particle swarm algorithm

Guyed masts are slender, tall structures widely used to support various types of antennas in the field of telecommunications. These structures consist of a tall vertical mast laterally supported at several levels along its height by sets of inclined pre-tensioned cables. It is well known that natural frequencies are fundamental parameters that affect the dynamic behavior of structures. Optimizing natural frequencies of Guyed masts is an important issue in structural engineering. The main objective of this study presented in this paper is to obtain a design that will maximize the frequency of the Guyed masts. Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, three population-based meta-heuristic algorithms are employed for frequency optimization of Guyed masts. These algorithms consist of the Genetic Algorithm (GA), Firefly Algorithm (FA) and Particle Swarm Optimization Algorithm (PSO) aims to maximize the natural frequencies of Guyed masts while satisfying their constraints. The study on optimizing the position of the cable anchorages along the pole of a guyed tower using algorithms demonstrates that the optimal values for these angles are approximately 35 degrees without pretension forces in the guys. Additionally, the optimum cable angle is 75 degrees when subjected to prestressed guy cables.

The goal of any structural design process is to produce a safe design that meets all the design codes requirements, while trying to minimize the cost of the design. In Civil Engineering Structures, majorly cost is the objective of optimization of the structure. In most cases, the material cost is included in objective function. In some cases, construction cost is also included in objective function. The objective of the optimization is to minimize the cost of the Structures while satisfying strength and serviceability constraints. In order to investigate the efficiency and accuracy of the modern optimization methods in comparison with classical methods, an elevated INTZE tank is primarily designed based on the recommendations of Indian code and then optimized using Genetic Algorithm (GA), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO). objective function is the material cost of the tank which is the function of the design variables. The total material cost of the tank can be expressed as the sum of the cost of concrete, reinforcement and cost of formwork. Design constraints of the problem also include the stresses of the structure members and the limitations of the volume and geometric dimensions of the water tank. All constraints were satisfied and the stresses and stability constraints were in acceptable ranges.

To reduce the seismic response of steel frames, energy dissipation devices can be placed at connections. These connections can be modeled as a rotational spring and damper in parallel. In this paper, an attempt is made to estimate the best distribution of the connections, by time-history analysis and optimization operation. Although in the previous studies, these connections were distributed uniformly, in this research the combination of these connections in rigid frames is proposed. Two 9 and 20-story frames with sections and dimensions based on SAC benchmark structures are studied. Seismic performance of optimized structures with connections equipped with dampers and rigid connections is evaluated and compared with that of the moment frame with uniformly placed such connections. It is observed that performance of hybrid structures, despite having less dampers in connections, is much better than the structure with uniform distribution of this type of connections. On the other hand, linear and nonlinear behavior of elements and connections in structure is developed. Also, in optimal condition, 62 and 68% of the connections in linear and 58 and 61% in nonlinear behavior have been equipped with dampers respectively for 9 and 20-story structures.

In this paper, Hamiltonian cycle was used in a standard and theoretical problem called TSP as well as a practical problem called finding the shortest Hamiltonian cycle to cover all provinces of Iran. These kinds of problems can be solved using swarm intelligence algorithms derived from natural, environmental and social factors. Accordingly, the PSO Algorithm, which is one of the algorithms of swarm intelligence, was used . Search was also used to improve the results of each particle. Besides, a complex network was used in order to enhance the better exchange of information between particles and to select the next most appropriate position for each particle. In this network of nodes in which a better solution is maintained, the degree of that node is always greater. In the complex network, two criteria, degree and neighborhood degree, are used to find the best solution. The TSPLib standard problems were used to compare the results.  The findings showed that the particle swarm optimization technique with a complex network local search was more cost-effective than the optimization of the particle swarm with local search and standard particle swarm in a way that the percentage of error to the best solution in TSPLib was reduced in the particle swarm optimization algorithms with complex network local search and the particle swarm optimization in the local search compared to the standard particle swarm method, respectively. To solve the ST70 in network and basic algorithms, the average cost of solving the problem is 705 and 797, respectively.

In this study, in roder to investigate the optimal location of a production well, the fast forward method (FMM) was implemented as a proxy to calculate the bottom hole pressure of wells instead of a comprehensive numerical simulation. Two reservoirs, the standard SPE10 reservoir and the Shadegan reservoir were studied. Also, for optimization, the metaheuristic particle swarm algorithm (PSO) has been used which the combination of two methods called PSO-FMM. This method is a fast forecasting method and is highly capable so that it enhances the objective function up to 20% , compared to the conventional well drilling methods. Also, this method has a considerable speed so that it reduced the simulation time 10% of direct stimulation (PSO). This study also proved that the fast forward FMM method is very compatible with numerical simulation-based methods for multi-well location optimization problems (SPE 10 standard model) and a single well optimization (one-sector of Shadegan reservoir).

