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Showing 27 results for Genetic Algorithm

M.h. Afshar, M.r. Ghasemi,
Volume 3, Issue 2 (6-2005)
Abstract

An efficient selection operator for use in genetic search of pipe networks optimal design is introduced in this paper. The proposed selection scheme is the superior member of a family of improved selection operators developed in an attempt to more closely simulate the main features of the natural mating process which is not reflected in existing selection schemes. The mating process occurring in the nature exhibits two distinct features. First, there is a competition between phenotypes looking for the fittest possible mate which usually ends up with choosing a mate with more or less the same fitness. Second, and more importantly, the search for a mate is often confined to a community of phenotypes rather than the whole population. Four different selection operators simulating these features in a random and pre-determined manner are developed and tested. All the selection schemes exhibit good convergence characteristics, in particular the one in which both the size of the sub-community and the pair of the mates in the sub-community are determined randomly. The efficiency of the proposed selection operator is shown by applying the method for the optimal design of three well-known benchmark networks, namely two-loop, Hanoi and New-York networks. The proposed scheme produces results comparable to the best results presented in the literature with much less computational effort
Kaveh A., Shahrouzi M.,
Volume 3, Issue 3 (9-2005)
Abstract

Genetic Algorithm is known as a generalized method of stochastic search and has been successfully applied to various types of optimization problems. By GA s it is expected to improve the solution at the expense of additional computational effort. One of the key points which controls the accuracy and convergence rate of such a process is the selected method of coding/decoding of the original problem variables and the discrete feasibility space to be searched by GAS. In this paper, a direct index coding (DIC) is developed and utilized for the discrete sizing optimization of structures. The GA operators are specialized and adopted for this kind of encoded chromosomes and are compared to those of traditional GA S. The well-known lO-bar truss example from literature is treated here as a comparison benchmark, and the outstanding computational efficiency and stability of the proposed method is illustrated. The application of the proposed encoding method is not limited to truss structures and can also be directly applied to frame sizing problems.
Sh. Afandizadeh Zargari, R. Taromi,
Volume 4, Issue 3 (9-2006)
Abstract

Optimization is an important methodology for activities in planning and design. The transportation designers are able to introduce better projects when they can save time and cost of travel for project by optimization methods. Most of the optimization problems in engineering are more complicated than they can be solved by custom optimization methods. The most common and available methods are heuristic methods. In these methods, the answer will be close to the optimum answer but it isn’t the exact one. For achieving more accuracy, more time has been spent. In fact, the accuracy of response will vary based on the time spent. In this research, using the generic algorithms, one of the most effective heuristic algorithms, a method of optimization for urban streets direction will be introduced. Therefore model of decision making in considered one way – two way streets is developed. The efficiency of model in Qazvin network is shown and the results compared whit the current situation as case study. The objective function of the research is to minimize the total travel time for all users, which is one of the most used in urban networks objectives.
Hon.m. Asce, M.r. Jalali, A. Afshar, M.a. Mariño,
Volume 5, Issue 4 (12-2007)
Abstract

Through a collection of cooperative agents called ants, the near optimal solution to the multi-reservoir operation problem may be effectively achieved employing Ant Colony Optimization Algorithms (ACOAs). The problem is approached by considering a finite operating horizon, classifying the possible releases from the reservoir(s) into pre-determined intervals, and projecting the problem on a graph. By defining an optimality criterion, the combination of desirable releases from the reservoirs or operating policy is determined. To minimize the possibility of premature convergence to a local optimum, a combination of Pheromone Re-Initiation (PRI) and Partial Path Replacement (PPR) mechanisms are presented and their effects have been tested in a benchmark, nonlinear, and multimodal mathematical function. The finalized model is then applied to develop an optimum operating policy for a single reservoir and a benchmark four-reservoir operation problem. Integration of these mechanisms improves the final result, as well as initial and final rate of convergence. In the benchmark Ackley function minimization problem, after 410 iterations, PRI mechanism improved the final solution by 97 percent and the combination of PRI and PPR mechanisms reduced final result to global optimum. As expected in the single-reservoir problem, with a continuous search space, a nonlinear programming (NLP) approach performed better than ACOAs employing a discretized search space on the decision variable (reservoir release). As the complexity of the system increases, the definition of an appropriate heuristic function becomes more and more difficult this may provide wrong initial sight or vision to the ants. By assigning a minimum weight to the exploitation term in a transition rule, the best result is obtained. In a benchmark 4-reservoir problem, a very low standard deviation is achieved for 10 different runs and it is considered as an indication of low diversity of the results. In 2 out of 10 runs, the global optimal solution is obtained, where in the other 8 runs results are as close as 99.8 percent of the global solution. Results and execution time compare well with those of well developed genetic algorithms (GAs).
M.h. Afshar, R. Rajabpour,
Volume 5, Issue 4 (12-2007)
Abstract

