particle swarm algorithm

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.

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.

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.

Optimum design of electrical motors may be considered as a complex optimization problem due to the wide variety of mechanical, electrical, electromagnetics parameters, although recently it can be accomplished utilizing heuristic optimization algorithms. In this paper optimum design of an out-runner PM BLDC motor for flywheel energy storage applications is performed. The optimization utilized particle swarm optimization (PSO) algorithm to achieve maximum torque density. Accordingly, the motor design equations are employed in the fitness function of the algorithm. Based on the random initial values and respecting the designs constraints, the optimum design is achieved. Effectiveness of the algorithm results are verified by finite element analysis (FEA) and motor operating parameters are obtained and analyzed. Finally, the prototype of the motor is fabricated and experimental results are demonstrated to show the applicability of the model and analysis.

Energy demand management has very importance in economic security and planning. To identify the energy demand affecting factors and energy demand prediction can help Policy makers and activists in the energy market to improve market performance and better economic decisions and high fuel security. Recently, new techniques have been developed to economic variables prediction and modeling. Among these techniques Genetic algorithm and Particle Swarm Optimization are the best known and most widely used in literature including economy. Therefore, in this study the genetic algorithm and particle Swarm Optimization is used for energy demand estimation and prediction in the form of linear and exponential and then their performance in each of the models evaluated. The results indicate that accuracy and efficiency of the particle swarm optimization in both of exponential and linear forms is better than genetic algorithm. In addition, among the different forms the exponential form estimated with the particle swarm optimization is the best way to predict the future energy demand.

In this paper, a new approach is presented for reactive power management considering security and economic objectives in order to meet optimum and secure operation of power systems. Reactive power management issue is formulated as a nonlinear multi-objective optimization problem with continuous and discrete variables, in which the active losses reduction is selected as economic goal and the voltage profile improvement and its stability are also selected as security goals. The above optimization problem is solved by using the objectives fuzzification approach and a new developed version of particle swarm optimization algorithm that has highly ability to provide the best solution in less time. In this paper, the optimal production of generator’s reactive power and the minimum eigenvalue of Jacobin matrix are two effective indices that considered to assessment the system’s voltage stability under normal operation. Moreover, a new index is proposed for optimal production of generator’s reactive power that used to voltage stability assessment. The proposed model has been implemented on IEEE 30 Bus power system. The simulation results confirm acceptable performance of the proposed algorithm in management of reactive power with respect to both security and economic conditions.