An Improved Flow Direction Algorithm for Engineering Optimization Problems

Abstract

Flow Direction Algorithm (FDA) has better searching performance than some traditional optimization algorithms. To give the basic Flow Direction Algorithm more effective searching ability and avoid multiple local minima under the searching space, and enable it to obtain better search results, an improved FDA based on the Lévy flight strategy and the self-renewable method (LSRFDA) was proposed in this paper. The Lévy flight strategy and the self-renewable approach were added to the basic Flow Direction Algorithm. Random parameters generated by the Lévy flight strategy can increase the algorithm’s diversity of feasible solutions in a short calculation time and greatly enhance the operational efficiency of the algorithm. The self-renewable method lets the algorithm quickly obtain a better possible solution and jump to the local solution space. Then, this paper tested different mathematical testing functions, including low-dimensional and high-dimensional functions, and the test results were compared with those of different algorithms. This paper includes iterative figures, box plots, and search paths to show the different performances of the LSRFDA. Finally, this paper calculated different engineering optimization problems. The test results show that the proposed algorithm in this paper has better searching ability and quicker searching speed than the basic Flow Direction Algorithm.

Disturbance Observer-Based PID Control System Using DNA Strand Displacement and Its Application in Exponential Gate

“…Karami H [34] has shown through tests that the flow direction method has a greater global search capability than the widely used optimization algorithms, and the parameter setup is easier. The flow direction technique is now being used to engineering material issues [35] and tackle machine learning [36]. The flow direction method provides several advantages in the direction of establishing parameters, as seen by its broad application potential.…”

Unsteady Heat Transfer of Pulsating Gas Flows in a Gas-Dynamic System When Filling and Emptying a Cylinder (as Applied to Reciprocating Machines)

“…The presented studies emphasize the importance of obtaining detailed information on local and average heat-transfer coefficients in intake and exhaust systems in order to find ways to enhance the efficiency and environmental friendliness of RICE. Moreover, reliable experimental data on the unsteady heat transfer of pulsating flows in gas dynamic systems are necessary for the development of new mathematical models and the verification of existing ones [25][26][27][28]. In addition, artificial intelligence algorithms are used to optimize thermophysical processes in the gas exchange system of reciprocating machines [29].…”