particle swarm algorithm

The present study presents a three-tier model with incomplete and uncertain information of supply chain needs, benefits and services. Objectives of the issue include determining the best decision to determine the optimal order amount and shortage for the manufacturer and the selling price of each player according to the shortage, discount and maintenance costs, purchase and marketing to achieve maximum revenue, minimum costs and The sum is the maximum possible profit for all the players participating in the chain. To solve the model, Gamz software and meta-heuristic algorithms have been used and finally, Given the complexity of the complexity of closed-loop supply chain problems, the problem ahead cannot be solved in a reasonable time for real-world dimensions. Therefore, to solve it, the meta-heuristic approach in the form of genetic algorithms and optimization of particle aggregation and the combination of these two algorithms have been used. The results show that the combined algorithm of genetics and particle swarming has a better situation compared to genetic and particle swarming algorithms.

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.

Environmental issues due to the emission of pollutants produced by fossil fuel power plants have recently become an important issue. In this study, the coefficients of particle swarm optimization (PSO) algorithm to solve the problem of economic dispatching to reduce the emission of environmental pollutants were obtained. According to Clerk method, personal learning coefficient was equal to 1.4962, global learning coefficient was equal to 1.4962 and inertia coefficient was equal to 0.73. Also, the penalty coefficient according to the Co evolution particle swarm (CPSO) optimization algorithm was 15.8. As a result, optimization of coefficients by Taguchi method, it showed that the optimal value of personal learning coefficient is equal to 1.5, global learning coefficient is equal to 1.5, inertia coefficient is equal to 0.70 and penalty coefficient is equal to 15, in this case the amount emission of environmental pollutants were reduced by 6.5% compared to the coefficients determined by Clerk and 1.2% compared to the coefficients determined by the Co evolution particle swarm (CPSO) optimization algorithm.

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.

Linear induction motors have single and double-sided structures which are used in special applications, according to their advantages and disadvantages. Due to advantages such as lack of normal forces in double-sided type, these motors are more controllable and popular than the single-sided type, especially for transportation applications.  In literature, single-sided linear induction motors have gained more attentions; so, there are few work on double-sided type. In this paper, first, equations are proposed for calculation of the equivalent circuit parameters of these motors, considering end-effect. Then, a method is proposed to design the motor, employing the presented equivalent circuit. Using the proposed method, a double-sided linear induction motor is designed. Then, to minimize the primary weight and maximize the efficiency and the power factor, the designed motor is optimized by using Particle Swarm Optimization algorithm. One of the important factors in design that affects the construction cost is the weight of the machine. The optimization results show that the initial weight of the motor decreases from 44 kg in the non-optimal case to 18.31 kg in the optimal case. For validation, the optimization results are compared with finite element analysis results and a laboratory prototype test results. The comparison of the experimental and the finite element results with optimization results confirms the validity of the proposed method.

Marine simulators are effective tools for making a ship feel like driving by creating a similar environment using motion commands. The main problem with simulators is the limited workspace which does not allow them to generate accurate real-time floating movements, so they require a motion synchronization algorithm. Recently, the use of predictive control has become popular in marine simulators. Values of control horizon and future forecast affect the computational load. However, because the designer manually selects these horizons, they are lower than the optimal level. In this paper, a new method based on particle swarm algorithm was used to achieve the best control and forecast horizons by minimizing some periods such as sensory error, displacement and computational load. The proposed method eliminates the disadvantages of the MPC-MCA method such as time-consuming empirical estimation through trial and error for initial control and forecast horizons, while minimizing optimal cost performance and computational load. The simulation results showed the efficiency of the proposed method based on the improvement of performance output and computational load.

The forming of tailor machined blanks due to the different thicknesses is more complex than the monolithic sheets. The presence of two sections with different thicknesses leads to different formability behaviors and consequently their different spring-backs. Therefore, it is important to examine the spring-back in the thin and thick sections of tailor machined blanks in order to minimize the spring-back value. In this paper, an adaptive neural-fuzzy inference system is used to model the effect of important parameters to predict the percent of spring-back corresponding to the thin and thick sections on the creep age formed tailor machined blanks. In addition, the obtained model is used to optimize the creep forming process using particle swarm optimization algorithm. In order to model the process behavior, some experiments have been performed on the creep forming of tailor machined blanks. The accuracy of the obtained model is investigated using different figures as well as by the specific criteria , , , and .

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.