This paper presents a relatively new management model for the optimal design and operation of irrigation water pumping systems. The model makes use of the newly introduced particle swarm optimization algorithm. A two step optimization model is developed and solved with the particle swarm optimization method. The model first carries out an exhaustive enumeration search for all feasible sets of pump combinations able to cope with a given demand curve over the required period. The particle swarm optimization algorithm is then called in to search for optimal operation of each set. Having solved the operation problem of all feasible sets, one can calculate the total cost of operation and depreciation of initial investment for all the sets and the optimal set and the corresponding operating policy is determined. The proposed model is applied to the design and operation of a real-world irrigation pumping system and the results are presented and compared with those of a genetic algorithm. The results indicate that the proposed mode in conjunction with the particle swarm optimization algorithm is a versatile management model for the design and operation of real-world irrigation pumping systems.
Abbas Afshar, S. Ali Zahraei, M. A. Marino,
Volume 6, Issue 1 (3-2008)
Abstract

In a large scale cyclic storage system ,as the number of rule parameters and/or number of operating period increase, general purpose gradient-based NLP solvers and/or genetic algorithms may loose their merits in finding optimally feasible solution to the problem. In these cases hybrid GA which decomposes the main problem into two manageable sub-problems with an iterative scheme between GA and LP solvers may be considered as a sound alternative This research develops a hybrid GA-LP algorithm to optimally design and operate a nonlinear, non-convex, and large scale lumped cyclic storage system. For optimal operation of the system a set of operating rules are derived for joint utilization of surface and groundwater storage capacities to meet a predefined demand with minimal construction and operation cost over a 20 seasonal planning period. Performance of the proposed model is compared with a non-cyclic storage system. The management model minimizes the present value of the design and operation cost of the cyclic and non-cyclic systems under specified and governing constraints, employing the developed GA-LP hybrid model. Results show that cyclic storage dominates non-cyclic storage system both in cost and operation flexibility.
Kourosh Behzadian, Abdollah Ardeshir, Zoran Kapelan, Dragan Savic,
Volume 6, Issue 1 (3-2008)
Abstract

A novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of sampling devices as a surrogate of sampling design cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal sampling locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic sampling design optimisation problem is solved for a number of randomly generated calibration model parameter samples.The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.
Shahriar Afandizadeh, Jalil Kianfar,
Volume 7, Issue 1 (3-2009)
Abstract

This paper presents a hybrid approach to developing a short-term traffic flow prediction model. In this

approach a primary model is synthesized based on Neural Networks and then the model structure is optimized through

Genetic Algorithm. The proposed approach is applied to a rural highway, Ghazvin-Rasht Road in Iran. The obtained

results are acceptable and indicate that the proposed approach can improve model accuracy while reducing model

structure complexity. Minimum achieved prediction r2 is 0.73 and number of connection links at least reduced 20%

as a result of optimization.


A. Kaveh, N. Farhoodi,
Volume 8, Issue 3 (9-2010)
Abstract

In this paper, the problem of layout optimization for X-bracing of steel frames is studied using the ant system (AS). A new design method is employed to share the gravity and the lateral loads between the main frame and the bracings according to the requirements of the IBC2006 code. An algorithm is developed which is called optimum steel designer (OSD). An optimization method based on an approximate analysis is also developed for layout optimization of braced frames. This method is called the approximate optimum steel designer (AOSD) and uses a simple deterministic optimization algorithm leading to the optimum patterns and it is much faster than the OSD. Several numerical examples are treated by the proposed methods. Efficiency and accuracy of the methods are then discussed. A comparison is also made with Genetic algorithm for one of the frames.