in this research, two conflicting objective functions used to solve the problem of optimization operation of Sistan's Chah Nimeh reservoirs. The first objective function defined minimizing the total of second power of difference between agricultural demand and release and the second objective function defined maximizing the reliability index. In this study, to compare the studied algorithms, the criteria of the algorithm's run time, the number of solutions in the optimal Pareto front, and distance, dispersion, convergence and generation distance were taken. The results of the study of Meta-Heuristic algorithms indicated that among MOPSO, MOGOA and MOALO algorithms, MOALO and MOGOA algorithms were more efficient than MOPSO algorithm. According to the performance criteria of the algorithm's run time and the dispersion criteria, the MOPSO algorithm showed high efficiency and according to the performance criteria of the distance, convergence and generation distance criteria, the MOGOA showed high efficiency. According to the performance criteria of the number of solutions on the optimal Pareto front MOALO algorithm showed high efficiency. Also, MOALO and MOGOA algorithms effectively covered optimal pareto front. It can be said, the solutions of these algorithms find in themselves optimal pareto front, create a rich set of optimal solutions that not only effectively cover the optimal Pareto front, but also dominate the solutions of the other two algorithms. Therefore, it seems that none of these performance criteria can alone determine the superiority of an algorithm than other algorithms in solving an optimization problem.

It is clear that, having a exact knowledge about the geometry and properties of the materials and the domain that engineering problems are involved are very important specially in structural health monitoring, geotechnical earthquake engineering and other related field in civil engineering; in many cases, it might be useful if a suitable inverse solution is applied in order to detect the characteristics of the problems domain.
The main purpose of this paper is to development of the hybrid finite element- finite difference method for solving inverse elastodynamic and elastostatic scattering problems and combining that with particle swarm optimization algorithm as a quantitative approach fo solving these types of the problems. This hybrid method has been used in order to preparing the forward solution of the problems and by defining a suitable cost function and minimizing that using PSO algorithm, various kind of inverse problems are solved.
In general, an inverse scattering problem can be solved using qualitative or quantitative approaches. In some branches of quantitative techniques, usually, a forward solution is required and then using heuristic algorithm, the goal will be achieved. In this study, a hybrid FE-FD method is used as forward solver (which has the flexibility of finite element method and low computational cost of finite difference method); so, the domain inside and outside of the inclusion will be dicretized using finite difference method and the boundaries near the inclusion will be discretized by finite element method, and in this condition, the solution will be more flexible near the scatterer. In each solution step, first the finite element will be solved and the results will be transferred to the finite difference code and when the result is prepared in it, again, the response of the problem will go to finite element region.
In this research, at first, a geometry and related location will be assumes, randomly and then regarding that, using an OpenSees program code, the boundaries of the inclusion will be discretized and using the MATLAB program the related to finite difference region is discretized, then the results from these two codes will go and back until the response goes converge. Then, the PSO code which is developed in MATLAB will qualify the results and evaluate the cost function (e.g., the cost function is defined by minimizing the the error between the displacement that is from the main model and the predicted model), and if the cost function is large, the PSO algorithm will propose the new location and/or geometry of the inclusion and again, the loop will be repeated until the cost function be near the zero and the solution procedure will be terminated.
In order to evaluate, the efficiency and accuracy of the proposed approach, several problems are solved, where this algorithm could find the location and geometry of the inclusions (e.g., regular and irregular inclusion), the non-homogeneity of the inclusion and also detecting the soil layers by both static and dynamic loading.; the results show a very good accuracy as well as efficiency of the proposed approach for solving inverse problems in bounded and smi-infinite domains.

Creation the necessary conditions to achieve reliable methods to estimate and detect structural damages of the main objectives of the research has been done in recent years. Due to the fact that each structure has its own characteristics, achieving to high reliable methods in the field of civil engineering is faced with a serious challenge [1, 2]. A number of researchers have proposed several methods based on different parameters and indices. But, vibration-based damage detection has been significantly developed in the past several decades but it has yet to be fully realized for large-scale structures and its technology is still developing rapidly [3]. In the vibration-based damage detection, the most widely used condition indices are modal parameters. In this research, new damage detection method based on combination optimization problem is introduced. In this study, modal parameters (Frequency, Frequency Response Function) used to detect and quantify damage with solving the inverse problem of optimization by particle swarm optimization (PSO) algorithm. Different damage scenarios apply on model and modal parameters is extracted and insert to the algorithm. According to results of solution the suitable parameter is introduced. Also, about the algorithm ability to detect damage scenarios discussed.

Reinforced concrete reservoirs (RCR) have been used extensively in municipal and industrial facilities for several decades. The design of these structures requires that attention be given not only to strength requirements, but to serviceability requirements as well. These types of structures will be square, round, and oval reinforced concrete structures which may be above, below, or partially below ground. The main challenge is to design concrete liquid containing structures which will resist the extremes of seasonal temperature changes, a variety of loading conditions, and remain liquid tight for useful life of 50 to 60 years. In this study, optimization is performed by particle swarm algorithm basd on structural design. Firstly by structural analysis all range of shell thickness and areas of rebar find. In the second step by parameter identification system interchange algorithm, source code which developed in particle swarm algorithm by MATLAB software linked to analysis software. Therefore best and optimized thicknesses and total area of bars for each element find. Lastly with circumferential stiffeners structure optimize and show 19% decrease in weight of rebar, 20% decrease in volume of concrete, and 13% minimum cost reduction in construction procedure compared with conventional 10,000 m3 RCR structures.