In this paper, the effect of using various intelligent algorithms to optimize the adaptive neuro-fuzzy disturbance observer has been investigated .First, a model reference adaptive control is designed for the spacecraft simulator. Then, in order to reduce the disturbance effect, an adaptive neuro-fuzzy disturbance observer is used. In this paper, the fuzzy system is optimized using Intelligent Genetic Algorithm, Particle Swarm Optimization, Imperialist Competitive Algorithm, Bee Colony, Ant Colony Optimization, and especially Policy Gradient Particle Swarm Algorithm, which speeds up and optimizes the response. The Policy Gradient Particle Swarm algorithm is a combination of gradient policy reinforcement learning and particle swarming ideas and is a hybrid optimization method to control a nonlinear complex system with many applications in the real world. In this method, influenced by reinforcement idea, the policy gradient for a non-fossilized system is calculated, and in the optimization of particle swarm relations, optimization is performed in addition to the factors included in the congestion methods in the direction of the policy gradient. It is intended to optimize the fuzzy neuro system parameters and input and output data in the cost function. Finally, the neuro-fuzzy systems optimized by these algorithms are compared and it is shown that the gradient particle swarm algorithm performs better than the particle swarm algorithm.

In this paper, a double-side bull’s eye plasmonic color filter used in complementary metal oxide semiconductor (CMOS) based standard imaging sensors is presented. The thin-film filter is silver-made and optimized by using a particle swarm algorithm in order to modify the corrugation period, duty cycle, film thickness, grating height and aperture diameter for infrared imaging. The filter is designed for central wavelength 835 nm, having the maximum transmittance of 38.6 % and the full width at half maximum (FWHM) of 110 nm. Inexpensive implementation, proximity to the sensitive region, reduced crosstalk and independence from  temperature-sensitive polymers, are some of the advantages of this filter, making it suitable for tough temperature conditions. Moreover, using functional analysis of this plasmonic filter and comparing it to the optical filters with similar central wavelength has led us to the investigation of surface plasmon features under a local electric field intensity distribution.

Regarding the complexity of WDNs, the need for computerized modelling of WDNs is felt more than ever for monitoring their performance at the operational stage. One of the most important issues in modelling is to adjust the results of modelling with the real status of the system. So it is necessary to calibrate the model by observed data. Finding a suitable method for adjusting the model's coefficients is one of the main challenges in computerized modelling. In this paper, a modified particle swarm method is presented to adjustment of water distribution network coefficients by modifying the velocity equation of the particle swarm and Combining it with the mutation operator. Thus by defining the coefficients of the models, four models such as standard particle swarm optimization model (SPSO), modified standard particle swarm optimization model (MSPSO), standard particle swarm optimization model with a mutation (SPSOM) and modified standard particle swarm optimization model with a mutation (MSPSOM) are constructed. The Rastrigin test function is used for verification of the modified particle swarm equation and the two-loop network is used for verification of models and also the four-loop network and real water distribution network are used for detailed analysis. The optimization is done in MATLAB by combining the particle swarm optimization algorithm and the EPANET software. Comparison of the results of the standard particle swarm model and the modified standard particle swarm model for the Rastrigin test function showed that modifying the particle swarm velocity equation increased the model's ability to determine the actual answers and reduced the costs. The MSPSOM model finds the optimal answer for two-loop and four-loop networks with a probability of 96.7 and 95 percent respectively. So it is the best model among all models in this criteria. Also, the MSPSO model finds the optimal answer for two-loop and four-loop networks in lowest time compared to other models. So it is the best model among all models in this criteria. Comparing the results of the models in the Ahar water distribution network showed that the modified standard particle swarm with the mutation model have the lowest minimum and average values of the modeling data error. So it has the best performance among the particle swarm models. In general, the correction of the particle swarm velocity equation in the form of the standard particle swarm model, and the correction of the particle swarm velocity and its integration with the mutation operator in the form of a modified standard particle swarm with a mutation has a higher ability to adjust the water distribution network coefficients.

Correct selection of projects The first step is project-based organizations in the targeted management of project portfolios. This is a complex process selection that includes many factors and considerations. Market conditions, global rapid changes in various dimensions and other related issues in the real environment have increased the uncertainty and ignorance of these issues. It is therefore necessary to provide models for showing the real status of the organization and its goals and preferences. In this paper, the goal is to provide a fuzzy fuzzy multi-objective model for the portfolio of rail transport projects considering the uncertainties in variables; budget, time needed to complete the project, environmental pollution, risk, and quality. In this model, minimizing environmental pollution, maximizing quality, minimizing the risk and cost of projects under inflation is considered in the objectives of the problem. Due to the fact that the model was presented, a particle swarm algorithm was used to solve the problem, and finally, the results were compared with the genetic algorithm in order to measure the efficiency.