Sh. Afandizadeh, M. Yadak, N. Kalantar,
Volume 9, Issue 1 (3-2011)
Abstract

The congestion pricing has been discussed as a practical tool for traffic management on urban transport networks. The traffic congestion is defined as an external diseconomy on the network in transport economics. It has been proposed that the congestion pricing would be used to reduce the traffic on the network. This paper investigates the cordon-based second-best congestion-pricing problems on road networks, including optimal selection of both toll levels and toll locations. A road network is viewed as a directed graph and the cutest concept in graph theory is used to describe the mathematical properties of a toll cordon by examining the incidence matrix of the network. Maximization of social welfare is sought subject to the elastic-demand traffic equilibrium constraint. A mathematical programming model with mixed (integer and continuous) variables is formulated and solved by use of two genetic algorithms for simultaneous determination of the toll levels and cordon location on the networks. The model and algorithm are demonstrated in the road network of Mashhad CBD.
M.h. Sebt, A. Yousefzadeh, M. Tehranizadeh,
Volume 9, Issue 1 (3-2011)
Abstract

In this paper, the optimal location and characteristics of TADAS dampers in moment resisting steel structures, considering the application of minimum number of TADAS dampers in a building as an objective function and the restriction for destruction of main members is studied. Genetic algorithm in first generation randomly produces different chromosomes representing unique TADAS dampers distributions in structure and the structure corresponding to each chromosome is time history analyzed. Then the damage index for each member and the average weighted damage index for all members are determined. Genetic algorithm evaluates the fitness of each chromosome then selection and crossover as logical operators and mutation as random operator effect the current generation's chromosomes according to their fitness and new chromosomes are generated. Accordingly, successive generations are reproduced in the same way until the convergence condition is fulfilled in final generation and four distributions are suggested as better options. Since these proposed distributions are selected under the one earthquake, therefore, it is better that the four new structures are cost-benefit analyzed in different earthquakes. Finally, the optimal placement for dampers is compared and selected based on a benefit to cost ratio, drift stories and the number of different TADAS types of such structures. The increase in amount of energy dissipated via dampers located in different floors as well as the status of plastic hinges in main members of the structure strengthened with optimum option are the proof of the optimal placement and suitable characteristics for dampers.


K. Behzadian, M. Alimohammadnejad, A. Ardeshir, H. Vasheghani, F. Jalilsani,
Volume 10, Issue 1 (3-2012)
Abstract

Compared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost

solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points

of a water distribution system (WDS). However, control of trihalomethane (THM) formation as a potentially carcinogenic

disinfection by-product (DBP) within a WDS has still remained as a water quality problem. This paper presents a two-phase

approach of multi-objective booster disinfection in which both chlorine residuals and THM formation are concurrently optimized

in a WDS. In the first phase, a booster disinfection system is formulated as a multi-objective optimization problem in which the

location of booster stations is determined. The objectives are defined as to maximize the volumetric discharge with appropriate

levels of disinfectant residuals throughout all demand nodes and to minimize the total mass of disinfectant applied with a specified

number of booster stations. The most frequently selected locations for installing booster disinfection stations are selected for the

second phase, in which another two-objective optimization problem is defined. The objectives in the second problem are to

minimize the volumetric discharge avoiding THM maximum levels and to maximize the volumetric discharge with standard levels

of disinfectant residuals. For each point on the resulted trade-off curve between the water quality objectives optimal scheduling of

chlorination injected at each booster station is obtained. Both optimization problems used NSGA-II algorithm as a multi-objective

genetic algorithm, coupled with EPANET as a hydraulic simulation model. The optimization problems are tested for different

numbers of booster chlorination stations in a real case WDS. As a result, this type of multi-objective optimization model can

explicitly give the decision makers the optimal location and scheduling of booster disinfection systems with respect to the tradeoff

between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels.


