper (TLD) device was optimized by the harmony search (HS) and adaptive harmony search algorithms (AHS). Using the harmony search algorithm, seismic excitations were directed at single and ten-story structures, and TLD parameters were optimized to minimize building movement. To improve design parameters, the optimization process was repeated by adapting the design factors of the harmony search algorithm. For this purpose, both the harmony memory consideration ratio (HMCR) and fret width (FW) were gradually reduced by providing an initial value, and optimum algorithm parameters were obtained. As a result of both optimizations, in a critical seismic analysis, the displacements of the adaptive harmony search showed smaller means and standard deviations than those of the classical harmony search. [ABSTRACT FROM AUTHOR]

This study focuses on tuned liquid dampers (TLDs) using liquids with different characteristics optimized with the adaptive harmony search algorithm (AHS). TLDs utilize the characteristic features of the liquid to absorb the dynamic forces entering the structure and benefit from the sloshing movement and the spring stiffness created by the liquid mass. TLDs have been optimized to investigate the effect of liquid characteristics on the control by analyzing various liquids. For optimization, the memory consideration ratio (HMCR) and fret width (FW) values were adapted from the classical harmony search (HS) algorithm parameters. The TLDs were used on three types of structure models, such as single-story, 10, and 40 stories. The contribution of the liquid characteristics to the damping performance was investigated by optimizing the minimum displacement under seismic excitation. According to the results, it was understood that the liquid density and kinematic viscosity do not affect single-story structures alone. However, two characteristic features should be evaluated together. As the structure mass increases, the viscosity and density become more prominent. [ABSTRACT FROM AUTHOR]

A computer network system is a complex system with a great number of dynamic components. There are many devices in the system, such as computers, routers, lines, hubs, and switches. In addition to these hardware systems, many protocols are integrated to set the rules and provide the way of communication. Due to the nature of the system, it is hard to formulate and solve problems analytically without making any assumptions. One of the prominent problems that occur in computer systems is the line capacity assignment problem. In the previous mathematical models, message routes were predetermined and the dynamic nature of the system was neglected. This study deals with the line capacity assignment problem under a dynamically routing policy. Four different computer network topologies are used and solved by two heuristic algorithms via simulation. A dynamic search approach based on the occupancy rate of lines is used to define the consecutive routes of messages. The performances of harmony search and genetic algorithms via simulation are compared with the results of OptQuest, one of the optimization packet programs embedded in simulation software Arena®. [ABSTRACT FROM AUTHOR]