bees algorithm

Upgrading the thermal efficiency of the cooling system of liquid rocket engines is one of the most significant and intricate problems in designing modern rocket engines in the missile industry. The present study employed the Bees algorithm (BA) to attempt a single-objective optimization of the cooling system of the combustion chamber and nozzle of an LH2/LOX rocket engine considering the overall heat transfer coefficient objective function and four parameters, including the diameter and thickness of the cooling tubes, the radius of the throat, and the mass flow rate of liquid hydrogen (cooling fluid). The optimization was examined by the heat transfer analysis of combustion gases with the chamber walls, the use of the BA optimization algorithm, and the consideration of the sensitivity of the design parameters regarded for the overall heat transfer coefficient objective function. In this respect, these parameters were considered constant in the design ranges, while other parameters were variable. The results show that the overall heat transfer coefficient can increase almost by 17.78% during the optimization process of the cooling system of this rocket engine through the parametric analysis of the four mentioned design parameters.

Today, the competitive business environment has led to increasing cooperation among companies as the members of supply chain networks. In this area, the supply chain logistics network design is an important subject due to its effect on the responsiveness and efficiency. Over the past few years, due to environmental issues, their legal requirements and economic benefits, great attention has been paid to inverse logistics. In this paper, a mixed integer stochastic location-inventory model has been proposed for the integrated design of the network of a multi-period multi-product closed loop supply chain considering multiple capacity levels for facilities. The market demand and correspondingly the return value are considered to be uncertain in order to make the model close to the real-life conditions. Since the capacitated facility location problem considered in this research is an NP-hard type problem, we have used two meta-heuristic algorithms including the genetic algorithm (GA) and the Bees algorithms (BA) for solving this problem. Some numerical problems are designed and solved to assess the performance of the model and solution heuristics. From the viewpoint of solution quality, the BA outperforms the GA (by an average of 11.6%) whilst from the viewpoint of solution time, the GA is five times faster than the BA on average.

In this study, the exergy analysis of a steam power plant generating electricity with Rankine thermodynamic cycle is studied. Exergy efficiency is a suitable criterion for the analysis of a thermodynamic cycle. To optimize the processes and obtaining higher exergy efficiency, some parameters were considered as decision variables, and by changing these parameters, it was tried to improve the exergy efficiencyOutput temperature and pressure of the boiler, and the output pressures of the four stages of turbine extraction, were selected as six decision variables. In other words, the exergy efficiency function was considered as the objective function, and the six aforementioned variables were considered as decision variables. Optimization algorithm using the behavior of bees, which is one of the relatively new intelligent algorithms for optimization problems, performs the optimization by inspiring from the behavior and action of bees to find food. In this study, firstly, the optimization of exergy efficiency function was done for the steam power plant, and then the results were compared with the results obtained using the genetic and particle swarm optimization algorithms. Results showed that by appropriate changes in decision variables and using bees algorithm, exergy efficiency of the thermal power plant increased from 30.1% to 30.6675%. This increase was equivalent to 0.6675% for the cycle, and compared to the use of genetic and swarm particle optimization algorithms it was 0.0038% and 0.0036% higher, respectively.

In this study, application of operational modal analysis and bees algorithm (BA) in order finite element model updating are investigated. BA optimization algorithm applies instinctive behavior of honeybees for finding food sources. An objective function is defined as the sum of the squared errors between the obtained natural frequencies by operational modal analysis and finite element method. Experimental natural frequencies are determined by stochastic subspace identification (SSI) which is considered as one of the strong methods of operational modal analysis. In order to investigate the accuracy of the proposed method, BA and SSI methods are implemented on a three story structure to update parameters of its finite element model. Furthermore, to study of effectiveness of BA algorithm, the results of BA algorithm are compared with particle swarm optimization (PSO) and Nelder-Mead (NM) algorithms. The results show that the BA optimization algorithm, in comparison with the PSO and NM methods, is more accurate and faster to update finite element model.