E. Sanaei, M. Babaei,
Volume 10, Issue 3 (9-2012)
Abstract

Due to the algorithmic simplicity, cellular automata (CA) models are useful and simple methods in structural optimization. In

this paper, a cellular-automaton-based algorithm is presented for simultaneous shape and topology optimization of continuum

structures, using five-step optimization procedure. Two objective functions are considered and the optimization process is

converted to the single objective optimization problem (SOOP) using weighted sum method (WSM). A novel triangle

neighborhood is proposed and the design domain is divided into small triangle elements, considering each cell as the finite

element. The finite element formulation for constant strain triangles using three-node triangular elements is developed in this

article. Topological parameters and shape of the design space are taken as the design variables, which for the purpose of this

paper are continuous variables. The paper reports the results of several design experiments, comparing them with the currently

available results obtained by CA and genetic algorithm in the literature. The outcomes of the developed scheme show the

accuracy and efficiency of the method as well as its timesaving behavior in achieving better results


A. Kaveh, O. Sabzi,
Volume 10, Issue 3 (9-2012)
Abstract

In this paper a discrete Big Bang-Big Crunch algorithm is applied to optimal design of reinforced concrete planar frames under

the gravity and lateral loads. Optimization is based on ACI 318-08 code. Columns are assumed to resist axial loads and bending

moments, while beams resist only bending moments. Second-order effects are also considered for the compression members, and

columns are checked for their slenderness and their end moments are magnified when necessary. The main aim of the BB-BC

process is to minimize the cost of material and construction of the reinforced concrete frames under the applied loads such that

the strength requirements of the ACI 318 code are fulfilled. In the process of optimization, the cost per unit length of the sections

is used for the formation of the subsequent generation. Three bending frames are optimized using BB-BC and the results are

compared to those of the genetic algorithm.


Sh. Afandizadeh, H. Khaksar, N. Kalantari,
Volume 11, Issue 1 (3-2013)
Abstract

In this paper, a new approach was presented for bus network design which took the effects of three out of four stages of the bus planning process into account. The presented model consisted of three majors steps 1- Network Design Procedure (NDP), 2- Frequency Determination and Assignment Procedure (FDAP), and 3- Network Evaluation Procedure (NEP). Genetic Algorithm (GA) was utilized to solve this problem since it was capable of solving large and complex problems. Optimization of bus assignment at depots is another important issue in bus system planning process which was considered in the presented model. In fact, the present model was tested on Mandl’s bus network which was a benchmark in Swiss network and was initially employed by Mandl and later by Baaj, Mahmassani, Kidwai, Chakroborty and Zhao. Several comparisons indicated that the model presented in this paper was superior to the previous models. Meanwhile, none of the previous approaches optimized depots assignment. Afterwards, sensitivity analysis on GA parameters was done and calculation times were presented. Subsequently the proposed model was evaluated thus, Mashhad bus network was designed using the methodology of the presented model.
H. Ziari, H. Divandari,
Volume 11, Issue 2 (6-2013)
Abstract

Pavement permanent deformations due to lack of shear strength in mixture are a major reason of rutting. Any simple test to determine mixtures resistance to permanent deformation isn’t distinguished in the 1st level of SUPERPAVE mix design method and prevalent methods for evaluating mixture rut resistance are expensive and time-consuming. Two aggregate types, gradations, asphalt cements and filler types were used in this research to present a prediction model for rutting based on flow number. A mathematical model to estimate flow number of dynamic creep test was developed using model parameters and gyratory compaction slope. The model is validated using Neural Network and Genetic Algorithm and makes it possible to evaluate mixtures shear strength while optimum asphalt content is being determined in laboratory. So not only there is no need to expensive test instruments of rutting or dynamic creep but a remarkable time saving in mix design procedure is achievable.
M. H. Sebt, M. H. Fazel Zarandi, Y. Alipouri,
Volume 11, Issue 3 (9-2013)
Abstract

