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.

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.

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.

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.

This study explores application of a bees algorithm (BA) for economic optimal design of plate-fin heat exchangers. Therefore in this study, the optimization is targeting two single-objective functions separately. The first is the minimum heat transfer area which is mainly associated with the capital cost of the heat exchanger and the other is minimum total pressure drop that represents the operating cost for specific heat duty requirement under given space restrictions. Based on applications, heat exchanger length, fin frequency, numbers of fin layers, lance length of fin, fin height and fin thickness of the heat exchanger are considered for optimization. The constraints are handled by penalty function method. Also, the effectiveness and accuracy ofthe proposed algorithm is demonstrated through a case study. Comparing the results with the corresponding results using genetic algorithm (GA)and particle swarm optimization (PSO) algorithm reveals that the bees algorithm can converge to optimum solution with higher accuracy