Automatic modulation recognition of digital signals is an essential for intelligent communication systems. Most automatic classifications of digital signal types deal with recognizing signals formats in presence of additive white Gaussian noise (AWGN) in channels. However, real world communication environments, such as wireless communication channels, suffer from fading effects. There are few methods proposed to perform in fading channels. This paper presents a high efficient method for identification of M-array phase shift keying (MPSK) digital signal type. The proposed method is heuristic hybrid, formed by a multilayer perceptron (MLP) neural network as the classifier and the bees algorithm (BA) as the optimizer. An equalizer is also used to reduce channel effects. A suitable combination of higher order statistics, up to eighth, is considered as prominent characteristics of signals. Simulation results validate the high efficiency of the proposed technique in recognizing the types of digital signals even at low SNRs.

Due to the strategic state of the naval forces in the country and the need to cope with today's advances in submarine technology, synchronous permanent magnet electric motors for use in unmanned underwater units, have been more applicable in recent years. Most of the prominent electrical companies therefore, have triggered several researchers in this field. For example, replacement of electromechanical or electro hydrostatical drivers by hydraulic and pneumatic drivers to reduce weight, cost of maintenance, and heighten power density, and efficiency have attracted many attentions. In this paper design optimization of three-phase permanent magnet synchronous motors applicable in unmanned submarines is discussed. For this purpose, characteristics of a motor used in unmanned submarines are investigated and then equations related to design are evaluated. Then motor is optimized using optimization Bees algorithm (BA) to have less volume and best efficiency and power density.

In this study, the effects of geometrical parameters of 6-DOF Hexa parallel robot on kinematic, and dynamic performance indices are investigated and its structure is optimized using the intelligent multi-objective Bees Algorithm. In this way, after describing the structure and specifying the geometrical parameters of the robot, inverse kinematic relations of the robot are obtained. Jacobian matrix that maps velocity from joint space to Cartesian space is developed. Mass matrix is obtained from calculating the total kinetic energy of the manipulator in terms of the actuated joints vector. Inverse of the homogen jacobian based condition number is considered as a index to evaluate the kinematic dexterity. based on mass matrix as relation between acceleration vector of the end effecter and torque vector of actuated joints, dynamic dexterity index is presented. Using the multi-objective Bees Algorithm and considering dynamic and kinematic performance indices in a pre-determined workspace as the objective functions, structure of Hexa parallel robot is optimized. In this way, the proper geometrical constraints such as limitation of universal and spherical joins, and the constraints to singularity avoidance are considered. Pareto front of the multi objective optimization of the robot is drawn. Diagrams of the kinematic and dynamic performance indices variation in the workspace and the effects of geometrical parameters variation on them are presented.

Multi-phase motors are increasingly used in electrical systems due to their benefits such as decreased current without decreasing voltage, decrease of DC link current and improved reliability. Major applications of such motors are naval systems, electric vehicles, spacecrafts and etc. besides, permanent magnet motors has gained researchers’ attention due to their simple design, high efficiency and power density, low volume and weight and minimum loss and noise.However, electrical motors companies are under pressure and they have to present devices with minimum financial charges. The first and the most important aspect in electrical machines design is to decrease the volume and weight and consequently improve their power density and efficiency. In other word, improvement is done by optimizing machines’ dimensions.This thesis presents design optimization of five-phase permanent magnet synchronous motors to use in underwater vehicles. For this purpose, motor is optimized using optimization Bees algorithm (BA) to have less volume and weight and best operational characteristics. Then, motor is simulated using Ansoft Maxwell which is based on finite element analysis. This software gives required information about performance of the motor. Finite element analysis is an accurate and valid method which is used extensively in analyzing such problems.