Resource-Constrained Project Scheduling Problem (RCPSP) is one of the most popular problems in the scheduling phase of any project. This paper tackles the RCPSP in which activity durations can vary within their certain ranges such as RCPSP with variable activity durations. In this paper, we have attempted to find the most suitable hybridization of GA variants to solve the mentioned problem. For this reason, three GA variants (Standard GA, Stud GA and Jumping Gene) were utilized for first GA, and two GA variants (Standard GA, Stud GA) for the second one, and their hybridizations were compared. For this purpose, several comparisons of the following hybridizations of GAs are performed: Standard-Standard GA, Standard-Stud GA, Stud-Standard GA, Stud-Stud GA, Jumping Gene-Standard GA, and Jumping Gene-Stud GA. Simulation results show that implementing Stud-Stud GA hybridization to solve this problem will cause convergence on the minimum project makespan, faster and more accurate than other hybrids. The robustness of the Stud GA in solving the well-known benchmarking RCPSP problems with deterministic activity durations is also analyzed.
S. Soudmand, M. Ghatee, S. M. Hashemi,
Volume 11, Issue 4 (12-2013)
Abstract

This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal toll prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the tolls of congested links can be derived by a bi-level fuzzy programming problem. The objective function of the upper level problem is to minimize the difference between current LOS and desired LOS of links. In this level, to find optimal toll, a simulated annealing algorithm is used. The lower level problem is a fuzzy flow estimator model with fuzzy link costs. Applying a famous defuzzification function, a real-valued multi-commodity flow problem can be obtained. Then a polynomial time interior point algorithm is proposed to find the optimal solution regarding to the estimated flows. In pricing process, by imposing cost on some links with LOS F or E, users incline to use other links with better LOS and less cost. During the iteration of SA algorithm, the LOS of a lot of links gradually closes to their desired values and so the algorithm decreases the number of links with LOS worse than desirable LOS. Sioux Falls network is considered to illustrate the performance of SA-IP method on congestion pricing based on different LOS. In this pilot, after toll pricing, the number of links with LOS D, E and F are reduced and LOS of a great number of links becomes C. Also the value of objective function improves 65.97% after toll pricing process. It is shown optimal toll for considerable network is 5 dollar and by imposing higher toll, objective function will be worse.
M. Abbasi, A. H. Davaei Markazi,
Volume 12, Issue 1 (3-2014)
Abstract

An important factor in the design and implementation of structural control strategies is the number and placement of actuators. By employing optimally-located actuators, the effectiveness of control system increases, while with an optimal number of actuators, an acceptable level of performance can be achieved with fewer actuators. The method proposed in this paper, simultaneously determines the number and location of actuators, installed in a building, in an optimal sense. In particular, a genetic algorithm which minimizes a suitably defined structural damage index is introduced and applied to a well-known nonlinear model of a 20-story benchmark building. It is shown in the paper that an equal damage protection, compared to the work of other researchers, can be achieved with fewer numbers of optimally placed actuators. This result can be important from economic point of view. However, the attempt to minimize one performance index has negative effect on the others. To cope with this problem to some extent, the proposed genetic methodology has been modified to be applied in a multi-objective optimization problem.
A. Kaveh, A. Nasrolahi,
Volume 12, Issue 1 (3-2014)
Abstract

In this paper, a new enhanced version of the Particle Swarm Optimization (PSO) is presented. An important modification is made by adding probabilistic functions into PSO, and it is named Probabilistic Particle Swarm Optimization (PPSO). Since the variation of the velocity of particles in PSO constitutes its search engine, it should provide two phases of optimization process which are: exploration and exploitation. However, this aim is unachievable due to the lack of balanced particles’ velocity formula in the PSO. The main feature presented in the study is the introduction of a probabilistic scheme for updating the velocity of each particle. The Probabilistic Particle Swarm Optimization (PPSO) formulation thus developed allows us to find the best sequence of the exploration and exploitation phases entailed by the optimization search process. The validity of the present approach is demonstrated by solving three classical sizing optimization problems of spatial truss structures.

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