Particle Swarm Optimization Algorithm

Dimensional Design of four-bar mechanisms, as the simplest and most widely used linkage, is always one of the main issues raised in the field of mechanism design. In the past, geometric and graphical analysis methods were used to design these mechanisms, but with the advancement of technology, numerical optimization methods, and meta-heuristic algorithms, it is possible to solve problems more precisely and consider a higher number of precision points. The path generation synthesis problem of this mechanism has been solved using different algorithms and in different design modes, including with and without prescribed timing, which the synthesis problem with prescribed timing is more difficult and has a higher path generation error. In this research, to solve the synthesis problem of four-bar mechanism path generation with prescribed timing, a novel objective function is utilized, which includes two terms, path error and angle error. This new objective function leads to a lower error for path generation with prescribed timing. Four different designs are considered and their results, where extracted by the PSO algorithm, are compared. The results of solving this problem for three numerical examples show that the design in the fourth state, which uses the new objective function, has fewer path generation errors.

In this paper, an intelligent integral Back-step controller is used to control the position of the quadrotor. Using a new strong particle swarm optimization algorithm, the optimal system response is Achieved by changing constant design values in the controller. Then, the optimal path for the quadrotor is designed using the algorithmic method of particle swarm and considering the predetermined obstacles in the three-dimensional environment, which ensures safety flight. Also, the shortest path between the origin and destination as the optimal path is determined. Finally, the path generated by the algorithm as the optimal path of planning. Thus, in addition to optimizing the control parameters in the controller, the quadrotor path is also optimized using the algorithm to minimize energy consumption from source to destination. Finally, by simulating the system in MATLAB environment, the system response to the desired input in a limited time was plotted and investigated.

The present study aimed to model and optimize the performance and emission characteristics of a diesel engine fueled with water-diesel emulsions containing metal-organic framework nanoparticles using a combination of adaptive neural-fuzzy inference system with optimal algorithm particle swarm generation (PSO-ANFIS). The multi-purpose particle swarm algorithm (MOPSO) was used to optimize engine performance and fuel composition. Water inclusion rate, engine load, and metal-organic framework nanoparticle concentration were considered as input parameters of the model. Brake specific fuel consumption, brake thermal efficiency, CO, CO2, UHC, NOx, and smoke were considered as model outputs. Sixteen experimental data were used in modeling and optimization processes. The results showed that the developed PSO-ANFIS models could accurately predict the objective functions. There was a good agreement between all the target data and the output of the developed models. According to the optimization results, water-diesel emulsion fuel containing 26.27 ppm metal-organic framework nanoparticles and 4.14 wt% water under engine load 60.15% of the full-load operating level was found to be optimal conditions.
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In this paper, a robust optimization method is developed to solve the Satellite Launch Vehicle (SLV) trajectory design problem in the presence of uncertainties using a powerful Particle Swarm Optimization (PSO) algorithm. Given the uncertainties such as uncertainties in the actual values ​​of aerodynamic coefficients, engine thrust, and mass in the ascent phase of a SLV, it is important to achieve an optimal trajectory that is robust to these uncertainties; because it improves the flight performance, reduces the workload of the guidance-control system, and increases the reliability of the satellite. For this purpose, first the optimization problem is considered by using the criterion of minimizing the flight time of the SLV as a cost function, and three-dimensional equations of motion as constraints governing the problem. Then, by adding the mean parameters and the standard deviation of uncertainties in the cost function, a robust optimizer model is developed and the algorithm is used to numerically optimize the model. Monte Carlo's perspective has also been used to analyze the results of uncertainties and their continuous feedback to the optimization model. Finally, the optimal trajectory is obtained that is robust to the uncertainties. The resulting simulation results show the accuracy of this claim.