In this paper, modeling and optimization of a plate-fin heat exchanger for heat recovery in simultaneous heat and power generation systems has been studied. Design variables for the optimization of heat exchanger are hot and cold flow lengths, number of layers, pitch, lance length, thickness and fin height. Effectiveness and total annual cost of system (sum of the initial investment cost and performance) are considered as two contradict objective functions. Due to the increase of efficiency (favorite), heat and pressure loss (total annual cost) increase (unfavorable), so instead of a specific answer, it is needed to balance solutions to satisfy all of the objective functions, simultaneously. In this study, the multi-objective Bees Algorithm (MOBA) is used to improve both objective functions and the results are shown as a Pareto curve. Finally, in order to prove the effectiveness of the proposed algorithm, the results are compared with a case study from literature review that is better than the references results in some design points.

Because of using the different polarization of electromagnetic wave in Polarimetric imagery، it provides a rich source of information from the several aspects of targets. Recently، Polarimetric images as a powerful and efficient tool have been interested to identify the various objects in the complex geographic areas. In order to extracting information، classification of Polarimetric image has an important effect. Support Vector Machines (SVMs) due to their operation based on geometrical characteristics and robustness in high dimensional space، are considered as a suitable case for classification of Polarimetric images. However، the performance of SVMs classifier is strongly influenced by its parameters. Therefore، the optimum values for SVMs parameters should be determined to achieve SVMs classifier with maximum efficiency. Traditional optimization techniques because of computational complexities in the large search space usually trap in local optimum. Thereby، it is inevitable to apply Meta -heuristic Algorithms which performe exploration and exploitation to obtain global optimum. In this paper، the potential of Genetic، Bees and Particle Swarm Optimization (PSO) Algorithms as powerfull techniques in determining the optimum SVMs parameters are evaluated. Comparing the results، demonstrates the superior performance of PSO Algorithm in terms of classification accuracy and speed of convergence.

Automatic signal type identifier plays an important role for modulation identification of satellite signals. Most of the proposed methods didn’t have good performance in low level of signal to noise ratios (SNRs). Also they cant identify more digital modulations. This study investigates the design of an accurate system for identification of digital modulations. First, it is introduced an efficient system that includes two main modules: the feature extraction module and the classifier module. First module extracts a suitable combination of the higher order moments up to eighth, higher order cumulants up to eighth. In the classifier module, an efficient supervised classifier, i.e. radial basis function neural network is proposed. The results show this system has good performance and recognize a lot of digital modulations. However the performance of system degrades at very low SNRs. Also selection of the parameters of the classifier and feature selection is made by trial and error method. The tradeoff between them is a difficult problem. Then at the second fold we have proposed a hybrid intelligent system which an optimization module, i.e. bees algorithm (BA) is considered in the previous system. This module optimizes the classifier design by searching for the best value of the parameters and the best subset of features that feed the classifier. Simulation results show that the proposed hybrid intelligent system has very high identification accuracy even at very low SNRs. This high efficiency is achieved with little features, which have been selected using BA.

Solar radiation estimation is an important part for various solar energy systems. Many researches have proposed different techniques to estimate the solar radiation that hinder using expencive devices for direction measurement of solar radiation. Nonlinear and complex nature of these methods forced researchers to look for quick and efficient techniques to solve related issues. In this paper، a new method based on the Angstrom equation is introduced which estimates the monthly average daily global solar radiation on a horizontal surface by Bees Algorithm as a heuristic technique implemented in MATLAB software. The empirical coefficients for Angstrom equation are calculated for four different climate regions of Iran using proposed method proved the efficiency and predominance of the new techniue to find a more accurate level of solar radiation.

In this paper, a shape memory alloy actuator was controlled by the type-2 Fuzzy Logic Controller optimized with bees Algorithm. At the beginning equations related to nonlinear behavior of a shape memory alloy actuator were simulated using Matlab/simulink software. Then in order to compare the performance of optimized type-2 fuzzy Logic controller with other controller, the position of the robot arm was controlled by two tuned with Bees Algorithm controllers, PID and Type-1 fuzzy logic controller. The simulation results show that optimized type-2 Fuzzy Logic Controller has better performance than the other controllers in determining the accurate position of robot arm.